diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
index 024ac9c21..4e75d7bfe 100644
--- a/CONTRIBUTING.md
+++ b/CONTRIBUTING.md
@@ -5,7 +5,7 @@ Thanks for your interest in DL4J. Our goal is to bring fast, open-source deep le
 
 ## Getting Started
 
-Deeplearning4j's [open issues are here](https://github.com/deeplearning4j/deeplearning4j/issues). In time, we'll tag issues that would make a good first pull request for new contributors. An easy way to get started helping the project is to *file an issue*. You can do that on the Deeplearning4j issues page by clicking on the green button at the right. Issues can include bugs to fix, features to add, or documentation that looks outdated. 
+Deeplearning4j's [open issues are here](https://github.com/eclipse/deeplearning4j/issues). In time, we'll tag issues that would make a good first pull request for new contributors. An easy way to get started helping the project is to *file an issue*. You can do that on the Deeplearning4j issues page by clicking on the green button at the right. Issues can include bugs to fix, features to add, or documentation that looks outdated. 
 
 Note that you will need to [build dl4j from source](https://deeplearning4j.org/docs/latest/deeplearning4j-build-from-source)
 
diff --git a/README.md b/README.md
index da04994d4..2b69f7981 100644
--- a/README.md
+++ b/README.md
@@ -2,17 +2,17 @@
 
 Welcome to the new monorepo of Deeplearning4j that contains the source code for all the following projects, in addition to the original repository of Deeplearning4j moved to [deeplearning4j](deeplearning4j):
 
- * https://github.com/deeplearning4j/libnd4j
- * https://github.com/deeplearning4j/nd4j
- * https://github.com/deeplearning4j/datavec
- * https://github.com/deeplearning4j/arbiter
- * https://github.com/deeplearning4j/nd4s
- * https://github.com/deeplearning4j/gym-java-client
- * https://github.com/deeplearning4j/rl4j
- * https://github.com/deeplearning4j/scalnet
- * https://github.com/deeplearning4j/pydl4j
- * https://github.com/deeplearning4j/jumpy
- * https://github.com/deeplearning4j/pydatavec
+ * https://github.com/eclipse/deeplearning4j/tree/master/libnd4j
+ * https://github.com/eclipse/deeplearning4j/tree/master/nd4j
+ * https://github.com/eclipse/deeplearning4j/tree/master/datavec
+ * https://github.com/eclipse/deeplearning4j/tree/master/arbiter
+ * https://github.com/eclipse/deeplearning4j/tree/master/nd4s
+ * https://github.com/eclipse/deeplearning4j/tree/master/gym-java-client
+ * https://github.com/eclipse/deeplearning4j/tree/master/rl4j
+ * https://github.com/eclipse/deeplearning4j/tree/master/scalnet
+ * https://github.com/eclipse/deeplearning4j/tree/master/pydl4j
+ * https://github.com/eclipse/deeplearning4j/tree/master/jumpy
+ * https://github.com/eclipse/deeplearning4j/tree/master/pydatavec
  
 
 To build everything, we can use commands like
@@ -30,6 +30,6 @@ mvn -B -V -U clean install -pl '!jumpy,!pydatavec,!pydl4j' -Dlibnd4j.platform=li
 An example of GPU "CC" or compute capability is 61 for Titan X Pascal.
 
 # Want some examples?
-We have separate repository with various examples available: https://github.com/deeplearning4j/dl4j-examples
+We have separate repository with various examples available: https://github.com/eclipse/deeplearning4j-examples
 
-In the examples repo, you'll also find a tutorial series in Zeppelin: https://github.com/deeplearning4j/dl4j-examples/tree/master/tutorials
+In the examples repo, you'll also find a tutorial series in Zeppelin: https://github.com/eclipse/deeplearning4j-examples/tree/master/tutorials
diff --git a/arbiter/.github/CONTRIBUTING.md b/arbiter/.github/CONTRIBUTING.md
deleted file mode 100644
index 17598f387..000000000
--- a/arbiter/.github/CONTRIBUTING.md
+++ /dev/null
@@ -1,15 +0,0 @@
-## Contribute
-
-1. Check for open issues, or open a new issue to start a discussion around a feature idea or a bug.
-2. If you feel uncomfortable or uncertain about an issue or your changes, feel free to contact us on Gitter using the link above.
-3. Fork [the repository](https://github.com/deeplearning4j/Arbiter.git) on GitHub to start making your changes to the **master** branch (or branch off of it).
-4. Write a test, which shows that the bug was fixed or that the feature works as expected.
-5. Note the repository follows
-   the [Google Java style](https://google.github.io/styleguide/javaguide.html)
-   with two modifications: 120-char column wrap and 4-spaces indentation. You
-   can format your code to this format by typing `mvn formatter:format` in the
-   subproject you work on, by using the `contrib/formatter.xml` at the root of
-   the repository to configure the Eclipse formatter, or by [using the INtellij
-   plugin](https://github.com/HPI-Information-Systems/Metanome/wiki/Installing-the-google-styleguide-settings-in-intellij-and-eclipse).
-
-6. Send a pull request, and bug us on Gitter until it gets merged and published.
diff --git a/arbiter/.github/ISSUE_TEMPLATE.md b/arbiter/.github/ISSUE_TEMPLATE.md
deleted file mode 100644
index 9dd585d22..000000000
--- a/arbiter/.github/ISSUE_TEMPLATE.md
+++ /dev/null
@@ -1,19 +0,0 @@
-#### Issue Description
-
-Please describe your issue, along with:
-- expected behavior
-- encountered behavior
-
-#### Version Information
-
-Please indicate relevant versions, including, if relevant:
-
-* Deeplearning4j version
-* platform information (OS, etc)
-* CUDA version, if used
-* NVIDIA driver version, if in use
-
-#### Contributing
-
-If you'd like to help us fix the issue by contributing some code, but would
-like guidance or help in doing so, please mention it!
diff --git a/arbiter/.github/PULL_REQUEST_TEMPLATE.md b/arbiter/.github/PULL_REQUEST_TEMPLATE.md
deleted file mode 100644
index 1f92bacd6..000000000
--- a/arbiter/.github/PULL_REQUEST_TEMPLATE.md
+++ /dev/null
@@ -1,10 +0,0 @@
-## What changes were proposed in this pull request?
-
-(Please fill in changes proposed in this fix)
-
-## How was this patch tested?
-
-(Please explain how this patch was tested. E.g. unit tests, integration tests, manual tests)
-
-Please review
-https://github.com/deeplearning4j/deeplearning4j/blob/master/CONTRIBUTING.md before opening a pull request.
diff --git a/datavec/.github/CONTRIBUTING.md b/datavec/.github/CONTRIBUTING.md
deleted file mode 100644
index a4ea5dc58..000000000
--- a/datavec/.github/CONTRIBUTING.md
+++ /dev/null
@@ -1,15 +0,0 @@
-## Contribute
-
-1. Check for open issues, or open a new issue to start a discussion around a feature idea or a bug.
-2. If you feel uncomfortable or uncertain about an issue or your changes, feel free to contact us on Gitter using the link above.
-3. Fork [the repository](https://github.com/deeplearning4j/DataVec.git) on GitHub to start making your changes to the **master** branch (or branch off of it).
-4. Write a test, which shows that the bug was fixed or that the feature works as expected.
-5. Note the repository follows
-   the [Google Java style](https://google.github.io/styleguide/javaguide.html)
-   with two modifications: 120-char column wrap and 4-spaces indentation. You
-   can format your code to this format by typing `mvn formatter:format` in the
-   subproject you work on, by using the `contrib/formatter.xml` at the root of
-   the repository to configure the Eclipse formatter, or by [using the INtellij
-   plugin](https://github.com/HPI-Information-Systems/Metanome/wiki/Installing-the-google-styleguide-settings-in-intellij-and-eclipse).
-
-6. Send a pull request, and bug us on Gitter until it gets merged and published.
diff --git a/datavec/.github/ISSUE_TEMPLATE.md b/datavec/.github/ISSUE_TEMPLATE.md
deleted file mode 100644
index 9dd585d22..000000000
--- a/datavec/.github/ISSUE_TEMPLATE.md
+++ /dev/null
@@ -1,19 +0,0 @@
-#### Issue Description
-
-Please describe your issue, along with:
-- expected behavior
-- encountered behavior
-
-#### Version Information
-
-Please indicate relevant versions, including, if relevant:
-
-* Deeplearning4j version
-* platform information (OS, etc)
-* CUDA version, if used
-* NVIDIA driver version, if in use
-
-#### Contributing
-
-If you'd like to help us fix the issue by contributing some code, but would
-like guidance or help in doing so, please mention it!
diff --git a/datavec/.github/PULL_REQUEST_TEMPLATE.md b/datavec/.github/PULL_REQUEST_TEMPLATE.md
deleted file mode 100644
index 1f92bacd6..000000000
--- a/datavec/.github/PULL_REQUEST_TEMPLATE.md
+++ /dev/null
@@ -1,10 +0,0 @@
-## What changes were proposed in this pull request?
-
-(Please fill in changes proposed in this fix)
-
-## How was this patch tested?
-
-(Please explain how this patch was tested. E.g. unit tests, integration tests, manual tests)
-
-Please review
-https://github.com/deeplearning4j/deeplearning4j/blob/master/CONTRIBUTING.md before opening a pull request.
diff --git a/datavec/README.md b/datavec/README.md
index 7188db359..e631d7510 100644
--- a/datavec/README.md
+++ b/datavec/README.md
@@ -32,7 +32,7 @@ static data and for sequences (time series). Such operations can be executed on
 
 Apart from obviously providing readers for classic data formats, DataVec also provides an interface. So if you wanted to
 ingest specific custom data, you wouldn't have to build the whole pipeline. You would just have to write the very first step. For example, if you describe through the API how your data fits into a common format that complies with the interface, DataVec
-would return a list of Writables for each record. You'll find more detail on the API in the corresponding [module](https://github.com/deeplearning4j/DataVec/tree/master/datavec-api).
+would return a list of Writables for each record. You'll find more detail on the API in the corresponding [module](https://github.com/eclipse/deeplearning4j/tree/master/datavec/datavec-api).
 
 Another thing you can do with DataVec is data cleaning. Instead of having clean, ready-to-go data, let's say you start with data in different forms or from different sources. You might need to do sampling, filtering, or several incredibly messy ETL tasks needed to prepare data in the real world. DataVec offers filters and transformations that help with curating, preparing and massaging your data. It leverages Apache Spark to do this at scale.
 
@@ -51,7 +51,7 @@ to be locked into a single tool, and using [Apache Flink](https://flink.apache.o
 ## Examples
 
 Examples for using DataVec are available
-here: [https://github.com/deeplearning4j/dl4j-examples](https://github.com/deeplearning4j/dl4j-examples)
+here: [https://github.com/eclipse/deeplearning4j-examples](https://github.com/eclipse/deeplearning4j-examples)
 
 
 ---
@@ -91,7 +91,7 @@ It's useful to know which maintainers to contact to get information on a particu
 1. Check for open issues, or open a new issue to start a discussion around a feature idea or a bug.
 2. If you feel uncomfortable or uncertain about an issue or your changes, feel free to contact us on Gitter using the
    link above.
-3. Fork [the repository](https://github.com/deeplearning4j/datavec.git) on GitHub to start making your changes.
+3. Fork [the repository](https://github.com/eclipse/deeplearning4j.git) on GitHub to start making your changes.
 4. Write a test, which shows that the bug was fixed or that the feature works as expected.
 5. Note the repository follows the [Google Java style](https://google.github.io/styleguide/javaguide.html) with two
    modifications: 120-char column wrap and 4-spaces indentation. You can format your code to this format by typing `mvn
diff --git a/deeplearning4j/.github/CONTRIBUTING.md b/deeplearning4j/.github/CONTRIBUTING.md
deleted file mode 120000
index 44fcc6343..000000000
--- a/deeplearning4j/.github/CONTRIBUTING.md
+++ /dev/null
@@ -1 +0,0 @@
-../CONTRIBUTING.md
\ No newline at end of file
diff --git a/deeplearning4j/.github/ISSUE_TEMPLATE.md b/deeplearning4j/.github/ISSUE_TEMPLATE.md
deleted file mode 100644
index 95d46fee4..000000000
--- a/deeplearning4j/.github/ISSUE_TEMPLATE.md
+++ /dev/null
@@ -1,19 +0,0 @@
-#### Issue Description
-
-Please describe our issue, along with:
-- expected behavior
-- encountered behavior
-
-#### Version Information
-
-Please indicate relevant versions, including, if relevant:
-
-* Deeplearning4j version
-* platform information (OS, etc)
-* CUDA version, if used
-* NVIDIA driver version, if in use
-
-#### Contributing
-
-If you'd like to help us fix the issue by contributing some code, but would
-like guidance or help in doing so, please mention it!
diff --git a/deeplearning4j/.github/PULL_REQUEST_TEMPLATE.md b/deeplearning4j/.github/PULL_REQUEST_TEMPLATE.md
deleted file mode 100644
index 44316b06f..000000000
--- a/deeplearning4j/.github/PULL_REQUEST_TEMPLATE.md
+++ /dev/null
@@ -1,16 +0,0 @@
-## What changes were proposed in this pull request?
-
-(Please fill in changes proposed in this fix)
-
-## How was this patch tested?
-
-(Please explain how this patch was tested. E.g. unit tests, integration tests, manual tests)
-
-## Quick checklist
-
-The following checklist helps ensure your PR is complete:
-
-- [ ] Reviewed the [Contributing Guidelines](https://github.com/deeplearning4j/deeplearning4j/blob/master/CONTRIBUTING.md) and followed the steps within.
-- [ ] Created tests for any significant new code additions.
-- [ ] Relevant tests for your changes are passing.
-- [ ] Ran mvn formatter:format (see [formatter instructions](http://code.revelc.net/formatter-maven-plugin/examples.html#Setting_Source_Files) for targeting your specific files).
diff --git a/deeplearning4j/.github/lock.yml b/deeplearning4j/.github/lock.yml
deleted file mode 100644
index cdf578845..000000000
--- a/deeplearning4j/.github/lock.yml
+++ /dev/null
@@ -1,28 +0,0 @@
-# Configuration for lock-threads - https://github.com/dessant/lock-threads
-
-# Number of days of inactivity before a closed issue or pull request is locked
-daysUntilLock: 45
-
-# Issues and pull requests with these labels will not be locked. Set to `[]` to disable
-exemptLabels: []
-
-# Label to add before locking, such as `outdated`. Set to `false` to disable
-lockLabel: Outdated
-
-# Comment to post before locking. Set to `false` to disable
-lockComment: >
-  This thread has been automatically locked since there has not been
-  any recent activity after it was closed. Please open a new issue for
-  related bugs.
-
-# Limit to only `issues` or `pulls`
-# only: issues
-
-# Optionally, specify configuration settings just for `issues` or `pulls`
-# issues:
-#   exemptLabels:
-#     - help-wanted
-#   lockLabel: outdated
-
-# pulls:
-#   daysUntilLock: 30
\ No newline at end of file
diff --git a/deeplearning4j/GITTER_GUIDELINES.md b/deeplearning4j/GITTER_GUIDELINES.md
index a8a43964a..fdb5609a8 100644
--- a/deeplearning4j/GITTER_GUIDELINES.md
+++ b/deeplearning4j/GITTER_GUIDELINES.md
@@ -7,14 +7,14 @@ Welcome, stranger. You probably just joined a Gitter channel for Deeplearning4j.
 3. We're doing our best to improve the documentation, but it's not perfect. We welcome ideas about how to improve it! Writing good docs is our responsibility; reading them is yours. Please consult the docs before you post in the channel. A little effort from you will earn a lot of respect from us. (DL4J is backed by a startup, Skymind, and we are serving customers as well as the open-source community, which feels like a lot sometimes.)
  * User guide: [https://deeplearning4j.org/docs/latest/](https://deeplearning4j.org/docs/latest/)
  * API: [https://deeplearning4j.org/api/latest/](https://deeplearning4j.org/api/latest/)
-4. We welcome new contributors! Once you get familiar with the libs, if you see how our code can be improved, please file an issue or consider sending us a pull request with the new feature. [https://github.com/deeplearning4j/deeplearning4j/issues](https://github.com/deeplearning4j/deeplearning4j/issues)
+4. We welcome new contributors! Once you get familiar with the libs, if you see how our code can be improved, please file an issue or consider sending us a pull request with the new feature. [https://github.com/eclipse/deeplearning4j/issues](https://github.com/eclipse/deeplearning4j/issues)
 
 Many of the questions asked on the Deeplearning4j Gitter support channel have been answered already in our documentation or can be easily Googled. To respect the Skymind team's time, Deeplearning4j users are kindly asked to remember a few things:
 
 1. Please use Google before you ask a question. The Deeplearning4j Gitter channel should not be used as a human-enhanced search engine. (We promise that you'll end up with a better open-source framework if you only ask us the hard questions...) Please remember that DL4J Gitter channels are devoted to DL4J and other Skymind libraries specifically. We can't help with other frameworks or tools, which have their own docs and communities.
 2. If you don't receive an immediate response, please post again and flag your question with the Gitter ID of one of the people in the channel answering questions. If you do receive a response and link, please spend some time reading and trying to understand the response and additional resources before you ask the same question again. 
 3. To answer questions, we need to know about your OS, Java version, Maven version and we may even need to see your code and stacktrace. When we ask for code, please send us a gist using https://gist.github.com/. If you can't give us code, in many cases we can't help you. (It's also a great sign of commitment on your part!)
-4. We're not perfect, and neither is our documentation. If you find ways for us to improve, please open an issue [here](https://github.com/deeplearning4j/deeplearning4j/issues) or email us at help@skymind.io and let us know what we need to fix. 
+4. We're not perfect, and neither is our documentation. If you find ways for us to improve, please open an issue [here](https://github.com/eclipse/deeplearning4j/issues) or email us at help@skymind.io and let us know what we need to fix. 
 5. Neural nets aren't magic. They are inherently hard to tune. We get many questions from beginners on how to tune neural nets. If you must post a question related to tuning, please post to: https://gitter.im/deeplearning4j/deeplearning4j/tuninghelp
 
 ### Guidelines for Help with Neural Network Tuning:
@@ -26,7 +26,7 @@ Providing help for tuning neural networks can be quite time consuming for the de
 1. Before posting a question, please first read both [https://deeplearning4j.org/docs/latest/deeplearning4j-troubleshooting-training](https://deeplearning4j.org/docs/latest/deeplearning4j-troubleshooting-training) and [https://deeplearning4j.org/docs/latest/deeplearning4j-nn-visualization](https://deeplearning4j.org/docs/latest/deeplearning4j-nn-visualization). You may also find an answer to your question on one of the other pages: [https://deeplearning4j.org/docs/latest/](https://deeplearning4j.org/docs/latest/)
 2. We generally won't answer questions that can be easily answered by searching Google or reading something like Andrej Karpathy's Stanford course on convolutional networks [http://cs231n.github.io/](http://cs231n.github.io/) or Ian Goodfellow and Yoshua Bengio's deep learning book [http://www.deeplearningbook.org/](http://www.deeplearningbook.org/)
 3. For some questions/issues, it may not be possible to provide a short/simple answer to your question. In these cases, we might decide to answer your question by improving our documentation, instead of answering your question directly in Gitter. Please understand that improving our documentation helps *everyone* and is a better use of the team's time than answering one-off questions.
-4. You should generally feel free to open issues ([https://github.com/deeplearning4j/deeplearning4j/issues](https://github.com/deeplearning4j/deeplearning4j/issues)) if you feel our documentation (troubleshooting/tuning) is lacking or doesn't answer common questions.
+4. You should generally feel free to open issues ([https://github.com/eclipse/deeplearning4j/issues](https://github.com/eclipse/deeplearning4j/issues)) if you feel our documentation (troubleshooting/tuning) is lacking or doesn't answer common questions.
 5. Upon entering the room, please do more than say "hi". Information-rich questions and comments are appreciated. Please keep the content relevant. Please note the below channels for different parts of the conversation.
  * Contributors/building from source: [https://gitter.im/deeplearning4j/deeplearning4j/earlyadopters](https://gitter.im/deeplearning4j/deeplearning4j/earlyadopters)
 
diff --git a/deeplearning4j/README.md b/deeplearning4j/README.md
index f7892cb8f..517cc820e 100755
--- a/deeplearning4j/README.md
+++ b/deeplearning4j/README.md
@@ -38,7 +38,7 @@ To get started using Deeplearning4j, please go to our [Quickstart](https://deepl
 
 ---
 ## Documentation
-Documentation is available at [deeplearning4j.org](https://deeplearning4j.org/overview) and [JavaDocs](https://deeplearning4j.org/api/latest/). Open-source contributors can help us improve our documentation for Deeplearning4j by sending pull requests for the DL4J website [here](https://github.com/deeplearning4j/deeplearning4j/tree/gh-pages) and ND4J [here](https://github.com/deeplearning4j/nd4j/tree/gh-pages).
+Documentation is available at [deeplearning4j.org](https://deeplearning4j.org/overview) and [JavaDocs](https://deeplearning4j.org/api/latest/). Open-source contributors can help us improve our documentation for Deeplearning4j by sending pull requests for the DL4J website [here](https://github.com/eclipse/deeplearning4j-docs)
 
 ## Support
 
@@ -52,7 +52,7 @@ To install Deeplearning4J, see our [Quickstart](https://deeplearning4j.org/docs/
 
 Search Maven Central for [deeplearning4j](https://search.maven.org/#search%7Cga%7C1%7Cdeeplearning4j) to get a list of dependencies. 
 
-Add the dependency information to your `pom.xml` file. **We highly recommend downloading via Maven unless you plan to help us develop DL4J.** An easy way to get up-to-date dependencies is to use the ones listed in our [dl4j-examples POM](https://github.com/deeplearning4j/dl4j-examples/blob/master/pom.xml).
+Add the dependency information to your `pom.xml` file. **We highly recommend downloading via Maven unless you plan to help us develop DL4J.** An easy way to get up-to-date dependencies is to use the ones listed in our [dl4j-examples POM](https://github.com/eclipse/deeplearning4j-examples/blob/master/pom.xml).
 
 <!--
 #### Yum Install / Load RPM (Fedora or CentOS)
@@ -80,9 +80,9 @@ Note, be sure to install the ND4J modules you need first, especially the backend
 ---
 ## Contribute
 
-1. Check for [open issues](https://github.com/deeplearning4j/deeplearning4j/issues) or open a fresh one to start a discussion around a feature idea or a bug.
+1. Check for [open issues](https://github.com/eclipse/deeplearning4j/issues) or open a fresh one to start a discussion around a feature idea or a bug.
 2. If you feel uncomfortable or uncertain about an issue or your changes, don't hesitate to contact us on Gitter using the link above.
-3. Fork [the repository](https://github.com/deeplearning4j/deeplearning4j.git)
+3. Fork [the repository](https://github.com/eclipse/deeplearning4j.git)
    on GitHub to start making your changes (branch off of the master branch).
 4. Write a test that shows the bug was fixed or the feature works as expected.
 5. Note the repository follows
@@ -93,4 +93,4 @@ Note, be sure to install the ND4J modules you need first, especially the backend
    the repository to configure the Eclipse formatter, or by [using the Intellij
    plugin](https://github.com/HPI-Information-Systems/Metanome/wiki/Installing-the-google-styleguide-settings-in-intellij-and-eclipse).
 6. Send a pull request and bug us on Gitter until it gets merged and published. :)
-7. Add technical documentation on the [Deeplearning4j website](https://github.com/deeplearning4j/deeplearning4j/tree/gh-pages) and fix any typos you see.
+7. Add technical documentation on the [Deeplearning4j website](https://github.com/eclipse/deeplearning4j/tree/gh-pages) and fix any typos you see.
diff --git a/docs/README.md b/docs/README.md
index 8068cdd62..e5b024ce2 100644
--- a/docs/README.md
+++ b/docs/README.md
@@ -5,7 +5,7 @@
 Run `./gen_all_docs.sh` to generate documentation from source for all supported projects. For each documentation module, files will be put into a `doc_sources` folder where they are staged for copying to the primary docs repository. Note that the autogen docs require Python 2.
 
 To deploy a new version of documentation, first make sure to set `$DL4J_DOCS_DIR` to your local copy of 
-https://github.com/deeplearning4j/deeplearning4j-docs and set `$DL4J_VERSION` to a URI-friendly version string such as `v100-RC` (note the lack of decimals). Then run `./copy-to-dl4j-docs.sh`. This puts documentation
+https://github.com/eclipse/deeplearning4j-docs and set `$DL4J_VERSION` to a URI-friendly version string such as `v100-RC` (note the lack of decimals). Then run `./copy-to-dl4j-docs.sh`. This puts documentation
 into the right folders and you can use `git` to create a PR and update the live docs.
 
 The structure of this project (template files, generating code, mkdocs YAML) is closely aligned
diff --git a/docs/datavec/templates/overview.md b/docs/datavec/templates/overview.md
index adcf1ef2d..28eab3071 100644
--- a/docs/datavec/templates/overview.md
+++ b/docs/datavec/templates/overview.md
@@ -24,7 +24,7 @@ This video describes the conversion of image data to a vector.
 <iframe width="420" height="315" src="https://www.youtube.com/embed/EHHtyRKQIJ0" frameborder="0" allowfullscreen></iframe>
 
 ## Key Aspects
-- [DataVec](https://github.com/deeplearning4j/DataVec) uses an input/output format system (similar in some ways to how Hadoop MapReduce uses InputFormat to determine InputSplits and RecordReaders, DataVec also provides RecordReaders to Serialize Data)
+- [DataVec](https://github.com/eclipse/deeplearning4j/tree/master/datavec) uses an input/output format system (similar in some ways to how Hadoop MapReduce uses InputFormat to determine InputSplits and RecordReaders, DataVec also provides RecordReaders to Serialize Data)
 - Designed to support all major types of input data (text, CSV, audio, image and video) with these specific input formats
 - Uses an output format system to specify an implementation-neutral type of vector format (SVMLight, etc.)
 - Can be extended for specialized input formats (such as exotic image formats); i.e. You can write your own custom input format and let the rest of the codebase handle the transformation pipeline
@@ -62,7 +62,7 @@ A video tutorial of a simple DataVec transform along with code is available belo
 
 ## Example Java Code
 
-Our [examples](https://github.com/deeplearning4j/dl4j-examples) include a collection of DataVec examples.   
+Our [examples](https://github.com/eclipse/deeplearning4j-examples) include a collection of DataVec examples.   
 
 <!-- Note to Tom, write DataVec setup content
 
@@ -90,7 +90,7 @@ recordReader.initialize(new FileSplit(new File(labeledPath)));
 
 The RecordReader is a class in DataVec that helps convert the byte-oriented input into data that's oriented toward a record; i.e. a collection of elements that are fixed in number and indexed with a unique ID. Converting data to records is the process of vectorization. The record itself is a vector, each element of which is a feature.
 
-The [ImageRecordReader](https://github.com/deeplearning4j/DataVec/blob/a64389c08396bb39626201beeabb7c4d5f9288f9/datavec-data/datavec-data-image/src/main/java/org/datavec/image/recordreader/ImageRecordReader.java) is a subclass of the RecordReader and is built to automatically take in 28 x 28 pixel images. Thus, LFW images are scaled to 28 pixels x 28 pixels. You can change dimensions to match your custom images by changing the parameters fed to the ImageRecordReader, as long as you make sure to adjust the `nIn` hyperparameter, which will be equal to the product of image height x image width. 
+The [ImageRecordReader](https://github.com/eclipse/deeplearning4j/tree/master/datavec/blob/a64389c08396bb39626201beeabb7c4d5f9288f9/datavec-data/datavec-data-image/src/main/java/org/datavec/image/recordreader/ImageRecordReader.java) is a subclass of the RecordReader and is built to automatically take in 28 x 28 pixel images. Thus, LFW images are scaled to 28 pixels x 28 pixels. You can change dimensions to match your custom images by changing the parameters fed to the ImageRecordReader, as long as you make sure to adjust the `nIn` hyperparameter, which will be equal to the product of image height x image width. 
 
 Other parameters shown above include `true`, which instructs the reader to append a label to the record, and `labels`, which is the array of supervised values (e.g. targets) used to validate neural net model results. Here are all the RecordReader extensions that come pre-built with DataVec (you can find them by right-clicking on `RecordReader` in IntelliJ, clicking `Go To` in the drop-down menu, and selection `Implementations`):
 
diff --git a/docs/deeplearning4j-nlp/templates/doc2vec.md b/docs/deeplearning4j-nlp/templates/doc2vec.md
index b7a986abb..5daf94150 100644
--- a/docs/deeplearning4j-nlp/templates/doc2vec.md
+++ b/docs/deeplearning4j-nlp/templates/doc2vec.md
@@ -18,7 +18,7 @@ In the ParagraphVectors builder pattern, the `labels()` method points to the lab
     .labels(Arrays.asList("negative", "neutral","positive"))
 ```
 
-Here's a full working example of [classification with paragraph vectors](https://github.com/deeplearning4j/dl4j-examples/blob/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/nlp/paragraphvectors/ParagraphVectorsClassifierExample.java):
+Here's a full working example of [classification with paragraph vectors](https://github.com/eclipse/deeplearning4j-examples/blob/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/nlp/paragraphvectors/ParagraphVectorsClassifierExample.java):
 
 ``` java
     public void testDifferentLabels() throws Exception {
diff --git a/docs/deeplearning4j-nlp/templates/overview.md b/docs/deeplearning4j-nlp/templates/overview.md
index c1d291660..0ad46f458 100644
--- a/docs/deeplearning4j-nlp/templates/overview.md
+++ b/docs/deeplearning4j-nlp/templates/overview.md
@@ -16,7 +16,7 @@ Deeplearning4j's NLP relies on [ClearTK](https://cleartk.github.io/cleartk/), an
 
 There are several steps involved in processing natural language. The first is to iterate over your corpus to create a list of documents, which can be as short as a tweet, or as long as a newspaper article. This is performed by a SentenceIterator, which will appear like this:
 
-<script src="https://gist-it.appspot.com/https://github.com/deeplearning4j/dl4j-examples/blob/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/nlp/word2vec/Word2VecRawTextExample.java?slice=33:41"></script>
+<script src="https://gist-it.appspot.com/https://github.com/eclipse/deeplearning4j-examples/blob/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/nlp/word2vec/Word2VecRawTextExample.java?slice=33:41"></script>
 
 The SentenceIterator encapsulates a corpus or text, organizing it, say, as one Tweet per line. It is responsible for feeding text piece by piece into your natural language processor. The SentenceIterator is not analogous to a similarly named class, the DatasetIterator, which creates a dataset for training a neural net. Instead it creates a collection of strings by segmenting a corpus.
 
@@ -24,11 +24,11 @@ The SentenceIterator encapsulates a corpus or text, organizing it, say, as one T
 
 A Tokenizer further segments the text at the level of single words, also alternatively as n-grams. ClearTK contains the underlying tokenizers, such as parts of speech (PoS) and parse trees, which allow for both dependency and constituency parsing, like that employed by a recursive neural tensor network (RNTN).
 
-A Tokenizer is created and wrapped by a [TokenizerFactory](https://github.com/deeplearning4j/deeplearning4j/blob/6f027fd5075e3e76a38123ae5e28c00c17db4361/deeplearning4j-scaleout/deeplearning4j-nlp/src/main/java/org/deeplearning4j/text/tokenization/tokenizerfactory/UimaTokenizerFactory.java). The default tokens are words separated by spaces. The tokenization process also involves some machine learning to differentiate between ambibuous symbols like . which end sentences and also abbreviate words such as Mr. and vs.
+A Tokenizer is created and wrapped by a [TokenizerFactory](https://github.com/eclipse/deeplearning4j/blob/6f027fd5075e3e76a38123ae5e28c00c17db4361/deeplearning4j-scaleout/deeplearning4j-nlp/src/main/java/org/deeplearning4j/text/tokenization/tokenizerfactory/UimaTokenizerFactory.java). The default tokens are words separated by spaces. The tokenization process also involves some machine learning to differentiate between ambibuous symbols like . which end sentences and also abbreviate words such as Mr. and vs.
 
 Both Tokenizers and SentenceIterators work with Preprocessors to deal with anomalies in messy text like Unicode, and to render such text, say, as lowercase characters uniformly.
 
-<script src="https://gist-it.appspot.com/https://github.com/deeplearning4j/dl4j-examples/blob/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/nlp/word2vec/Word2VecRawTextExample.java?slice=43:57"></script>
+<script src="https://gist-it.appspot.com/https://github.com/eclipse/deeplearning4j-examples/blob/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/nlp/word2vec/Word2VecRawTextExample.java?slice=43:57"></script>
 
 
 
@@ -38,6 +38,6 @@ Each document has to be tokenized to create a vocab, the set of words that matte
 
 The vocab cache stores metadata for methods such as Word2vec and Bag of Words, which treat words in radically different ways. Word2vec creates representations of words, or neural word embeddings, in the form of vectors that are hundreds of coefficients long. Those coefficients help neural nets predict the likelihood of a word appearing in any given context; for example, after another word. Here's Word2vec, configured:
 
-<script src="https://gist-it.appspot.com/https://github.com/deeplearning4j/dl4j-examples/blob/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/nlp/word2vec/Word2VecRawTextExample.java"></script>
+<script src="https://gist-it.appspot.com/https://github.com/eclipse/deeplearning4j-examples/blob/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/nlp/word2vec/Word2VecRawTextExample.java"></script>
 
 Once you obtain word vectors, you can feed them into a deep net for classification, prediction, sentiment analysis and the like.
\ No newline at end of file
diff --git a/docs/deeplearning4j-nlp/templates/word2vec.md b/docs/deeplearning4j-nlp/templates/word2vec.md
index b5a7306bc..d49b02265 100644
--- a/docs/deeplearning4j-nlp/templates/word2vec.md
+++ b/docs/deeplearning4j-nlp/templates/word2vec.md
@@ -32,7 +32,7 @@ Why? Because words are simply discrete states like the other data mentioned abov
 
 The purpose and usefulness of Word2vec is to group the vectors of similar words together in vectorspace. That is, it detects similarities mathematically. Word2vec creates vectors that are distributed numerical representations of word features, features such as the context of individual words. It does so without human intervention.
 
-Given enough data, usage and contexts, Word2vec can make highly accurate guesses about a word’s meaning based on past appearances. Those guesses can be used to establish a word's association with other words (e.g. "man" is to "boy" what "woman" is to "girl"), or cluster documents and classify them by topic. Those clusters can form the basis of search, [sentiment analysis](https://github.com/deeplearning4j/dl4j-examples/blob/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/recurrent/word2vecsentiment/Word2VecSentimentRNN.java) and recommendations in such diverse fields as scientific research, legal discovery, e-commerce and customer relationship management. 
+Given enough data, usage and contexts, Word2vec can make highly accurate guesses about a word’s meaning based on past appearances. Those guesses can be used to establish a word's association with other words (e.g. "man" is to "boy" what "woman" is to "girl"), or cluster documents and classify them by topic. Those clusters can form the basis of search, [sentiment analysis](https://github.com/eclipse/deeplearning4j-examples/blob/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/recurrent/word2vecsentiment/Word2VecSentimentRNN.java) and recommendations in such diverse fields as scientific research, legal discovery, e-commerce and customer relationship management. 
 
 The output of the Word2vec neural net is a vocabulary in which each item has a vector attached to it, which can be fed into a deep-learning net or simply queried to detect relationships between words. 
 
@@ -216,7 +216,7 @@ This configuration accepts a number of hyperparameters. A few require some expla
 * *tokenizer* feeds it the words from the current batch. 
 * *vec.fit()* tells the configured net to begin training. 
 
-An example for [uptraining your previously trained word vectors is here](https://github.com/deeplearning4j/dl4j-examples/blob/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/nlp/word2vec/Word2VecUptrainingExample.java).
+An example for [uptraining your previously trained word vectors is here](https://github.com/eclipse/deeplearning4j-examples/blob/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/nlp/word2vec/Word2VecUptrainingExample.java).
 
 ### <a name="eval">Evaluating the Model, Using Word2vec</a> 
 
@@ -253,7 +253,7 @@ With `vec.wordsNearest("word1", numWordsNearest)`, the words printed to the scre
 
 ### Visualizing the Model
 
-We rely on [TSNE](https://lvdmaaten.github.io/tsne/) to reduce the dimensionality of word feature vectors and project words into a two or three-dimensional space. The full [DL4J/ND4J example for TSNE is here](https://github.com/deeplearning4j/dl4j-examples/blob/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/nlp/tsne/TSNEStandardExample.java).
+We rely on [TSNE](https://lvdmaaten.github.io/tsne/) to reduce the dimensionality of word feature vectors and project words into a two or three-dimensional space. The full [DL4J/ND4J example for TSNE is here](https://github.com/eclipse/deeplearning4j-examples/blob/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/nlp/tsne/TSNEStandardExample.java).
 
 ``` java
         Nd4j.setDataType(DataBuffer.Type.DOUBLE);
@@ -363,11 +363,11 @@ This n-gram is then fed into a neural network to learn the significance of a giv
 
 ### <a name="code">A Working Example</a>
 
-**Please note** : The code below may be outdated. For updated examples, please see our [dl4j-examples repository on Github](https://github.com/deeplearning4j/dl4j-examples/tree/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/nlp).
+**Please note** : The code below may be outdated. For updated examples, please see our [dl4j-examples repository on Github](https://github.com/eclipse/deeplearning4j-examples/tree/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/nlp).
 
-Now that you have a basic idea of how to set up Word2Vec, here's [one example](https://github.com/deeplearning4j/dl4j-examples/blob/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/nlp/word2vec/Word2VecRawTextExample.java) of how it can be used with DL4J's API:
+Now that you have a basic idea of how to set up Word2Vec, here's [one example](https://github.com/eclipse/deeplearning4j-examples/blob/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/nlp/word2vec/Word2VecRawTextExample.java) of how it can be used with DL4J's API:
 
-<script src="https://gist-it.appspot.com/https://github.com/deeplearning4j/dl4j-examples/blob/master/src/main/java/org/deeplearning4j/examples/nlp/word2vec/Word2VecRawTextExample.java?slice=22:64"></script>
+<script src="https://gist-it.appspot.com/https://github.com/eclipse/deeplearning4j-examples/blob/master/src/main/java/org/deeplearning4j/examples/nlp/word2vec/Word2VecRawTextExample.java?slice=22:64"></script>
 
 After following the instructions in the [Quickstart](./deeplearning4j-quickstart), you can open this example in IntelliJ and hit run to see it work. If you query the Word2vec model with a word isn't contained in the training corpus, it will return null. 
 
@@ -463,7 +463,7 @@ Loading and saving GloVe models to word2vec can be done like so:
 
 ### <a name="sequence">Sequence Vectors</a>
 
-Deeplearning4j has a class called [SequenceVectors](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nlp-parent/deeplearning4j-nlp/src/main/java/org/deeplearning4j/models/sequencevectors/SequenceVectors.java), which is one level of abstraction above word vectors, and which allows you to extract features from any sequence, including social media profiles, transactions, proteins, etc. If data can be described as sequence, it can be learned via skip-gram and hierarchic softmax with the AbstractVectors class. This is compatible with the [DeepWalk algorithm](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-graph/src/main/java/org/deeplearning4j/graph/models/deepwalk/DeepWalk.java), also implemented in Deeplearning4j. 
+Deeplearning4j has a class called [SequenceVectors](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nlp-parent/deeplearning4j-nlp/src/main/java/org/deeplearning4j/models/sequencevectors/SequenceVectors.java), which is one level of abstraction above word vectors, and which allows you to extract features from any sequence, including social media profiles, transactions, proteins, etc. If data can be described as sequence, it can be learned via skip-gram and hierarchic softmax with the AbstractVectors class. This is compatible with the [DeepWalk algorithm](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-graph/src/main/java/org/deeplearning4j/graph/models/deepwalk/DeepWalk.java), also implemented in Deeplearning4j. 
 
 ### <a name="features">Word2Vec Features on Deeplearning4j</a>
 
@@ -477,8 +477,8 @@ Deeplearning4j has a class called [SequenceVectors](https://github.com/deeplearn
 
 ### Doc2vec & Other NLP Resources
 
-* [DL4J Example of Text Classification With Word2vec & RNNs](https://github.com/deeplearning4j/dl4j-examples/blob/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/recurrent/word2vecsentiment/Word2VecSentimentRNN.java)
-* [DL4J Example of Text Classification With Paragraph Vectors](https://github.com/deeplearning4j/dl4j-examples/blob/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/nlp/paragraphvectors/ParagraphVectorsClassifierExample.java)
+* [DL4J Example of Text Classification With Word2vec & RNNs](https://github.com/eclipse/deeplearning4j-examples/blob/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/recurrent/word2vecsentiment/Word2VecSentimentRNN.java)
+* [DL4J Example of Text Classification With Paragraph Vectors](https://github.com/eclipse/deeplearning4j-examples/blob/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/nlp/paragraphvectors/ParagraphVectorsClassifierExample.java)
 * [Doc2vec, or Paragraph Vectors, With Deeplearning4j](./deeplearning4j-nlp-doc2vec)
 * [Thought Vectors, Natural Language Processing & the Future of AI](https://skymind.ai/wiki/thought-vectors)
 * [Quora: How Does Word2vec Work?](http://www.quora.com/How-does-word2vec-work)
diff --git a/docs/deeplearning4j-nn/templates/computationgraph.md b/docs/deeplearning4j-nn/templates/computationgraph.md
index 7fe2ae2c8..a5ced0ceb 100644
--- a/docs/deeplearning4j-nn/templates/computationgraph.md
+++ b/docs/deeplearning4j-nn/templates/computationgraph.md
@@ -29,8 +29,8 @@ This page describes how to build more complicated networks, using DL4J's Computa
 
 DL4J has two types of networks comprised of multiple layers:
 
-- The [MultiLayerNetwork](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/multilayer/MultiLayerNetwork.java), which is essentially a stack of neural network layers (with a single input layer and single output layer), and
-- The [ComputationGraph](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/graph/ComputationGraph.java), which allows for greater freedom in network architectures
+- The [MultiLayerNetwork](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/multilayer/MultiLayerNetwork.java), which is essentially a stack of neural network layers (with a single input layer and single output layer), and
+- The [ComputationGraph](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/graph/ComputationGraph.java), which allows for greater freedom in network architectures
 
 
 Specifically, the ComputationGraph allows for networks to be built with the following features:
@@ -53,7 +53,7 @@ Examples of some architectures that can be built using ComputationGraph include:
 - Recurrent neural networks with skip connections
 - [GoogLeNet](http://arxiv.org/abs/1409.4842), a complex type of convolutional netural network for image classification
 - [Image caption generation](http://arxiv.org/abs/1411.4555)
-- [Convolutional networks for sentence classification](https://github.com/deeplearning4j/dl4j-examples/blob/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/convolution/sentenceclassification/CnnSentenceClassificationExample.java)
+- [Convolutional networks for sentence classification](https://github.com/eclipse/deeplearning4j-examples/blob/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/convolution/sentenceclassification/CnnSentenceClassificationExample.java)
 - [Residual learning convolutional neural networks](http://arxiv.org/abs/1512.03385)
 
 
@@ -61,7 +61,7 @@ Examples of some architectures that can be built using ComputationGraph include:
 
 ### <a name="vertextypes">Types of Graph Vertices</a>
 
-The basic idea is that in the ComputationGraph, the core building block is the [GraphVertex](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/graph/vertex/GraphVertex.java), instead of layers. Layers (or, more accurately the [LayerVertex](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/graph/vertex/impl/LayerVertex.java) objects), are but one type of vertex in the graph. Other types of vertices include:
+The basic idea is that in the ComputationGraph, the core building block is the [GraphVertex](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/graph/vertex/GraphVertex.java), instead of layers. Layers (or, more accurately the [LayerVertex](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/graph/vertex/impl/LayerVertex.java) objects), are but one type of vertex in the graph. Other types of vertices include:
 
 - Input Vertices
 - Element-wise operation vertices
@@ -72,7 +72,7 @@ The basic idea is that in the ComputationGraph, the core building block is the [
 These types of graph vertices are described briefly below.
 
 **LayerVertex**: Layer vertices (graph vertices with neural network layers) are added using the ```.addLayer(String,Layer,String...)``` method. The first argument is the label for the layer, and the last arguments are the inputs to that layer.
-If you need to manually add an [InputPreProcessor](https://github.com/deeplearning4j/deeplearning4j/tree/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/preprocessor) (usually this is unnecessary - see next section) you can use the ```.addLayer(String,Layer,InputPreProcessor,String...)``` method.
+If you need to manually add an [InputPreProcessor](https://github.com/eclipse/deeplearning4j/tree/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/preprocessor) (usually this is unnecessary - see next section) you can use the ```.addLayer(String,Layer,InputPreProcessor,String...)``` method.
 
 **InputVertex**: Input vertices are specified by the ```addInputs(String...)``` method in your configuration. The strings used as inputs can be arbitrary - they are user-defined labels, and can be referenced later in the configuration. The number of strings provided define the number of inputs; the order of the input also defines the order of the corresponding INDArrays in the fit methods (or the DataSet/MultiDataSet objects).
 
@@ -82,9 +82,9 @@ If you need to manually add an [InputPreProcessor](https://github.com/deeplearni
 
 **SubsetVertex**: The subset vertex allows you to get only part of the activations out of another vertex. For example, to get the first 5 activations out of another vertex with label "layer1", you can use ```.addVertex("subset1", new SubsetVertex(0,4), "layer1")```: this means that the 0th through 4th (inclusive) activations out of the "layer1" vertex will be used as output from the subset vertex.
 
-**PreProcessorVertex**: Occasionally, you might want to the functionality of an [InputPreProcessor](https://github.com/deeplearning4j/deeplearning4j/tree/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/preprocessor) without that preprocessor being associated with a layer. The PreProcessorVertex allows you to do this.
+**PreProcessorVertex**: Occasionally, you might want to the functionality of an [InputPreProcessor](https://github.com/eclipse/deeplearning4j/tree/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/preprocessor) without that preprocessor being associated with a layer. The PreProcessorVertex allows you to do this.
 
-Finally, it is also possible to define custom graph vertices by implementing both a [configuration](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/graph/GraphVertex.java) and [implementation](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/graph/vertex/GraphVertex.java) class for your custom GraphVertex.
+Finally, it is also possible to define custom graph vertices by implementing both a [configuration](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/graph/GraphVertex.java) and [implementation](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/graph/vertex/GraphVertex.java) class for your custom GraphVertex.
 
 
 ### <a name="rnnskip">Example 1: Recurrent Network with Skip Connections</a>
@@ -165,7 +165,7 @@ One feature of the ComputationGraphConfiguration is that you can specify the typ
 
 The setInputType method has two effects:
 
-1. It will automatically add any [InputPreProcessor](https://github.com/deeplearning4j/deeplearning4j/tree/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/preprocessor)s as required. InputPreProcessors are necessary to handle the interaction between for example fully connected (dense) and convolutional layers, or recurrent and fully connected layers.
+1. It will automatically add any [InputPreProcessor](https://github.com/eclipse/deeplearning4j/tree/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/preprocessor)s as required. InputPreProcessors are necessary to handle the interaction between for example fully connected (dense) and convolutional layers, or recurrent and fully connected layers.
 2. It will automatically calculate the number of inputs (.nIn(x) config) to a layer. Thus, if you are using the ```setInputTypes(InputType...)``` functionality, it is not necessary to manually specify the .nIn(x) options in your configuration. This can simplify building some architectures (such as convolutional networks with fully connected layers). If the .nIn(x) is specified for a layer, the network will not override this when using the InputType functionality.
 
 
@@ -188,8 +188,8 @@ MultiDataSet is multiple input and/or multiple output version of DataSet. It may
 
 There are currently two ways to use a MultiDataSetIterator:
 
-- By implementing the [MultiDataSetIterator](https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/dataset/api/iterator/MultiDataSetIterator.java) interface directly
-- By using the [RecordReaderMultiDataSetIterator](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-data/deeplearning4j-datavec-iterators/src/main/java/org/deeplearning4j/datasets/datavec/RecordReaderMultiDataSetIterator.java) in conjuction with DataVec record readers
+- By implementing the [MultiDataSetIterator](https://github.com/eclipse/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/dataset/api/iterator/MultiDataSetIterator.java) interface directly
+- By using the [RecordReaderMultiDataSetIterator](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-data/deeplearning4j-datavec-iterators/src/main/java/org/deeplearning4j/datasets/datavec/RecordReaderMultiDataSetIterator.java) in conjuction with DataVec record readers
 
 
 The RecordReaderMultiDataSetIterator provides a number of options for loading data. In particular, the RecordReaderMultiDataSetIterator provides the following functionality:
@@ -200,7 +200,7 @@ The RecordReaderMultiDataSetIterator provides a number of options for loading da
 - It is possible to convert single columns from a class index to a one-hot representation
 
 
-Some basic examples on how to use the RecordReaderMultiDataSetIterator follow. You might also find [these unit tests](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/datasets/datavec/RecordReaderMultiDataSetIteratorTest.java) to be useful.
+Some basic examples on how to use the RecordReaderMultiDataSetIterator follow. You might also find [these unit tests](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/datasets/datavec/RecordReaderMultiDataSetIteratorTest.java) to be useful.
 
 ### <a name="rrmdsi1">RecordReaderMultiDataSetIterator Example 1: Regression Data</a>
 
diff --git a/docs/deeplearning4j-nn/templates/custom-layer.md b/docs/deeplearning4j-nn/templates/custom-layer.md
index 767dba06a..7bf059a83 100644
--- a/docs/deeplearning4j-nn/templates/custom-layer.md
+++ b/docs/deeplearning4j-nn/templates/custom-layer.md
@@ -45,7 +45,7 @@ These tests should at a minimum include the following:
 
 ## Example
 
-A full custom layer example is available in our [examples repository](https://github.com/deeplearning4j/dl4j-examples/tree/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/misc/customlayers).
+A full custom layer example is available in our [examples repository](https://github.com/eclipse/deeplearning4j-examples/tree/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/misc/customlayers).
 
 ## API
 
diff --git a/docs/deeplearning4j-nn/templates/early-stopping.md b/docs/deeplearning4j-nn/templates/early-stopping.md
index 14b622ca1..df2708c2f 100644
--- a/docs/deeplearning4j-nn/templates/early-stopping.md
+++ b/docs/deeplearning4j-nn/templates/early-stopping.md
@@ -30,7 +30,7 @@ The best model is the one saved at the time of the vertical dotted line - i.e.,
 
 Using DL4J's early stopping functionality requires you to provide a number of configuration options:
 
-* A score calculator, such as the *DataSetLossCalculator*([JavaDoc](https://deeplearning4j.org/api/{{page.version}}/org/deeplearning4j/earlystopping/scorecalc/DataSetLossCalculator.html), [Source Code](https://github.com/deeplearning4j/deeplearning4j/blob/c152293ef8d1094c281f5375ded61ff5f8eb6587/deeplearning4j-core/src/main/java/org/deeplearning4j/earlystopping/scorecalc/DataSetLossCalculator.java)) for a Multi Layer Network, or *DataSetLossCalculatorCG* ([JavaDoc](https://deeplearning4j.org/api/{{page.version}}/org/deeplearning4j/earlystopping/scorecalc/DataSetLossCalculatorCG.html), [Source Code](https://github.com/deeplearning4j/deeplearning4j/blob/c152293ef8d1094c281f5375ded61ff5f8eb6587/deeplearning4j-core/src/main/java/org/deeplearning4j/earlystopping/scorecalc/DataSetLossCalculatorCG.java)) for a Computation Graph. Is used to calculate at every epoch (for example: the loss function value on a test set, or the accuracy on the test set)
+* A score calculator, such as the *DataSetLossCalculator*([JavaDoc](https://deeplearning4j.org/api/{{page.version}}/org/deeplearning4j/earlystopping/scorecalc/DataSetLossCalculator.html), [Source Code](https://github.com/eclipse/deeplearning4j/blob/c152293ef8d1094c281f5375ded61ff5f8eb6587/deeplearning4j-core/src/main/java/org/deeplearning4j/earlystopping/scorecalc/DataSetLossCalculator.java)) for a Multi Layer Network, or *DataSetLossCalculatorCG* ([JavaDoc](https://deeplearning4j.org/api/{{page.version}}/org/deeplearning4j/earlystopping/scorecalc/DataSetLossCalculatorCG.html), [Source Code](https://github.com/eclipse/deeplearning4j/blob/c152293ef8d1094c281f5375ded61ff5f8eb6587/deeplearning4j-core/src/main/java/org/deeplearning4j/earlystopping/scorecalc/DataSetLossCalculatorCG.java)) for a Computation Graph. Is used to calculate at every epoch (for example: the loss function value on a test set, or the accuracy on the test set)
 * How frequently we want to calculate the score function (default: every epoch)
 * One or more termination conditions, which tell the training process when to stop. There are two classes of termination conditions:
   * Epoch termination conditions: evaluated every N epochs
diff --git a/docs/deeplearning4j-nn/templates/evaluation.md b/docs/deeplearning4j-nn/templates/evaluation.md
index f78b97c4d..85ed12474 100644
--- a/docs/deeplearning4j-nn/templates/evaluation.md
+++ b/docs/deeplearning4j-nn/templates/evaluation.md
@@ -22,7 +22,7 @@ DataSetIterator myTestData = ...
 Evaluation eval = model.evaluate(myTestData);
 ```
 
-However, evaluation can be performed on individual minibatches also. Here is an example taken from our dataexamples/CSVExample in the [Examples](https://github.com/deeplearning4j/dl4j-examples) project.
+However, evaluation can be performed on individual minibatches also. Here is an example taken from our dataexamples/CSVExample in the [Examples](https://github.com/eclipse/deeplearning4j-examples) project.
 
 The CSV example has CSV data for 3 classes of flowers and builds a simple feed forward neural network to classify the flowers based on 4 measurements. 
 
diff --git a/docs/deeplearning4j-nn/templates/model-persistence.md b/docs/deeplearning4j-nn/templates/model-persistence.md
index bb5229d86..ef4d593e9 100644
--- a/docs/deeplearning4j-nn/templates/model-persistence.md
+++ b/docs/deeplearning4j-nn/templates/model-persistence.md
@@ -10,7 +10,7 @@ weight: 10
 
 The `ModelSerializer` is a class which handles loading and saving models. There are two methods for saving models shown in the examples through the link. The first example saves a normal multilayer network, the second one saves a [computation graph](https://deeplearning4j.org/compgraph).
 
-Here is a [basic example](https://github.com/deeplearning4j/dl4j-examples/tree/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/misc/modelsaving) with code to save a computation graph using the `ModelSerializer` class, as well as an example of using ModelSerializer to save a neural net built using MultiLayer configuration.
+Here is a [basic example](https://github.com/eclipse/deeplearning4j-examples/tree/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/misc/modelsaving) with code to save a computation graph using the `ModelSerializer` class, as well as an example of using ModelSerializer to save a neural net built using MultiLayer configuration.
 
 ### RNG Seed
 
diff --git a/docs/deeplearning4j-nn/templates/recurrent.md b/docs/deeplearning4j-nn/templates/recurrent.md
index c2552a6cc..0b33981e7 100644
--- a/docs/deeplearning4j-nn/templates/recurrent.md
+++ b/docs/deeplearning4j-nn/templates/recurrent.md
@@ -182,7 +182,7 @@ In either case, we can then do the following:
 
 RNN layers in DL4J can be combined with other layer types. For example, it is possible to combine DenseLayer and LSTM layers in the same network; or combine Convolutional (CNN) layers and LSTM layers for video.
 
-Of course, the DenseLayer and Convolutional layers do not handle time series data - they expect a different  type of input. To deal with this, we need to use the layer preprocessor functionality: for example, the CnnToRnnPreProcessor and FeedForwardToRnnPreprocessor classes. See [here](https://github.com/deeplearning4j/deeplearning4j/tree/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/preprocessor) for all preprocessors. Fortunately, in most situations, the DL4J configuration system will automatically add these preprocessors as required. However, the preprocessors can be added manually (overriding the automatic addition of preprocessors, for each layer).
+Of course, the DenseLayer and Convolutional layers do not handle time series data - they expect a different  type of input. To deal with this, we need to use the layer preprocessor functionality: for example, the CnnToRnnPreProcessor and FeedForwardToRnnPreprocessor classes. See [here](https://github.com/eclipse/deeplearning4j/tree/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/preprocessor) for all preprocessors. Fortunately, in most situations, the DL4J configuration system will automatically add these preprocessors as required. However, the preprocessors can be added manually (overriding the automatic addition of preprocessors, for each layer).
 
 For example, to manually add a preprocessor between layers 1 and 2, add the following to your network configuration: `.inputPreProcessor(2, new RnnToFeedForwardPreProcessor())`.
 
@@ -252,13 +252,13 @@ This method also supports:
 
 Note that in all cases, each line in the data files represents one time step.
 
-(In addition to the examples below, you might find [these unit tests](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/datasets/datavec/RecordReaderDataSetiteratorTest.java) to be of some use.)
+(In addition to the examples below, you might find [these unit tests](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/datasets/datavec/RecordReaderDataSetiteratorTest.java) to be of some use.)
 
 #### Example 1: Time Series of Same Length, Input and Labels in Separate Files
 
 Suppose we have 10 time series in our training data, represented by 20 files: 10 files for the input of each time series, and 10 files for the output/labels. For now, assume these 20 files all contain the same number of time steps (i.e., same number of rows).
 
-To use the [SequenceRecordReaderDataSetIterator](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-data/deeplearning4j-datavec-iterators/src/main/java/org/deeplearning4j/datasets/datavec/SequenceRecordReaderDataSetIterator.java) and [CSVSequenceRecordReader](https://github.com/deeplearning4j/deeplearning4j/blob/master/datavec/datavec-api/src/main/java/org/datavec/api/records/reader/impl/csv/CSVSequenceRecordReader.java) approaches, we first create two CSVSequenceRecordReader objects, one for input and one for labels:
+To use the [SequenceRecordReaderDataSetIterator](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-data/deeplearning4j-datavec-iterators/src/main/java/org/deeplearning4j/datasets/datavec/SequenceRecordReaderDataSetIterator.java) and [CSVSequenceRecordReader](https://github.com/eclipse/deeplearning4j/blob/master/datavec/datavec-api/src/main/java/org/datavec/api/records/reader/impl/csv/CSVSequenceRecordReader.java) approaches, we first create two CSVSequenceRecordReader objects, one for input and one for labels:
 
     SequenceRecordReader featureReader = new CSVSequenceRecordReader(1, ",");
     SequenceRecordReader labelReader = new CSVSequenceRecordReader(1, ",");
@@ -266,7 +266,7 @@ To use the [SequenceRecordReaderDataSetIterator](https://github.com/deeplearning
 This particular constructor takes the number of lines to skip (1 row skipped here), and the delimiter (comma character used here).
 
 Second, we need to initialize these two readers, by telling them where to get the data from. We do this with an InputSplit object.
-Suppose that our time series are numbered, with file names "myInput_0.csv", "myInput_1.csv", ..., "myLabels_0.csv", etc. One approach is to use the [NumberedFileInputSplit](https://github.com/deeplearning4j/deeplearning4j/blob/master/datavec/datavec-api/src/main/java/org/datavec/api/split/NumberedFileInputSplit.java):
+Suppose that our time series are numbered, with file names "myInput_0.csv", "myInput_1.csv", ..., "myLabels_0.csv", etc. One approach is to use the [NumberedFileInputSplit](https://github.com/eclipse/deeplearning4j/blob/master/datavec/datavec-api/src/main/java/org/datavec/api/split/NumberedFileInputSplit.java):
 
     featureReader.initialize(new NumberedFileInputSplit("/path/to/data/myInput_%d.csv", 0, 9));
     labelReader.initialize(new NumberedFileInputSplit(/path/to/data/myLabels_%d.csv", 0, 9));
diff --git a/docs/deeplearning4j-nn/templates/visualization.md b/docs/deeplearning4j-nn/templates/visualization.md
index e178fcc77..d4a4b30fc 100644
--- a/docs/deeplearning4j-nn/templates/visualization.md
+++ b/docs/deeplearning4j-nn/templates/visualization.md
@@ -58,9 +58,9 @@ You can set the port by using the ```org.deeplearning4j.ui.port``` system proper
 Information will then be collected and routed to the UI when you call the ```fit``` method on your network.
 
 
-**Example:** [See a UI example here](https://github.com/deeplearning4j/dl4j-examples/blob/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/userInterface/UIExample.java)
+**Example:** [See a UI example here](https://github.com/eclipse/deeplearning4j-examples/blob/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/userInterface/UIExample.java)
 
-The full set of UI examples are available [here](https://github.com/deeplearning4j/dl4j-examples/tree/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/userInterface).
+The full set of UI examples are available [here](https://github.com/eclipse/deeplearning4j-examples/tree/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/userInterface).
 
 
 ### <a name="overviewpage">Deeplearning4j UI: The Overview Page</a>
@@ -137,7 +137,7 @@ First, in the JVM running the UI (note this is the server):
 This will require the ```deeplearning4j-ui_2.10``` or ```deeplearning4j-ui_2.11``` dependency. (NOTE THIS IS NOT THE CLIENT THIS IS YOUR SERVER - SEE BELOW FOR THE CLIENT WHICH USES: deeplearning4j-ui-model)
 
 Client (both spark and standalone neural networks using simple deeplearning4j-nn)
-Second, for your neural net (Note this example is for spark, but computation graph and multi layer network both have the equivalemtn setListeners method with the same usage, [example found here](https://github.com/deeplearning4j/dl4j-examples/blob/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/userInterface/RemoteUIExample.java)):
+Second, for your neural net (Note this example is for spark, but computation graph and multi layer network both have the equivalemtn setListeners method with the same usage, [example found here](https://github.com/eclipse/deeplearning4j-examples/blob/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/userInterface/RemoteUIExample.java)):
 
 ```
     SparkDl4jMultiLayer sparkNet = new SparkDl4jMultiLayer(sc, conf, tm);
@@ -214,7 +214,7 @@ The layer update histogram is displayed for the most recent iteration only.
 - Keep an eye out for very large values: this can indicate exploding gradients in your network
     - Exploding gradients are problematic as they can 'mess up' the parameters of your network
     - In this case, it may indicate a weight initialization, learning rate or input/labels data normalization issue
-    - In the case of recurrent neural networks, adding some [gradient normalization or gradient clipping](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/GradientNormalization.java) may help
+    - In the case of recurrent neural networks, adding some [gradient normalization or gradient clipping](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/GradientNormalization.java) may help
 
 **Model Page: Parameter Learning Rates Chart**
 
diff --git a/docs/deeplearning4j-scaleout/templates/data-howto.md b/docs/deeplearning4j-scaleout/templates/data-howto.md
index 0b2d3fa9b..fefdcfb10 100644
--- a/docs/deeplearning4j-scaleout/templates/data-howto.md
+++ b/docs/deeplearning4j-scaleout/templates/data-howto.md
@@ -231,7 +231,7 @@ while(iter.hasNext()){
 Note that for the purposes of Spark, the exact file names don't matter.
 The process for saving MultiDataSets is almost identical.
 
-As an aside: you can read these saved DataSet objects on a single machine (for non-Spark training) using [FileDataSetIterator](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-data/deeplearning4j-utility-iterators/src/main/java/org/deeplearning4j/datasets/iterator/file/FileDataSetIterator.java)).
+As an aside: you can read these saved DataSet objects on a single machine (for non-Spark training) using [FileDataSetIterator](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-data/deeplearning4j-utility-iterators/src/main/java/org/deeplearning4j/datasets/iterator/file/FileDataSetIterator.java)).
 
 An alternative approach is to save directly to the cluster using output streams, to (for example) HDFS. This can only be done if the machine running the code is properly configured with the required libraries and access rights. For example, to save the DataSets directly to HDFS you could use:
 
@@ -370,10 +370,10 @@ JavaRDD<DataSet> dataSetRdd = sequencesRdd.map(new DataVecSequenceDataSetFunctio
 
 This guide shows how to create an ```RDD<DataSet>``` for image classification, starting from images stored either locally, or on a network file system such as HDFS.
 
-The approach here used (added in 1.0.0-beta3) is to first preprocess the images into batches of files - [FileBatch](https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-common/src/main/java/org/nd4j/api/loader/FileBatch.java) objects.
+The approach here used (added in 1.0.0-beta3) is to first preprocess the images into batches of files - [FileBatch](https://github.com/eclipse/deeplearning4j/blob/master/nd4j/nd4j-common/src/main/java/org/nd4j/api/loader/FileBatch.java) objects.
 The motivation for this approach is simple: the original image files typically use efficient compresion (JPEG for example) which is much more space (and network) efficient than a bitmap (int8 or 32-bit floating point) representation. However, on a cluster we want to minimize disk reads due to latency issues with remote storage - one file read/transfer is going to be faster than ```minibatchSize``` remote file reads.
 
-The [TinyImageNet example](https://github.com/deeplearning4j/dl4j-examples/tree/master/dl4j-spark-examples/dl4j-spark/src/main/java/org/deeplearning4j/tinyimagenet) also shows how this can be done.
+The [TinyImageNet example](https://github.com/eclipse/deeplearning4j-examples/tree/master/dl4j-spark-examples/dl4j-spark/src/main/java/org/deeplearning4j/tinyimagenet) also shows how this can be done.
 
 Note that one limitation of the implementation is that the set of classes (i.e., the class/category labels when doing classification) needs to be known, provided or collected manually. This differs from using ImageRecordReader for classification on a single machine, which can automatically infer the set of class labels.
 
@@ -415,7 +415,7 @@ SparkDataUtils.createFileBatchesSpark(sourceDirectory, destinationDirectory, bat
 ```
 
 **Step 2: Training**
-The data pipeline for image classification can be constructed as follows. This code is taken from the [TinyImageNet example](https://github.com/deeplearning4j/dl4j-examples/blob/master/dl4j-spark-examples/dl4j-spark/src/main/java/org/deeplearning4j/tinyimagenet/TrainSpark.java):
+The data pipeline for image classification can be constructed as follows. This code is taken from the [TinyImageNet example](https://github.com/eclipse/deeplearning4j-examples/blob/master/dl4j-spark-examples/dl4j-spark/src/main/java/org/deeplearning4j/tinyimagenet/TrainSpark.java):
 ```
 //Create data loader
 int imageHeightWidth = 64;      //64x64 pixel input to network
@@ -458,10 +458,10 @@ When files represent a single record/example (instead of a minibatch) in a custo
 
 The interfaces of note are:
 
-* [DataSetLoader](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-core/src/main/java/org/deeplearning4j/api/loader/DataSetLoader.java)
-* [MultiDataSetLoader](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-core/src/main/java/org/deeplearning4j/api/loader/MultiDataSetLoader.java)
+* [DataSetLoader](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-core/src/main/java/org/deeplearning4j/api/loader/DataSetLoader.java)
+* [MultiDataSetLoader](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-core/src/main/java/org/deeplearning4j/api/loader/MultiDataSetLoader.java)
 
-Both of which extend the single-method [Loader](https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-common/src/main/java/org/nd4j/api/loader/Loader.java) interface.
+Both of which extend the single-method [Loader](https://github.com/eclipse/deeplearning4j/blob/master/nd4j/nd4j-common/src/main/java/org/nd4j/api/loader/Loader.java) interface.
 
 Suppose a HDFS directory contains a number of files, each being a minibatch in some custom format.
 These can be loaded using the following process:
diff --git a/docs/deeplearning4j-scaleout/templates/howto.md b/docs/deeplearning4j-scaleout/templates/howto.md
index be184133c..500b1a241 100644
--- a/docs/deeplearning4j-scaleout/templates/howto.md
+++ b/docs/deeplearning4j-scaleout/templates/howto.md
@@ -101,7 +101,7 @@ When using Spark submit, you will need an uber-jar to submit to start and run yo
 
 We recommend that you use the maven shade plugin for building an uber-jar. There are alternative tools/plugins for this purpose, but these do not always include all relevant files from the source jars, such as those required for Java's ServiceLoader mechanism to function correctly. (The ServiceLoader mechanism is used by ND4J and a lot of other software libraries).
 
-A Maven shade configuration suitable for this purpose is provided in the example standalone sample project [pom.xml file](https://github.com/deeplearning4j/dl4j-examples/blob/master/standalone-sample-project/pom.xml):
+A Maven shade configuration suitable for this purpose is provided in the example standalone sample project [pom.xml file](https://github.com/eclipse/deeplearning4j-examples/blob/master/standalone-sample-project/pom.xml):
 ```
 <build>
     <plugins>
@@ -191,7 +191,7 @@ If resources (i.e., the number of available GPU machines) are not constrained, i
 
 Assuming the master/driver is executing on a CPU machine, and the workers are executing on GPU machines, you can simply include both backends (i.e., both the ```nd4j-cuda-x.x``` and ```nd4j-native``` dependencies as described in the [uber-jar section](#uberjar)).
 
-When multiple backends are present on the classpath, by default the CUDA backend will be tried first. If this cannot be loaded, the CPU (nd4j-native) backend will be loaded second. Thus, if the driver does not have a GPU, it should fall back to using a CPU. However, this default behaviour can be changed by setting the ```BACKEND_PRIORITY_CPU``` or ```BACKEND_PRIORITY_GPU``` environment variables on the master/driver, as described [here](https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-common/src/main/java/org/nd4j/config/ND4JEnvironmentVars.java).
+When multiple backends are present on the classpath, by default the CUDA backend will be tried first. If this cannot be loaded, the CPU (nd4j-native) backend will be loaded second. Thus, if the driver does not have a GPU, it should fall back to using a CPU. However, this default behaviour can be changed by setting the ```BACKEND_PRIORITY_CPU``` or ```BACKEND_PRIORITY_GPU``` environment variables on the master/driver, as described [here](https://github.com/eclipse/deeplearning4j/blob/master/nd4j/nd4j-common/src/main/java/org/nd4j/config/ND4JEnvironmentVars.java).
 The exact process for setting environment variables may depend on the cluster manager - Spark standalone vs. YARN vs. Mesos. Please consult the documentation for each on how to set the environment variables for Spark jobs for the driver/master.
 
 <br><br>
@@ -324,9 +324,9 @@ Both too large thresholds and too small thresholds can result in sub-optimal per
 * Large thresholds mean infrequent communication - too infrequent and convergence can suffer
 * Small thresholds mean more frequent communication - but smaller changes are communicated at each step
 
-The encoding threshold to be used is controlled by the [ThresholdAlgorithm](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/optimize/solvers/accumulation/encoding/ThresholdAlgorithm.java). The specific implementation of the ThresholdAlgorithm determines what threshold should be used.
+The encoding threshold to be used is controlled by the [ThresholdAlgorithm](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/optimize/solvers/accumulation/encoding/ThresholdAlgorithm.java). The specific implementation of the ThresholdAlgorithm determines what threshold should be used.
 
-The default behaviour for DL4J is to use [AdaptiveThresholdAlgorithm](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/optimize/solvers/accumulation/encoding/threshold/AdaptiveThresholdAlgorithm.java) which tries to keep the sparsity ratio in a certain range.
+The default behaviour for DL4J is to use [AdaptiveThresholdAlgorithm](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/optimize/solvers/accumulation/encoding/threshold/AdaptiveThresholdAlgorithm.java) which tries to keep the sparsity ratio in a certain range.
 * The sparsity ratio is defined as numValues(encodedUpdate)/numParameters - 1.0 means fully dense (all values communicated), 0.0 means fully sparse (no values communicated)
 * Larger thresholds mean more sparse values (less network communication), and a smaller threshold means less sparse values (more network communication)
 * The AdaptiveThresholdAlgorithm tries to keep the sparsity ratio between 0.01 and 0.0001 by default. If the sparsity of the updates falls outside of this range, the threshold is either increased or decreased until it is within this range.
@@ -336,10 +336,10 @@ In practice, we have seen that this adaptive threshold process to work well.
 The built-in implementations for threshold algorithms include:
 
 * AdaptiveThresholdAlgorithm
-* [FixedThresholdAlgorithm](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/optimize/solvers/accumulation/encoding/threshold/FixedThresholdAlgorithm.java): a fixed, non-adaptive threshold using the specified encoding threshold.
-* [TargetSparsityThresholdAlgorithm](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/optimize/solvers/accumulation/encoding/threshold/TargetSparsityThresholdAlgorithm.java): an adaptive threshold algorithm that targets a specific sparsity, and increases or decreases the threshold to try to match the target.
+* [FixedThresholdAlgorithm](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/optimize/solvers/accumulation/encoding/threshold/FixedThresholdAlgorithm.java): a fixed, non-adaptive threshold using the specified encoding threshold.
+* [TargetSparsityThresholdAlgorithm](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/optimize/solvers/accumulation/encoding/threshold/TargetSparsityThresholdAlgorithm.java): an adaptive threshold algorithm that targets a specific sparsity, and increases or decreases the threshold to try to match the target.
 
-In addition, DL4J has a [ResidualPostProcessor](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/optimize/solvers/accumulation/encoding/ResidualPostProcessor.java) interface, with the default implementation being [ResidualClippingPostProcessor](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/optimize/solvers/accumulation/encoding/residual/ResidualClippingPostProcessor.java) which clips the residual vector to a maximum of 5x the current threshold, every 5 steps.
+In addition, DL4J has a [ResidualPostProcessor](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/optimize/solvers/accumulation/encoding/ResidualPostProcessor.java) interface, with the default implementation being [ResidualClippingPostProcessor](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/optimize/solvers/accumulation/encoding/residual/ResidualClippingPostProcessor.java) which clips the residual vector to a maximum of 5x the current threshold, every 5 steps.
 The motivation for this is that the "left over" parts of the updates (i.e., those parts not communicated) are store in the residual vector. If the updates are much larger than the threshold, we can have a phenomenon we have termed "residual explosion" - that is, the residual values can continue to grow to many times the threshold (hence would take many steps to communicate the gradient). The residual post processor is used to avoid this phenomenon.
 
 The threshold algorithm (and initial threshold) and the residual post processor can be set as follows:
diff --git a/docs/deeplearning4j-scaleout/templates/intro.md b/docs/deeplearning4j-scaleout/templates/intro.md
index 9bd7f9529..39e330663 100644
--- a/docs/deeplearning4j-scaleout/templates/intro.md
+++ b/docs/deeplearning4j-scaleout/templates/intro.md
@@ -30,7 +30,7 @@ You should use Spark when:
 2. You need more than single machine to train the network
 3. Your network is large to justify a distributed implementation
 
-For a single machine with multiple GPUs or multiple physical processors, users should consider using DL4J's Parallel-Wrapper implementation as shown in [this example](https://github.com/deeplearning4j/dl4j-examples/blob/master/dl4j-cuda-specific-examples/src/main/java/org/deeplearning4j/examples/multigpu/MultiGpuLenetMnistExample.java). ParallelWrapper allows for easy data parallel training of networks on a single machine with multiple cores. Spark has higher overheads compared to ParallelWrapper for single machine training.
+For a single machine with multiple GPUs or multiple physical processors, users should consider using DL4J's Parallel-Wrapper implementation as shown in [this example](https://github.com/eclipse/deeplearning4j-examples/blob/master/dl4j-cuda-specific-examples/src/main/java/org/deeplearning4j/examples/multigpu/MultiGpuLenetMnistExample.java). ParallelWrapper allows for easy data parallel training of networks on a single machine with multiple cores. Spark has higher overheads compared to ParallelWrapper for single machine training.
 
 Similarly, if you don't need Spark (smaller networks and/or datasets) - it is recommended to use single machine training, which is usually simpler to set up.
 
@@ -150,4 +150,4 @@ for (int i = 0; i < numEpochs; i++) {
 * [Deeplearning4j on Spark: How To Guides](deeplearning4j-scaleout-howto)
 * [Deeplearning4j on Spark: How To Build Data Pipelines](deeplearning4j-scaleout-data-howto)
 * [Deeplearning4j on Spark: API Reference](deeplearning4j-scaleout-apiref)
-* The [Deeplearning4j examples repo](https://github.com/deeplearning4j/dl4j-examples) contains a number of Spark examples that can be used by the user as reference.
+* The [Deeplearning4j examples repo](https://github.com/eclipse/deeplearning4j-examples) contains a number of Spark examples that can be used by the user as reference.
diff --git a/docs/deeplearning4j-scaleout/templates/parameter-server.md b/docs/deeplearning4j-scaleout/templates/parameter-server.md
index 8b8fc7aec..38e1b2da3 100644
--- a/docs/deeplearning4j-scaleout/templates/parameter-server.md
+++ b/docs/deeplearning4j-scaleout/templates/parameter-server.md
@@ -25,7 +25,7 @@ Here are a few more perks were added to original algorithm proposed by Nikko Str
  
 ![Two phases within the cluster](/images/guide/distributed.png)
 
-Note that using Spark entails overhead. In order to determine whether Spark will help you or not, consider using the [Performance Listener](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/optimize/listeners/PerformanceListener.java) and look at the millisecond iteration time.
+Note that using Spark entails overhead. In order to determine whether Spark will help you or not, consider using the [Performance Listener](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/optimize/listeners/PerformanceListener.java) and look at the millisecond iteration time.
 If it's <= 150ms, Spark may not be worth it.
 
 ## Setting up Your Cluster
@@ -74,7 +74,7 @@ For example:
 
 ### Example Configuration:
 
-Below is a snippet from an example project taken from [our examples repo on Github](https://github.com/deeplearning4j/dl4j-examples/blob/master/dl4j-spark-examples/dl4j-spark/src/main/java/org/deeplearning4j/mlp/MnistMLPDistributedExample.java)  
+Below is a snippet from an example project taken from [our examples repo on Github](https://github.com/eclipse/deeplearning4j-examples/blob/master/dl4j-spark-examples/dl4j-spark/src/main/java/org/deeplearning4j/mlp/MnistMLPDistributedExample.java)  
 
 ```
 SparkConf sparkConf = new SparkConf();
diff --git a/docs/deeplearning4j-scaleout/templates/technicalref.md b/docs/deeplearning4j-scaleout/templates/technicalref.md
index 96d020dec..99f78968e 100644
--- a/docs/deeplearning4j-scaleout/templates/technicalref.md
+++ b/docs/deeplearning4j-scaleout/templates/technicalref.md
@@ -50,9 +50,9 @@ The implementation allows the user to choose between two modes of network organi
 2. Two encoding schemes:
 	DL4J uses two encoding schemes, dynamically switching between the two depending on which will provide less network communication. Refer to the section on [encoding](#encoding) for more details.
 3. Quantization thresholds adjusted:
-	The quantization threshold is stepped up or down depending on the distribution of the updates after each iteration. This is done on each node independently to make sure that updates are indeed sparse. In practice, this is implemented via the [ThresholdAlgorithm](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/optimize/solvers/accumulation/encoding/ThresholdAlgorithm.java) interface and the [implementations](https://github.com/deeplearning4j/deeplearning4j/tree/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/optimize/solvers/accumulation/encoding/threshold) there-of.
+	The quantization threshold is stepped up or down depending on the distribution of the updates after each iteration. This is done on each node independently to make sure that updates are indeed sparse. In practice, this is implemented via the [ThresholdAlgorithm](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/optimize/solvers/accumulation/encoding/ThresholdAlgorithm.java) interface and the [implementations](https://github.com/eclipse/deeplearning4j/tree/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/optimize/solvers/accumulation/encoding/threshold) there-of.
 4. Residual clipping
-	As noted earlier, the "left over" parts of the updates (i.e., those parts not communicated) are store in the residual vector. If the updates are much larger than the threshold, we can have a phenomenon we have termed "residual explosion" - that is, the residual values can continue to grow to many times the threshold (hence would take many steps to communicate the gradient). To avoid this, DL4J has a [ResidualPostProcessor](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/optimize/solvers/accumulation/encoding/ResidualPostProcessor.java) interface, with the default implementation being [ResidualClippingPostProcessor](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/optimize/solvers/accumulation/encoding/residual/ResidualClippingPostProcessor.java) which clips the residual vector to a maximum of 5x the current threshold, every 5 steps.
+	As noted earlier, the "left over" parts of the updates (i.e., those parts not communicated) are store in the residual vector. If the updates are much larger than the threshold, we can have a phenomenon we have termed "residual explosion" - that is, the residual values can continue to grow to many times the threshold (hence would take many steps to communicate the gradient). To avoid this, DL4J has a [ResidualPostProcessor](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/optimize/solvers/accumulation/encoding/ResidualPostProcessor.java) interface, with the default implementation being [ResidualClippingPostProcessor](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/optimize/solvers/accumulation/encoding/residual/ResidualClippingPostProcessor.java) which clips the residual vector to a maximum of 5x the current threshold, every 5 steps.
 5. Local parallelism via ParallelWrapper: 
   This enables multi-CPU/GPU nodes to share information faster
 
diff --git a/docs/deeplearning4j-zoo/templates/overview.md b/docs/deeplearning4j-zoo/templates/overview.md
index a6bb55861..ed51ef92d 100644
--- a/docs/deeplearning4j-zoo/templates/overview.md
+++ b/docs/deeplearning4j-zoo/templates/overview.md
@@ -87,21 +87,21 @@ Note that for convolutional models, input shape information follows the NCHW con
 
 The model zoo comes with well-known image recognition configurations in the deep learning community. The zoo also includes an LSTM for text generation, and a simple CNN for general image recognition.
 
-You can find a complete list of models using this [deeplearning4j-zoo Github link](https://github.com/deeplearning4j/deeplearning4j/tree/master/deeplearning4j/deeplearning4j-zoo/src/main/java/org/deeplearning4j/zoo/model).
+You can find a complete list of models using this [deeplearning4j-zoo Github link](https://github.com/eclipse/deeplearning4j/tree/master/deeplearning4j/deeplearning4j-zoo/src/main/java/org/deeplearning4j/zoo/model).
 
 This includes ImageNet models such as VGG-16, ResNet-50, AlexNet, Inception-ResNet-v1, LeNet, and more. 
 
-* [AlexNet](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-zoo/src/main/java/org/deeplearning4j/zoo/model/AlexNet.java)	
-* [Darknet19](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-zoo/src/main/java/org/deeplearning4j/zoo/model/Darknet19.java)	
-* [FaceNetNN4Small2](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-zoo/src/main/java/org/deeplearning4j/zoo/model/FaceNetNN4Small2.java)	
-* [InceptionResNetV1](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-zoo/src/main/java/org/deeplearning4j/zoo/model/InceptionResNetV1.java)	
-* [LeNet](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-zoo/src/main/java/org/deeplearning4j/zoo/model/LeNet.java)
-* [ResNet50](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-zoo/src/main/java/org/deeplearning4j/zoo/model/ResNet50.java)
-* [SimpleCNN](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-zoo/src/main/java/org/deeplearning4j/zoo/model/SimpleCNN.java)
-* [TextGenerationLSTM](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-zoo/src/main/java/org/deeplearning4j/zoo/model/TextGenerationLSTM.java)
-* [TinyYOLO](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-zoo/src/main/java/org/deeplearning4j/zoo/model/TinyYOLO.java)
-* [VGG16](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-zoo/src/main/java/org/deeplearning4j/zoo/model/VGG16.java)	
-* [VGG19](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-zoo/src/main/java/org/deeplearning4j/zoo/model/VGG19.java)
+* [AlexNet](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-zoo/src/main/java/org/deeplearning4j/zoo/model/AlexNet.java)	
+* [Darknet19](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-zoo/src/main/java/org/deeplearning4j/zoo/model/Darknet19.java)	
+* [FaceNetNN4Small2](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-zoo/src/main/java/org/deeplearning4j/zoo/model/FaceNetNN4Small2.java)	
+* [InceptionResNetV1](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-zoo/src/main/java/org/deeplearning4j/zoo/model/InceptionResNetV1.java)	
+* [LeNet](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-zoo/src/main/java/org/deeplearning4j/zoo/model/LeNet.java)
+* [ResNet50](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-zoo/src/main/java/org/deeplearning4j/zoo/model/ResNet50.java)
+* [SimpleCNN](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-zoo/src/main/java/org/deeplearning4j/zoo/model/SimpleCNN.java)
+* [TextGenerationLSTM](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-zoo/src/main/java/org/deeplearning4j/zoo/model/TextGenerationLSTM.java)
+* [TinyYOLO](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-zoo/src/main/java/org/deeplearning4j/zoo/model/TinyYOLO.java)
+* [VGG16](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-zoo/src/main/java/org/deeplearning4j/zoo/model/VGG16.java)	
+* [VGG19](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-zoo/src/main/java/org/deeplearning4j/zoo/model/VGG19.java)
 
 ## Advanced usage
 
diff --git a/docs/deeplearning4j/templates/android-image-classification.md b/docs/deeplearning4j/templates/android-image-classification.md
index 1295a641b..a0239f834 100644
--- a/docs/deeplearning4j/templates/android-image-classification.md
+++ b/docs/deeplearning4j/templates/android-image-classification.md
@@ -392,4 +392,4 @@ The onPostExecute method will receive an INDArray which contains the neural netw
 
 This tutorial provides a basic framework for image recognition in an Android Application using a DL4J neural network. It illustrates how to load a pre-trained DL4J model from the raw resources file and how to test user generate input images against the model. The AsyncTask then returns the output to the main thread and updates the UI.
 
-The complete code for this example is available [here.](https://github.com/deeplearning4j/dl4j-examples/tree/master/android/DL4JImageRecognitionDemo)
+The complete code for this example is available [here.](https://github.com/eclipse/deeplearning4j-examples/tree/master/android/DL4JImageRecognitionDemo)
diff --git a/docs/deeplearning4j/templates/android-linear-classifier.md b/docs/deeplearning4j/templates/android-linear-classifier.md
index 8eb6632c3..57439e18c 100644
--- a/docs/deeplearning4j/templates/android-linear-classifier.md
+++ b/docs/deeplearning4j/templates/android-linear-classifier.md
@@ -275,7 +275,7 @@ Once the training of the neural network and the classification of the user measu
 Hopefully this tutorial has illustrated how the compatibility of DL4J with Android makes it easy to build, train, and evaluate neural networks on mobile devices. We used a simple UI to take input values from the measurement and then passed them as the *Params* in an AsyncTask. The processor intensive steps of data preparation, network layer building, model training, and evaluation of the user data were all performed in the doInBackground() method of the background thread, maintaining a stable and responsive device. Once completed, we passed the output INDArray as the AsyncTask *Results* to onPostExecute() where the UI was updated to demonstrate the classification results. 
 The limitations of processing power and battery life of mobile devices make training robust, multi-layer networks somewhat unfeasible. To address this limitation, we will next look at an example Android application that saves the trained model on the device for faster performance after an initial model training.
 
-The complete code for this example is available [here.](https://github.com/deeplearning4j/dl4j-examples/tree/master/android/DL4JIrisClassifierDemo)
+The complete code for this example is available [here.](https://github.com/eclipse/deeplearning4j-examples/tree/master/android/DL4JIrisClassifierDemo)
 
 
 
diff --git a/docs/deeplearning4j/templates/beginners.md b/docs/deeplearning4j/templates/beginners.md
index c5fce0a62..f7740516d 100644
--- a/docs/deeplearning4j/templates/beginners.md
+++ b/docs/deeplearning4j/templates/beginners.md
@@ -89,7 +89,7 @@ Once you have programming basics down, tackle Java, the world's most widely used
 
 ## Deeplearning4j
 
-With that under your belt, we recommend you approach Deeplearning4j through its [examples](https://github.com/deeplearning4j/dl4j-examples). 
+With that under your belt, we recommend you approach Deeplearning4j through its [examples](https://github.com/eclipse/deeplearning4j-examples). 
 
 * [Quickstart](./deeplearning4j-quickstart)
 
diff --git a/docs/deeplearning4j/templates/benchmark.md b/docs/deeplearning4j/templates/benchmark.md
index 93e30fda9..0dddd748c 100644
--- a/docs/deeplearning4j/templates/benchmark.md
+++ b/docs/deeplearning4j/templates/benchmark.md
@@ -274,7 +274,7 @@ Here's how the DatasetIterator is uniformly invoked for MNIST:
 
 You can optimize by using an asychronous loader in the background. Java can do real multi-threading. It can load data in the background while other threads take care of compute. So you load data into the GPU at the same time that compute is being run. The neural net trains even as you grab new data from memory.
 
-This is the [relevant code](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-scaleout/deeplearning4j-scaleout-parallelwrapper/src/main/java/org/deeplearning4j/parallelism/ParallelWrapper.java#L136), in particular the third line:
+This is the [relevant code](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-scaleout/deeplearning4j-scaleout-parallelwrapper/src/main/java/org/deeplearning4j/parallelism/ParallelWrapper.java#L136), in particular the third line:
 
         MultiDataSetIterator iterator;
         if (prefetchSize > 0 && source.asyncSupported()) {
@@ -301,11 +301,11 @@ But if your training pipeline is doing that every time, Deeplearning4j will seem
 
 One way is to pre-save the datasets, in a manner similar to the Python frameworks. (Pickles are pre-formatted data.) When you pre-save the dataset, you create a separate class.
 
-Here’s how you [pre-save datasets](https://github.com/deeplearning4j/dl4j-examples/blob/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/misc/presave/PreSave.java).
+Here’s how you [pre-save datasets](https://github.com/eclipse/deeplearning4j-examples/blob/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/misc/presave/PreSave.java).
 
 A `Recordreaderdatasetiterator` talks to Datavec and outputs datasets for DL4J. 
 
-Here’s how you [load a pre-saved dataset](https://github.com/deeplearning4j/dl4j-examples/blob/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/misc/presave/LoadPreSavedLenetMnistExample.java).
+Here’s how you [load a pre-saved dataset](https://github.com/eclipse/deeplearning4j-examples/blob/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/misc/presave/LoadPreSavedLenetMnistExample.java).
 
 Line 90 is where you see the asynchronous ETL. In this case, it's wrapping the pre-saved iterator, so you're taking advantage of both methods, with the asynch loading the pre-saved data in the background as the net trains. 
 
diff --git a/docs/deeplearning4j/templates/build-from-source.md b/docs/deeplearning4j/templates/build-from-source.md
index 19d3391e7..20a0243bb 100644
--- a/docs/deeplearning4j/templates/build-from-source.md
+++ b/docs/deeplearning4j/templates/build-from-source.md
@@ -16,10 +16,10 @@ For those developers and engineers who prefer to use the most up-to-date version
 
 Building locally requires that you build the entire Deeplearning4j stack which includes:
 
-- [libnd4j](https://github.com/deeplearning4j/libnd4j)
-- [nd4j](https://github.com/deeplearning4j/nd4j)
-- [datavec](https://github.com/deeplearning4j/datavec)
-- [deeplearning4j](https://github.com/deeplearning4j/deeplearning4j)
+- [libnd4j](https://github.com/eclipse/deeplearning4j/tree/master/libnd4j)
+- [nd4j](https://github.com/eclipse/deeplearning4j/tree/master/nd4j)
+- [datavec](https://github.com/eclipse/deeplearning4j/tree/master/datavec)
+- [deeplearning4j](https://github.com/eclipse/deeplearning4j)
 
 Note that Deeplearning4j is designed to work on most platforms (Windows, OS X, and Linux) and is also includes multiple "flavors" depending on the computing architecture you choose to utilize. This includes CPU (OpenBLAS, MKL, ATLAS) and GPU (CUDA). The DL4J stack also supports x86 and PowerPC architectures.
 
@@ -287,7 +287,7 @@ copy mkl_rt.dll libblas3.dll
 
 ### Build Script
 
-You can use the [build-dl4j-stack.sh](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/build-dl4j-stack.sh) script from the deeplearning4j repository to build the whole deeplearning4j stack from source: libndj4, ndj4, datavec, deeplearning4j. It clones the DL4J stack, builds each repository, and installs them locally to Maven. This script will work on both Linux and OS X platforms.
+You can use the [build-dl4j-stack.sh](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/build-dl4j-stack.sh) script from the deeplearning4j repository to build the whole deeplearning4j stack from source: libndj4, ndj4, datavec, deeplearning4j. It clones the DL4J stack, builds each repository, and installs them locally to Maven. This script will work on both Linux and OS X platforms.
 
 OK, now read the following section carefully. 
 
@@ -297,7 +297,7 @@ Use the build script below for CPU architectures:
 ./build-dl4j-stack.sh
 ```
 Make sure to read this if you are on OS X (ensure gcc 5.x is setup and you aren't using clang):
-https://github.com/deeplearning4j/deeplearning4j/issues/2668
+https://github.com/eclipse/deeplearning4j/issues/2668
 
 
 If you are using a GPU backend, use this instead:
@@ -306,7 +306,7 @@ If you are using a GPU backend, use this instead:
 ./build-dl4j-stack.sh -c cuda
 ```
 
-You can speed up your CUDA builds by using the `cc` flag as explained in the [libndj4 README](https://github.com/deeplearning4j/libnd4j).
+You can speed up your CUDA builds by using the `cc` flag as explained in the [libndj4 README](https://github.com/eclipse/deeplearning4j/tree/master/libnd4j).
 
 For Scala users, you can pass your binary version for Spark compatibility:
 
@@ -334,7 +334,7 @@ rm -rf datavec
 rm -rf deeplearning4j
 
 # compile libnd4j
-git clone https://github.com/deeplearning4j/libnd4j.git
+git clone https://github.com/eclipse/deeplearning4j.git
 cd libnd4j
 ./buildnativeoperations.sh
 # and/or when using GPU
@@ -344,7 +344,7 @@ export LIBND4J_HOME=`pwd`
 cd ..
 
 # build and install nd4j to maven locally
-git clone https://github.com/deeplearning4j/nd4j.git
+git clone https://github.com/eclipse/deeplearning4j.git
 cd nd4j
 # cross-build across Scala versions (recommended)
 bash buildmultiplescalaversions.sh clean install -DskipTests -Dmaven.javadoc.skip=true -pl '!:nd4j-cuda-9.0,!:nd4j-cuda-9.0-platform,!:nd4j-tests'
@@ -355,7 +355,7 @@ bash buildmultiplescalaversions.sh clean install -DskipTests -Dmaven.javadoc.ski
 cd ..
 
 # build and install datavec
-git clone https://github.com/deeplearning4j/datavec.git
+git clone https://github.com/eclipse/deeplearning4j.git
 cd datavec
 if [ "$SCALAV" == "" ]; then
   bash buildmultiplescalaversions.sh clean install -DskipTests -Dmaven.javadoc.skip=true
@@ -365,7 +365,7 @@ fi
 cd ..
 
 # build and install deeplearning4j
-git clone https://github.com/deeplearning4j/deeplearning4j.git
+git clone https://github.com/eclipse/deeplearning4j.git
 cd deeplearning4j
 # cross-build across Scala versions (recommended)
 ./buildmultiplescalaversions.sh clean install -DskipTests -Dmaven.javadoc.skip=true
@@ -379,7 +379,7 @@ cd ..
 
 ## Using Local Dependencies
 
-Once you've installed the DL4J stack to your local maven repository, you can now include it in your build tool's dependencies. Follow the typical [Getting Started](http://deeplearning4j.org/gettingstarted) instructions for Deeplearning4j, and appropriately replace versions with the SNAPSHOT version currently on the [master POM](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/pom.xml).
+Once you've installed the DL4J stack to your local maven repository, you can now include it in your build tool's dependencies. Follow the typical [Getting Started](http://deeplearning4j.org/gettingstarted) instructions for Deeplearning4j, and appropriately replace versions with the SNAPSHOT version currently on the [master POM](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/pom.xml).
 
 Note that some build tools such as Gradle and SBT don't properly pull in platform-specific binaries. You can follow instructions [here](http://nd4j.org/dependencies.html) for setting up your favorite build tool.
 
diff --git a/docs/deeplearning4j/templates/cheat-sheet.md b/docs/deeplearning4j/templates/cheat-sheet.md
index 703fd193f..3437ffa0f 100644
--- a/docs/deeplearning4j/templates/cheat-sheet.md
+++ b/docs/deeplearning4j/templates/cheat-sheet.md
@@ -53,85 +53,85 @@ Deeplearning4j (and related projects) have a lot of functionality. The goal of t
 
 ### <a name="layers-ff">Feed-Forward Layers</a>
 
-* **DenseLayer** - ([Source](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/layers/feedforward/dense/DenseLayer.java)) - A simple/standard fully-connected layer
-* **EmbeddingLayer** - ([Source](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/layers/feedforward/embedding/EmbeddingLayer.java)) - Takes positive integer indexes as input, outputs vectors. Only usable as first layer in a model. Mathematically equivalent (when bias is enabled) to DenseLayer with one-hot input, but more efficient. See also: EmbeddingSequenceLayer.
+* **DenseLayer** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/layers/feedforward/dense/DenseLayer.java)) - A simple/standard fully-connected layer
+* **EmbeddingLayer** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/layers/feedforward/embedding/EmbeddingLayer.java)) - Takes positive integer indexes as input, outputs vectors. Only usable as first layer in a model. Mathematically equivalent (when bias is enabled) to DenseLayer with one-hot input, but more efficient. See also: EmbeddingSequenceLayer.
 
 #### <a name="layers-out">Output Layers</a>
 
 Output layers: usable only as the last layer in a network. Loss functions are set here.
 
-* **OutputLayer** - ([Source](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/layers/OutputLayer.java)) - Output layer for standard classification/regression in MLPs/CNNs. Has a fully connected DenseLayer built in. 2d input/output (i.e., row vector per example).
-* **LossLayer** - ([Source](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/layers/LossLayer.java)) - Output layer without parameters - only loss function and activation function. 2d input/output (i.e., row vector per example). Unlike Outputlayer, restricted to nIn = nOut.
-* **RnnOutputLayer** - ([Source](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/layers/RnnOutputLayer.java)) - Output layer for recurrent neural networks. 3d (time series) input and output. Has time distributed fully connected layer built in.
-* **RnnLossLayer** - ([Source](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/layers/RnnLossLayer.java)) - The 'no parameter' version of RnnOutputLayer. 3d (time series) input and output.
-* **CnnLossLayer** - ([Source](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/layers/CnnLossLayer.java)) - Used with CNNs, where a prediction must be made at each spatial location of the output (for example: segmentation or denoising). No parameters, 4d input/output with shape [minibatch, depth, height, width]. When using softmax, this is applied depthwise at each spatial location.
-* **Cnn3DLossLayer** - ([Source](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/layers/Cnn3DLossLayer.java)) - used with 3D CNNs, where a preduction must be made at each spatial location (x/y/z) of the output. Layer has no parameters, 5d data in either NCDHW or NDHWC ("channels first" or "channels last") format (configurable). Supports masking. When using Softmax, this is applied along channels at each spatial location.
-* **Yolo2OutputLayer** - ([Source](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/layers/objdetect/Yolo2OutputLayer.java)) - Implentation of the YOLO 2 model for object detection in images
-* **CenterLossOutputLayer** - ([Source](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/layers/CenterLossOutputLayer.java)) - A version of OutputLayer that also attempts to minimize the intra-class distance of examples' activations - i.e., "If example x is in class Y, ensure that embedding(x) is close to average(embedding(y)) for all examples y in Y"
+* **OutputLayer** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/layers/OutputLayer.java)) - Output layer for standard classification/regression in MLPs/CNNs. Has a fully connected DenseLayer built in. 2d input/output (i.e., row vector per example).
+* **LossLayer** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/layers/LossLayer.java)) - Output layer without parameters - only loss function and activation function. 2d input/output (i.e., row vector per example). Unlike Outputlayer, restricted to nIn = nOut.
+* **RnnOutputLayer** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/layers/RnnOutputLayer.java)) - Output layer for recurrent neural networks. 3d (time series) input and output. Has time distributed fully connected layer built in.
+* **RnnLossLayer** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/layers/RnnLossLayer.java)) - The 'no parameter' version of RnnOutputLayer. 3d (time series) input and output.
+* **CnnLossLayer** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/layers/CnnLossLayer.java)) - Used with CNNs, where a prediction must be made at each spatial location of the output (for example: segmentation or denoising). No parameters, 4d input/output with shape [minibatch, depth, height, width]. When using softmax, this is applied depthwise at each spatial location.
+* **Cnn3DLossLayer** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/layers/Cnn3DLossLayer.java)) - used with 3D CNNs, where a preduction must be made at each spatial location (x/y/z) of the output. Layer has no parameters, 5d data in either NCDHW or NDHWC ("channels first" or "channels last") format (configurable). Supports masking. When using Softmax, this is applied along channels at each spatial location.
+* **Yolo2OutputLayer** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/layers/objdetect/Yolo2OutputLayer.java)) - Implentation of the YOLO 2 model for object detection in images
+* **CenterLossOutputLayer** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/layers/CenterLossOutputLayer.java)) - A version of OutputLayer that also attempts to minimize the intra-class distance of examples' activations - i.e., "If example x is in class Y, ensure that embedding(x) is close to average(embedding(y)) for all examples y in Y"
 
 
 #### <a name="layers-conv">Convolutional Layers</a>
 
-* **ConvolutionLayer** / Convolution2D - ([Source](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/layers/ConvolutionLayer.java)) - Standard 2d convolutional neural network layer. Inputs and outputs have 4 dimensions with shape [minibatch,depthIn,heightIn,widthIn] and [minibatch,depthOut,heightOut,widthOut] respectively.
-* **Convolution1DLayer** / Convolution1D - ([Source](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/layers/Convolution1DLayer.java)) - Standard 1d convolution layer
-* **Convolution3DLayer** / Convolution3D - ([Source](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/layers/Convolution3D.java)) - Standard 3D convolution layer. Supports both NDHWC ("channels last") and NCDHW ("channels first") activations format.
-* **Deconvolution2DLayer** - ([Source](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/layers/convolution/Deconvolution2DLayer.java)) - also known as transpose or fractionally strided convolutions. Can be considered a "reversed" ConvolutionLayer; output size is generally larger than the input, whilst maintaining the spatial connection structure.
-* **SeparableConvolution2DLayer** - ([Source](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/layers/convolution/SeparableConvolution2DLayer.java)) - depthwise separable convolution layer
-* **SubsamplingLayer** - ([Source](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/layers/convolution/subsampling/SubsamplingLayer.java)) - Implements standard 2d spatial pooling for CNNs - with max, average and p-norm pooling available.
-* **Subsampling1DLayer** - ([Source](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/layers/Subsampling1DLayer.java)) - 1D version of the subsampling layer.
-* **Upsampling2D** - ([Source](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/layers/convolution/upsampling/Upsampling2D.java)) - Upscale CNN activations by repeating the row/column values
-* **Upsampling1D** - ([Source](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/layers/convolution/upsampling/Upsampling1D.java)) - 1D version of the upsampling layer
-* **Cropping2D** - ([Source](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/layers/convolutional/Cropping2D.java)) - Cropping layer for 2D convolutional neural networks
-* **DepthwiseConvolution2D** ([Source](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/layers/DepthwiseConvolution2D.java))- 2d depthwise convolution layer
-* **ZeroPaddingLayer** - ([Source](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/layers/convolution/ZeroPaddingLayer.java)) - Very simple layer that adds the specified amount of zero padding to edges of the 4d input activations.
-* **ZeroPadding1DLayer** - ([Source](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/layers/convolution/ZeroPadding1DLayer.java)) - 1D version of ZeroPaddingLayer
-* **SpaceToDepth** - ([Source](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/layers/SpaceToDepthLayer.java)) - This operation takes 4D array in, and moves data from spatial dimensions (HW) to channels (C) for given blockSize
-* **SpaceToBatch** - ([Source](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/layers/SpaceToBatchLayer.java)) - Transforms data from a tensor from 2 spatial dimensions into batch dimension according to the "blocks" specified
+* **ConvolutionLayer** / Convolution2D - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/layers/ConvolutionLayer.java)) - Standard 2d convolutional neural network layer. Inputs and outputs have 4 dimensions with shape [minibatch,depthIn,heightIn,widthIn] and [minibatch,depthOut,heightOut,widthOut] respectively.
+* **Convolution1DLayer** / Convolution1D - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/layers/Convolution1DLayer.java)) - Standard 1d convolution layer
+* **Convolution3DLayer** / Convolution3D - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/layers/Convolution3D.java)) - Standard 3D convolution layer. Supports both NDHWC ("channels last") and NCDHW ("channels first") activations format.
+* **Deconvolution2DLayer** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/layers/convolution/Deconvolution2DLayer.java)) - also known as transpose or fractionally strided convolutions. Can be considered a "reversed" ConvolutionLayer; output size is generally larger than the input, whilst maintaining the spatial connection structure.
+* **SeparableConvolution2DLayer** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/layers/convolution/SeparableConvolution2DLayer.java)) - depthwise separable convolution layer
+* **SubsamplingLayer** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/layers/convolution/subsampling/SubsamplingLayer.java)) - Implements standard 2d spatial pooling for CNNs - with max, average and p-norm pooling available.
+* **Subsampling1DLayer** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/layers/Subsampling1DLayer.java)) - 1D version of the subsampling layer.
+* **Upsampling2D** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/layers/convolution/upsampling/Upsampling2D.java)) - Upscale CNN activations by repeating the row/column values
+* **Upsampling1D** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/layers/convolution/upsampling/Upsampling1D.java)) - 1D version of the upsampling layer
+* **Cropping2D** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/layers/convolutional/Cropping2D.java)) - Cropping layer for 2D convolutional neural networks
+* **DepthwiseConvolution2D** ([Source](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/layers/DepthwiseConvolution2D.java))- 2d depthwise convolution layer
+* **ZeroPaddingLayer** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/layers/convolution/ZeroPaddingLayer.java)) - Very simple layer that adds the specified amount of zero padding to edges of the 4d input activations.
+* **ZeroPadding1DLayer** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/layers/convolution/ZeroPadding1DLayer.java)) - 1D version of ZeroPaddingLayer
+* **SpaceToDepth** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/layers/SpaceToDepthLayer.java)) - This operation takes 4D array in, and moves data from spatial dimensions (HW) to channels (C) for given blockSize
+* **SpaceToBatch** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/layers/SpaceToBatchLayer.java)) - Transforms data from a tensor from 2 spatial dimensions into batch dimension according to the "blocks" specified
 
 
 #### <a name="layers-rnn">Recurrent Layers</a>
 
-* **LSTM** - ([Source](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/layers/LSTM.java)) - LSTM RNN without peephole connections. Supports CuDNN.
-* **GravesLSTM** - ([Source](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/layers/GravesLSTM.java)) - LSTM RNN with peephole connections. Does *not* support CuDNN (thus for GPUs, LSTM should be used in preference).
-* **GravesBidirectionalLSTM** - ([Source](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/layers/GravesBidirectionalLSTM.java)) - A bidirectional LSTM implementation with peephole connections. Equivalent to Bidirectional(ADD, GravesLSTM). Due to addition of Bidirecitonal wrapper (below), has been deprecated on master.
-* **Bidirectional** - ([Source](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/layers/recurrent/Bidirectional.java)) - A 'wrapper' layer - converts any standard uni-directional RNN into a bidirectional RNN (doubles number of params - forward/backward nets have independent parameters). Activations from forward/backward nets may be either added, multiplied, averaged or concatenated.
-* **SimpleRnn** - ([Source](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/layers/recurrent/SimpleRnn.java)) - A standard/'vanilla' RNN layer. Usually not effective in practice with long time series dependencies - LSTM is generally preferred.
-* **LastTimeStep** - ([Source](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/layers/recurrent/LastTimeStep.java)) - A 'wrapper' layer - extracts out the last time step of the (non-bidirectional) RNN layer it wraps. 3d input with shape [minibatch, size, timeSeriesLength], 2d output with shape [minibatch, size].
-* EmbeddingSequenceLayer: ([Source](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/layers/EmbeddingSequenceLayer.java)) - A version of EmbeddingLayer that expects fixed-length number (inputLength) of integers/indices per example as input, ranged from 0 to numClasses - 1. This input thus has shape [numExamples, inputLength] or shape [numExamples, 1, inputLength]. The output of this layer is 3D (sequence/time series), namely of shape [numExamples, nOut, inputLength]. Can only be used as the first layer for a network.
+* **LSTM** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/layers/LSTM.java)) - LSTM RNN without peephole connections. Supports CuDNN.
+* **GravesLSTM** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/layers/GravesLSTM.java)) - LSTM RNN with peephole connections. Does *not* support CuDNN (thus for GPUs, LSTM should be used in preference).
+* **GravesBidirectionalLSTM** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/layers/GravesBidirectionalLSTM.java)) - A bidirectional LSTM implementation with peephole connections. Equivalent to Bidirectional(ADD, GravesLSTM). Due to addition of Bidirecitonal wrapper (below), has been deprecated on master.
+* **Bidirectional** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/layers/recurrent/Bidirectional.java)) - A 'wrapper' layer - converts any standard uni-directional RNN into a bidirectional RNN (doubles number of params - forward/backward nets have independent parameters). Activations from forward/backward nets may be either added, multiplied, averaged or concatenated.
+* **SimpleRnn** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/layers/recurrent/SimpleRnn.java)) - A standard/'vanilla' RNN layer. Usually not effective in practice with long time series dependencies - LSTM is generally preferred.
+* **LastTimeStep** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/layers/recurrent/LastTimeStep.java)) - A 'wrapper' layer - extracts out the last time step of the (non-bidirectional) RNN layer it wraps. 3d input with shape [minibatch, size, timeSeriesLength], 2d output with shape [minibatch, size].
+* EmbeddingSequenceLayer: ([Source](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/layers/EmbeddingSequenceLayer.java)) - A version of EmbeddingLayer that expects fixed-length number (inputLength) of integers/indices per example as input, ranged from 0 to numClasses - 1. This input thus has shape [numExamples, inputLength] or shape [numExamples, 1, inputLength]. The output of this layer is 3D (sequence/time series), namely of shape [numExamples, nOut, inputLength]. Can only be used as the first layer for a network.
 
 
 #### <a name="layers-unsupervised">Unsupervised Layers</a>
 
-* **VariationalAutoencoder** - ([Source](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/layers/variational/VariationalAutoencoder.java)) - A variational autoencoder implementation with MLP/dense layers for the encoder and decoder. Supports multiple different types of [reconstruction distributions](https://github.com/deeplearning4j/deeplearning4j/tree/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/layers/variational)
-* **AutoEncoder** - ([Source](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/layers/AutoEncoder.java)) - Standard denoising autoencoder layer
+* **VariationalAutoencoder** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/layers/variational/VariationalAutoencoder.java)) - A variational autoencoder implementation with MLP/dense layers for the encoder and decoder. Supports multiple different types of [reconstruction distributions](https://github.com/eclipse/deeplearning4j/tree/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/layers/variational)
+* **AutoEncoder** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/layers/AutoEncoder.java)) - Standard denoising autoencoder layer
 
 #### <a name="layers-other">Other Layers</a>
 
-* **GlobalPoolingLayer** - ([Source](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/layers/GlobalPoolingLayer.java)) - Implements both pooling over time (for RNNs/time series - input size [minibatch, size, timeSeriesLength], out [minibatch, size]) and global spatial pooling (for CNNs - input size [minibatch, depth, h, w], out [minibatch, depth]). Available pooling modes: sum, average, max and p-norm.
-* **ActivationLayer** - ([Source](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/layers/ActivationLayer.java)) - Applies an activation function (only) to the input activations. Note that most DL4J layers have activation functions built in as a config option.
-* **DropoutLayer** - ([Source](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/layers/DropoutLayer.java)) - Implements dropout as a separate/single layer. Note that most DL4J layers have a "built-in" dropout configuration option.
-* **BatchNormalization** - ([Source](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/layers/BatchNormalization.java)) - Batch normalization for 2d (feedforward), 3d (time series) or 4d (CNN) activations. For time series, parameter sharing across time; for CNNs, parameter sharing across spatial locations (but not depth).
-* **LocalResponseNormalization** - ([Source](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/layers/LocalResponseNormalization.java)) - Local response normalization layer for CNNs. Not frequently used in modern CNN architectures.
-* **FrozenLayer** - ([Source](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/layers/misc/FrozenLayer.java)) - Usually not used directly by users - added as part of transfer learning, to freeze a layer's parameters such that they don't change during further training.
-* **LocallyConnected2D** - ([Source](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/layers/LocallyConnected2D.java)) - a 2d locally connected layer, assumes input is 4d data in NCHW ("channels first") format.
-* **LocallyConected1D** - ([Source](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/layers/LocallyConnected1D.java)) - a 1d locally connected layer, assumes input is 3d data in NCW ([minibatch, size, sequenceLength]) format
+* **GlobalPoolingLayer** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/layers/GlobalPoolingLayer.java)) - Implements both pooling over time (for RNNs/time series - input size [minibatch, size, timeSeriesLength], out [minibatch, size]) and global spatial pooling (for CNNs - input size [minibatch, depth, h, w], out [minibatch, depth]). Available pooling modes: sum, average, max and p-norm.
+* **ActivationLayer** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/layers/ActivationLayer.java)) - Applies an activation function (only) to the input activations. Note that most DL4J layers have activation functions built in as a config option.
+* **DropoutLayer** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/layers/DropoutLayer.java)) - Implements dropout as a separate/single layer. Note that most DL4J layers have a "built-in" dropout configuration option.
+* **BatchNormalization** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/layers/BatchNormalization.java)) - Batch normalization for 2d (feedforward), 3d (time series) or 4d (CNN) activations. For time series, parameter sharing across time; for CNNs, parameter sharing across spatial locations (but not depth).
+* **LocalResponseNormalization** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/layers/LocalResponseNormalization.java)) - Local response normalization layer for CNNs. Not frequently used in modern CNN architectures.
+* **FrozenLayer** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/layers/misc/FrozenLayer.java)) - Usually not used directly by users - added as part of transfer learning, to freeze a layer's parameters such that they don't change during further training.
+* **LocallyConnected2D** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/layers/LocallyConnected2D.java)) - a 2d locally connected layer, assumes input is 4d data in NCHW ("channels first") format.
+* **LocallyConected1D** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/layers/LocallyConnected1D.java)) - a 1d locally connected layer, assumes input is 3d data in NCW ([minibatch, size, sequenceLength]) format
 
 
 #### <a name="layers-vertices">Graph Vertices</a>
 
 Graph vertex: use with ComputationGraph. Similar to layers, vertices usually don't have any parameters, and may support multiple inputs.
 
-* **ElementWiseVertex** - ([Source](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/graph/ElementWiseVertex.java)) - Performs an element-wise operation on the inputs - add, subtract, product, average, max
-* **L2NormalizeVertex** - ([Source](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/graph/L2NormalizeVertex.java)) - normalizes the input activations by dividing by the L2 norm for each example. i.e., out <- out / l2Norm(out)
-* **L2Vertex** - ([Source](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/graph/L2Vertex.java)) - calculates the L2 distance between the two input arrays, for each example separately. Output is a single value, for each input value.
-* **MergeVertex** - ([Source](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/graph/L2Vertex.java)) - merge the input activations along dimension 1, to make a larger output array. For CNNs, this implements merging along the depth/channels dimension
-* **PreprocessorVertex** - ([Source](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/graph/PreprocessorVertex.java)) - a simple GraphVertex that contains an InputPreProcessor only
-*  **ReshapeVertex** - ([Source](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/graph/ReshapeVertex.java)) - Performs arbitrary activation array reshaping. The preprocessors in the next section should usually be preferred.
-* **ScaleVertex** - ([Source](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/graph/ScaleVertex.java)) - implements simple multiplicative scaling of the inputs - i.e., out = scalar * input
-* **ShiftVertex** - ([Source](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/graph/ShiftVertex.java)) - implements simple scalar element-wise addition on the inputs - i.e., out = input + scalar
-* **StackVertex** - ([Source](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/graph/StackVertex.java)) - used to stack all inputs along the minibatch dimension. Analogous to MergeVertex, but along dimension 0 (minibatch) instead of dimension 1 (nOut/channels)
-* **SubsetVertex** - ([Source](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/graph/SubsetVertex.java)) - used to get a contiguous subset of the input activations along dimension 1. For example, two SubsetVertex instances could be used to split the activations from an input array into two separate activations. Essentially the opposite of MergeVertex.
-*  **UnstackVertex** - ([Source](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/graph/UnstackVertex.java)) - similar to SubsetVertex, but along dimension 0 (minibatch) instead of dimension 1 (nOut/channels). Opposite of StackVertex
+* **ElementWiseVertex** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/graph/ElementWiseVertex.java)) - Performs an element-wise operation on the inputs - add, subtract, product, average, max
+* **L2NormalizeVertex** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/graph/L2NormalizeVertex.java)) - normalizes the input activations by dividing by the L2 norm for each example. i.e., out <- out / l2Norm(out)
+* **L2Vertex** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/graph/L2Vertex.java)) - calculates the L2 distance between the two input arrays, for each example separately. Output is a single value, for each input value.
+* **MergeVertex** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/graph/L2Vertex.java)) - merge the input activations along dimension 1, to make a larger output array. For CNNs, this implements merging along the depth/channels dimension
+* **PreprocessorVertex** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/graph/PreprocessorVertex.java)) - a simple GraphVertex that contains an InputPreProcessor only
+*  **ReshapeVertex** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/graph/ReshapeVertex.java)) - Performs arbitrary activation array reshaping. The preprocessors in the next section should usually be preferred.
+* **ScaleVertex** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/graph/ScaleVertex.java)) - implements simple multiplicative scaling of the inputs - i.e., out = scalar * input
+* **ShiftVertex** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/graph/ShiftVertex.java)) - implements simple scalar element-wise addition on the inputs - i.e., out = input + scalar
+* **StackVertex** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/graph/StackVertex.java)) - used to stack all inputs along the minibatch dimension. Analogous to MergeVertex, but along dimension 0 (minibatch) instead of dimension 1 (nOut/channels)
+* **SubsetVertex** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/graph/SubsetVertex.java)) - used to get a contiguous subset of the input activations along dimension 1. For example, two SubsetVertex instances could be used to split the activations from an input array into two separate activations. Essentially the opposite of MergeVertex.
+*  **UnstackVertex** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/graph/UnstackVertex.java)) - similar to SubsetVertex, but along dimension 0 (minibatch) instead of dimension 1 (nOut/channels). Opposite of StackVertex
 
 
 
@@ -141,13 +141,13 @@ An InputPreProcessor is a simple class/interface that operates on the input to a
 
 Note that in many cases (such as the XtoYPreProcessor classes), users won't need to (and shouldn't) add these manually, and can instead just use ```.setInputType(InputType.feedForward(10))``` or similar, which whill infer and add the preprocessors as required.
 
-* **CnnToFeedForwardPreProcessor** - ([Source](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/preprocessor/CnnToFeedForwardPreProcessor.java)) - handles the activation reshaping necessary to transition from a CNN layer (ConvolutionLayer, SubsamplingLayer, etc) to DenseLayer/OutputLayer etc.
-* **CnnToRnnPreProcessor** - ([Source](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/preprocessor/CnnToRnnPreProcessor.java)) - handles reshaping necessary to transition from a (effectively, time distributed) CNN layer to a RNN layer.
-* **ComposableInputPreProcessor** - ([Source](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/preprocessor/ComposableInputPreProcessor.java)) - simple class that allows multiple preprocessors to be chained + used on a single layer
-* **FeedForwardToCnnPreProcessor** - ([Source](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/preprocessor/FeedForwardToCnnPreProcessor.java)) - handles activation reshaping to transition from a row vector (per example) to a CNN layer. Note that this transition/preprocessor only makes sense if the activations are actually CNN activations, but have been 'flattened' to a row vector.
-* **FeedForwardToRnnPreProcessor** - ([Source](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/preprocessor/FeedForwardToRnnPreProcessor.java)) - handles transition from a (time distributed) feed-forward layer to a RNN layer
-* **RnnToCnnPreProcessor** - ([Source](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/preprocessor/RnnToCnnPreProcessor.java)) - handles transition from a sequence of CNN activations with shape ```[minibatch, depth*height*width, timeSeriesLength]``` to time-distributed ```[numExamples*timeSeriesLength, numChannels, inputWidth, inputHeight]``` format
-* **RnnToFeedForwardPreProcessor** - ([Source](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/preprocessor/RnnToFeedForwardPreProcessor.java)) - handles transition from time series activations (shape ```[minibatch,size,timeSeriesLength]```) to time-distributed feed-forward (shape ```[minibatch*tsLength,size]```) activations.
+* **CnnToFeedForwardPreProcessor** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/preprocessor/CnnToFeedForwardPreProcessor.java)) - handles the activation reshaping necessary to transition from a CNN layer (ConvolutionLayer, SubsamplingLayer, etc) to DenseLayer/OutputLayer etc.
+* **CnnToRnnPreProcessor** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/preprocessor/CnnToRnnPreProcessor.java)) - handles reshaping necessary to transition from a (effectively, time distributed) CNN layer to a RNN layer.
+* **ComposableInputPreProcessor** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/preprocessor/ComposableInputPreProcessor.java)) - simple class that allows multiple preprocessors to be chained + used on a single layer
+* **FeedForwardToCnnPreProcessor** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/preprocessor/FeedForwardToCnnPreProcessor.java)) - handles activation reshaping to transition from a row vector (per example) to a CNN layer. Note that this transition/preprocessor only makes sense if the activations are actually CNN activations, but have been 'flattened' to a row vector.
+* **FeedForwardToRnnPreProcessor** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/preprocessor/FeedForwardToRnnPreProcessor.java)) - handles transition from a (time distributed) feed-forward layer to a RNN layer
+* **RnnToCnnPreProcessor** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/preprocessor/RnnToCnnPreProcessor.java)) - handles transition from a sequence of CNN activations with shape ```[minibatch, depth*height*width, timeSeriesLength]``` to time-distributed ```[numExamples*timeSeriesLength, numChannels, inputWidth, inputHeight]``` format
+* **RnnToFeedForwardPreProcessor** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/preprocessor/RnnToFeedForwardPreProcessor.java)) - handles transition from time series activations (shape ```[minibatch,size,timeSeriesLength]```) to time-distributed feed-forward (shape ```[minibatch*tsLength,size]```) activations.
 
 
 ### <a name="listeners">Iteration/Training Listeners</a>
@@ -158,13 +158,13 @@ TrainingListener: extends IterationListener. Has a number of additional methods
 Neither type (iteration/training) are called outside of training (i.e., during output or feed-forward methods)
 
 
-* **ScoreIterationListener** - ([Source](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/optimize/listeners/ScoreIterationListener.java), Javadoc) - Logs the loss function score every N training iterations
-* **PerformanceListener** - ([Source](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/optimize/listeners/PerformanceListener.java), Javadoc) - Logs performance (examples per sec, minibatches per sec, ETL time), and optionally score, every N training iterations.
-* **EvaluativeListener** - ([Source](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/optimize/listeners/EvaluativeListener.java), Javadoc) - Evaluates network performance on a test set every N iterations or epochs. Also has a system for callbacks, to (for example) save the evaluation results.
-* **CheckpointListener** - ([Source](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/optimize/listeners/checkpoint/CheckpointListener.java), Javadoc) - Save network checkpoints periodically - based on epochs, iterations or time (or some combination of all three).
-* **StatsListener** - ([Source](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-ui-parent/deeplearning4j-ui-model/src/main/java/org/deeplearning4j/ui/stats/StatsListener.java)) - Main listener for DL4J's web-based network training user interface. See [visualization page](https://deeplearning4j.org/visualization) for more details.
-* **CollectScoresIterationListener** - ([Source](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/optimize/listeners/CollectScoresIterationListener.java), Javadoc) - Similar to ScoreIterationListener, but stores scores internally in a list (for later retrieval) instead of logging scores
-* **TimeIterationListener** - ([Source](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/optimize/listeners/TimeIterationListener.java), Javadoc) - Attempts to estimate time until training completion, based on current speed and specified total number of iterations
+* **ScoreIterationListener** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/optimize/listeners/ScoreIterationListener.java), Javadoc) - Logs the loss function score every N training iterations
+* **PerformanceListener** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/optimize/listeners/PerformanceListener.java), Javadoc) - Logs performance (examples per sec, minibatches per sec, ETL time), and optionally score, every N training iterations.
+* **EvaluativeListener** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/optimize/listeners/EvaluativeListener.java), Javadoc) - Evaluates network performance on a test set every N iterations or epochs. Also has a system for callbacks, to (for example) save the evaluation results.
+* **CheckpointListener** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/optimize/listeners/checkpoint/CheckpointListener.java), Javadoc) - Save network checkpoints periodically - based on epochs, iterations or time (or some combination of all three).
+* **StatsListener** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-ui-parent/deeplearning4j-ui-model/src/main/java/org/deeplearning4j/ui/stats/StatsListener.java)) - Main listener for DL4J's web-based network training user interface. See [visualization page](https://deeplearning4j.org/visualization) for more details.
+* **CollectScoresIterationListener** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/optimize/listeners/CollectScoresIterationListener.java), Javadoc) - Similar to ScoreIterationListener, but stores scores internally in a list (for later retrieval) instead of logging scores
+* **TimeIterationListener** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/optimize/listeners/TimeIterationListener.java), Javadoc) - Attempts to estimate time until training completion, based on current speed and specified total number of iterations
 
 ### <a name="evaluation">Evaluation</a>
 
@@ -174,22 +174,22 @@ ND4J has a number of classes for evaluating the performance of a network, agains
 Note: in 1.0.0-beta3 (November 2018), all evaluation classes were moved from DL4J to ND4J; previously they were in DL4J.
 
 
-* **Evaluation** - ([Source](https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/evaluation/classification/Evaluation.java)) - Used for the evaluation of multi-class classifiers (assumes standard one-hot labels, and softmax probability distribution over N classes for predictions). Calculates a number of metrics - accuracy, precision, recall, F1, F-beta, Matthews correlation coefficient, confusion matrix. Optionally calculates top N accuracy, custom binary decision thresholds, and cost arrays (for non-binary case). Typically used for softmax + mcxent/negative-log-likelihood networks.
-* **EvaluationBinary** - ([Source](https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/evaluation/classification/EvaluationBinary.java)) - A multi-label binary version of the Evaluation class. Each network output is assumed to be a separate/independent binary class, with probability 0 to 1 independent of all other outputs. Typically used for sigmoid + binary cross entropy networks.
-* **EvaluationCalibration** - ([Source](https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/evaluation/classification/EvaluationCalibration.java)) - Used to evaluation the calibration of a binary or multi-class classifier. Produces reliability diagrams, residual plots, and histograms of probabilities. Export plots to HTML using [EvaluationTools](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-core/src/main/java/org/deeplearning4j/evaluation/EvaluationTools.java).exportevaluationCalibrationToHtmlFile method
-* **ROC** - ([Source](https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/evaluation/classification/ROC.java)) - Used for single output binary classifiers only - i.e., networks with nOut(1) + sigmoid, or nOut(2) + softmax. Supports 2 modes: thresholded (approximate) or exact (the default). Calculates area under ROC curve, area under precision-recall curve. Plot ROC and P-R curves to HTML using [EvaluationTools](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-core/src/main/java/org/deeplearning4j/evaluation/EvaluationTools.java)
-* **ROCBinary** - ([Source](https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/evaluation/classification/ROCBinary.java)) - a version of ROC that is used for multi-label binary networks (i.e., sigmoid + binary cross entropy), where each network output is assumed to be an independent binary variable.  
-* **ROCMultiClass** - ([Source](https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/evaluation/classification/ROCMultiClass.java)) - a version of ROC that is used for multi-class (non-binary) networks (i.e., softmax + mcxent/negative-log-likelihood networks). As ROC metrics are only defined for binary classification, this treats the multi-class output as a set of 'one-vs-all' binary classification problems.
-* **RegressionEvaluation** - ([Source](https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/evaluation/regression/RegressionEvaluation.java)) - An evaluation class used for regression models (including multi-output regression models). Reports metrics such as mean-squared error (MSE), mean-absolute error, etc for each output/column.
+* **Evaluation** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/evaluation/classification/Evaluation.java)) - Used for the evaluation of multi-class classifiers (assumes standard one-hot labels, and softmax probability distribution over N classes for predictions). Calculates a number of metrics - accuracy, precision, recall, F1, F-beta, Matthews correlation coefficient, confusion matrix. Optionally calculates top N accuracy, custom binary decision thresholds, and cost arrays (for non-binary case). Typically used for softmax + mcxent/negative-log-likelihood networks.
+* **EvaluationBinary** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/evaluation/classification/EvaluationBinary.java)) - A multi-label binary version of the Evaluation class. Each network output is assumed to be a separate/independent binary class, with probability 0 to 1 independent of all other outputs. Typically used for sigmoid + binary cross entropy networks.
+* **EvaluationCalibration** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/evaluation/classification/EvaluationCalibration.java)) - Used to evaluation the calibration of a binary or multi-class classifier. Produces reliability diagrams, residual plots, and histograms of probabilities. Export plots to HTML using [EvaluationTools](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-core/src/main/java/org/deeplearning4j/evaluation/EvaluationTools.java).exportevaluationCalibrationToHtmlFile method
+* **ROC** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/evaluation/classification/ROC.java)) - Used for single output binary classifiers only - i.e., networks with nOut(1) + sigmoid, or nOut(2) + softmax. Supports 2 modes: thresholded (approximate) or exact (the default). Calculates area under ROC curve, area under precision-recall curve. Plot ROC and P-R curves to HTML using [EvaluationTools](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-core/src/main/java/org/deeplearning4j/evaluation/EvaluationTools.java)
+* **ROCBinary** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/evaluation/classification/ROCBinary.java)) - a version of ROC that is used for multi-label binary networks (i.e., sigmoid + binary cross entropy), where each network output is assumed to be an independent binary variable.  
+* **ROCMultiClass** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/evaluation/classification/ROCMultiClass.java)) - a version of ROC that is used for multi-class (non-binary) networks (i.e., softmax + mcxent/negative-log-likelihood networks). As ROC metrics are only defined for binary classification, this treats the multi-class output as a set of 'one-vs-all' binary classification problems.
+* **RegressionEvaluation** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/evaluation/regression/RegressionEvaluation.java)) - An evaluation class used for regression models (including multi-output regression models). Reports metrics such as mean-squared error (MSE), mean-absolute error, etc for each output/column.
 
 
 ## <a name="saving">Network Saving and Loading</a>
 
 ```MultiLayerNetwork.save(File)``` and ```MultiLayerNetwork.load(File)``` methods can be used to save and load models. These use ModelSerializer internally. Similar save/load methods are also available for ComputationGraph.
 
-MultiLayerNetwork and ComputationGraph can be saved using the [ModelSerializer](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/util/ModelSerializer.java) class - and specifically the ```writeModel```, ```restoreMultiLayerNetwork``` and ```restoreComputationGraph``` methods.
+MultiLayerNetwork and ComputationGraph can be saved using the [ModelSerializer](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/util/ModelSerializer.java) class - and specifically the ```writeModel```, ```restoreMultiLayerNetwork``` and ```restoreComputationGraph``` methods.
 
-Examples: [Saving and loading network](https://github.com/deeplearning4j/dl4j-examples/tree/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/misc/modelsaving)
+Examples: [Saving and loading network](https://github.com/eclipse/deeplearning4j-examples/tree/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/misc/modelsaving)
 
 Networks can be trained further after saving and loading: however, be sure to load the 'updater' (i.e., the historical state for updaters like momentum, ). If no futher training is required, the updater state can be ommitted to save disk space and memory.
 
@@ -206,35 +206,35 @@ This section lists the various configuration options that Deeplearning4j support
 ### <a name="config-afn">Activation Functions</a>
 
 Activation functions can be defined in one of two ways:
-(a) By passing an [Activation](https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/activations/Activation.java) enumeration value to the configuration - for example, ```.activation(Activation.TANH)```
-(b) By passing an [IActivation](https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/activations/IActivation.java) instance - for example, ```.activation(new ActivationSigmoid())```
+(a) By passing an [Activation](https://github.com/eclipse/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/activations/Activation.java) enumeration value to the configuration - for example, ```.activation(Activation.TANH)```
+(b) By passing an [IActivation](https://github.com/eclipse/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/activations/IActivation.java) instance - for example, ```.activation(new ActivationSigmoid())```
 
-Note that Deeplearning4j supports custom activation functions, which can be defined by extending [BaseActivationFunction](https://github.com/deeplearning4j/deeplearning4j/tree/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/activations/impl)
+Note that Deeplearning4j supports custom activation functions, which can be defined by extending [BaseActivationFunction](https://github.com/eclipse/deeplearning4j/tree/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/activations/impl)
 
 List of supported activation functions:
-* **CUBE** - ([Source](https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/activations/impl/ActivationCube.java)) - ```f(x) = x^3```
-* **ELU** - ([Source](https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/activations/impl/ActivationELU.java)) - Exponential linear unit ([Reference](https://arxiv.org/abs/1511.07289))
-* **HARDSIGMOID** - ([Source](https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/activations/impl/ActivationHardSigmoid.java)) - a piecewise linear version of the standard sigmoid activation function. ```f(x) = min(1, max(0, 0.2*x + 0.5))```
-* **HARDTANH** - ([Source](https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/activations/impl/ActivationHardTanH.java)) - a piecewise linear version of the standard tanh activation function.
-* **IDENTITY** - ([Source](https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/activations/impl/ActivationIdentity.java)) - a 'no op' activation function: ```f(x) = x```
-* **LEAKYRELU** - ([Source](https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/activations/impl/ActivationLReLU.java)) - leaky rectified linear unit. ```f(x) = max(0, x) + alpha * min(0, x)``` with ```alpha=0.01``` by default.
-* **RATIONALTANH** - ([Source](https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/activations/impl/ActivationRationalTanh.java)) - ```tanh(y) ~ sgn(y) * { 1 - 1/(1+|y|+y^2+1.41645*y^4)}``` which approximates ```f(x) = 1.7159 * tanh(2x/3)```, but should be faster to execute. ([Reference](https://arxiv.org/abs/1508.01292))
-* **RELU** - ([Source](https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/activations/impl/ActivationReLU.java)) - standard rectified linear unit: ```f(x) = x``` if ```x>0``` or ```f(x) = 0``` otherwise
-* **RRELU** - ([Source](https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/activations/impl/ActivationRReLU.java)) - randomized rectified linear unit. Deterministic during test time. ([Reference](http://arxiv.org/abs/1505.00853))
-* **SIGMOID** - ([Source](https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/activations/impl/ActivationSigmoid.java)) - standard sigmoid activation function, ```f(x) = 1 / (1 + exp(-x))```
-* **SOFTMAX** - ([Source](https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/activations/impl/ActivationSoftmax.java)) - standard softmax activation function
-* **SOFTPLUS** - ([Source](https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/activations/impl/ActivationSoftPlus.java)) - ```f(x) = log(1+e^x)``` - shape is similar to a smooth version of the RELU activation function
-* **SOFTSIGN** - ([Source](https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/activations/impl/ActivationSoftSign.java)) - ```f(x) = x / (1+|x|)``` - somewhat similar in shape to the standard tanh activation function (faster to calculate).
-* **TANH** - ([Source](https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/activations/impl/ActivationTanH.java)) - standard tanh (hyperbolic tangent) activation function
-* **RECTIFIEDTANH** - ([Source](https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/activations/impl/ActivationRectifiedTanh.java)) - ```f(x) = max(0, tanh(x))```
-* **SELU** - ([Source](https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/activations/impl/ActivationSELU.java)) - scaled exponential linear unit - used with [self normalizing neural networks](https://arxiv.org/abs/1706.02515)
-* **SWISH** - ([Source](https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/activations/impl/ActivationSwish.java)) - Swish activation function, ```f(x) = x * sigmoid(x)``` ([Reference](https://arxiv.org/abs/1710.05941))
+* **CUBE** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/activations/impl/ActivationCube.java)) - ```f(x) = x^3```
+* **ELU** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/activations/impl/ActivationELU.java)) - Exponential linear unit ([Reference](https://arxiv.org/abs/1511.07289))
+* **HARDSIGMOID** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/activations/impl/ActivationHardSigmoid.java)) - a piecewise linear version of the standard sigmoid activation function. ```f(x) = min(1, max(0, 0.2*x + 0.5))```
+* **HARDTANH** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/activations/impl/ActivationHardTanH.java)) - a piecewise linear version of the standard tanh activation function.
+* **IDENTITY** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/activations/impl/ActivationIdentity.java)) - a 'no op' activation function: ```f(x) = x```
+* **LEAKYRELU** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/activations/impl/ActivationLReLU.java)) - leaky rectified linear unit. ```f(x) = max(0, x) + alpha * min(0, x)``` with ```alpha=0.01``` by default.
+* **RATIONALTANH** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/activations/impl/ActivationRationalTanh.java)) - ```tanh(y) ~ sgn(y) * { 1 - 1/(1+|y|+y^2+1.41645*y^4)}``` which approximates ```f(x) = 1.7159 * tanh(2x/3)```, but should be faster to execute. ([Reference](https://arxiv.org/abs/1508.01292))
+* **RELU** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/activations/impl/ActivationReLU.java)) - standard rectified linear unit: ```f(x) = x``` if ```x>0``` or ```f(x) = 0``` otherwise
+* **RRELU** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/activations/impl/ActivationRReLU.java)) - randomized rectified linear unit. Deterministic during test time. ([Reference](http://arxiv.org/abs/1505.00853))
+* **SIGMOID** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/activations/impl/ActivationSigmoid.java)) - standard sigmoid activation function, ```f(x) = 1 / (1 + exp(-x))```
+* **SOFTMAX** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/activations/impl/ActivationSoftmax.java)) - standard softmax activation function
+* **SOFTPLUS** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/activations/impl/ActivationSoftPlus.java)) - ```f(x) = log(1+e^x)``` - shape is similar to a smooth version of the RELU activation function
+* **SOFTSIGN** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/activations/impl/ActivationSoftSign.java)) - ```f(x) = x / (1+|x|)``` - somewhat similar in shape to the standard tanh activation function (faster to calculate).
+* **TANH** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/activations/impl/ActivationTanH.java)) - standard tanh (hyperbolic tangent) activation function
+* **RECTIFIEDTANH** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/activations/impl/ActivationRectifiedTanh.java)) - ```f(x) = max(0, tanh(x))```
+* **SELU** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/activations/impl/ActivationSELU.java)) - scaled exponential linear unit - used with [self normalizing neural networks](https://arxiv.org/abs/1706.02515)
+* **SWISH** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/activations/impl/ActivationSwish.java)) - Swish activation function, ```f(x) = x * sigmoid(x)``` ([Reference](https://arxiv.org/abs/1710.05941))
 
 ### <a name="config-init">Weight Initialization</a>
 
 Weight initialization refers to the method by which the initial parameters for a new network should be set.
 
-Weight initialization are usually defined using the [WeightInit](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/weights/WeightInit.java) enumeration.
+Weight initialization are usually defined using the [WeightInit](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/weights/WeightInit.java) enumeration.
 
 Custom weight initializations can be specified using ```.weightInit(WeightInit.DISTRIBUTION).dist(new NormalDistribution(0, 1))``` for example. As for master (but not 0.9.1 release) ```.weightInit(new NormalDistribution(0, 1))``` is also possible, which is equivalent to the previous approach.
 
@@ -267,15 +267,15 @@ An 'updater' in DL4J is a class that takes raw gradients and modifies them to be
 The [CS231n course notes](http://cs231n.github.io/neural-networks-3/#ada) have a good explanation of some of these updaters.
 
 Supported updaters in Deeplearning4j:
-* **AdaDelta** - ([Source](https://github.com/deeplearning4j/nd4j/blob/e92cae4626e2c9cf27d416136f1895b747a32cee/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/learning/config/AdaDelta.java)) - [Reference](https://arxiv.org/abs/1212.5701)
-* **AdaGrad** - ([Source](https://github.com/deeplearning4j/nd4j/blob/e92cae4626e2c9cf27d416136f1895b747a32cee/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/learning/config/AdaGrad.java)) - [Reference](http://jmlr.org/papers/v12/duchi11a.html)
-* **AdaMax** - ([Source](https://github.com/deeplearning4j/nd4j/blob/e92cae4626e2c9cf27d416136f1895b747a32cee/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/learning/config/AdaMax.java)) - A variant of the Adam updater - [Reference](http://arxiv.org/abs/1412.6980)
-* **Adam** - ([Source](https://github.com/deeplearning4j/nd4j/blob/e92cae4626e2c9cf27d416136f1895b747a32cee/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/learning/config/Adam.java))
-* **Nadam** - ([Source](https://github.com/deeplearning4j/nd4j/blob/e92cae4626e2c9cf27d416136f1895b747a32cee/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/learning/config/Nadam.java)) - A variant of the Adam updater, using the Nesterov mementum update rule - [Reference](https://arxiv.org/abs/1609.04747)
-* **Nesterovs** - ([Source](https://github.com/deeplearning4j/nd4j/blob/e92cae4626e2c9cf27d416136f1895b747a32cee/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/learning/config/Nesterovs.java)) - Nesterov momentum updater
-* **NoOp** - ([Source](https://github.com/deeplearning4j/nd4j/blob/e92cae4626e2c9cf27d416136f1895b747a32cee/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/learning/config/NoOp.java)) - A 'no operation' updater. That is, gradients are not modified at all by this updater. Mathematically equivalent to the SGD updater with a learning rate of 1.0
-* **RmsProp** - ([Source](https://github.com/deeplearning4j/nd4j/blob/e92cae4626e2c9cf27d416136f1895b747a32cee/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/learning/config/RmsProp.java)) - [Reference - slide 29](http://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf)
-* **Sgd** - ([Source](https://github.com/deeplearning4j/nd4j/blob/e92cae4626e2c9cf27d416136f1895b747a32cee/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/learning/config/Sgd.java)) - Standard stochastic gradient descent updater. This updater applies a learning rate only.
+* **AdaDelta** - ([Source](https://github.com/eclipse/deeplearning4j/tree/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/learning/config/AdaDelta.java)) - [Reference](https://arxiv.org/abs/1212.5701)
+* **AdaGrad** - ([Source](https://github.com/eclipse/deeplearning4j/tree/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/learning/config/AdaGrad.java)) - [Reference](http://jmlr.org/papers/v12/duchi11a.html)
+* **AdaMax** - ([Source](https://github.com/eclipse/deeplearning4j/tree/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/learning/config/AdaMax.java)) - A variant of the Adam updater - [Reference](http://arxiv.org/abs/1412.6980)
+* **Adam** - ([Source](https://github.com/eclipse/deeplearning4j/tree/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/learning/config/Adam.java))
+* **Nadam** - ([Source](https://github.com/eclipse/deeplearning4j/tree/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/learning/config/Nadam.java)) - A variant of the Adam updater, using the Nesterov mementum update rule - [Reference](https://arxiv.org/abs/1609.04747)
+* **Nesterovs** - ([Source](https://github.com/eclipse/deeplearning4j/tree/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/learning/config/Nesterovs.java)) - Nesterov momentum updater
+* **NoOp** - ([Source](https://github.com/eclipse/deeplearning4j/tree/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/learning/config/NoOp.java)) - A 'no operation' updater. That is, gradients are not modified at all by this updater. Mathematically equivalent to the SGD updater with a learning rate of 1.0
+* **RmsProp** - ([Source](https://github.com/eclipse/deeplearning4j/tree/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/learning/config/RmsProp.java)) - [Reference - slide 29](http://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf)
+* **Sgd** - ([Source](https://github.com/eclipse/deeplearning4j/tree/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/learning/config/Sgd.java)) - Standard stochastic gradient descent updater. This updater applies a learning rate only.
 
 
 ### <a name="config-schedules">Learning Rate Schedules</a>
@@ -287,12 +287,12 @@ You can plot/inspect the learning rate that will be used at any point by calling
 
 Available schedules:
 
-* **ExponentialSchedule** - ([Source](https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/schedule/ExponentialSchedule.java)) - Implements ```value(i) = initialValue * gamma^i```
-* **InverseSchedule** - ([Source](https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/schedule/InverseSchedule.java)) - Implements ```value(i) = initialValue * (1 + gamma * i)^(-power)```
-* **MapSchedule** - ([Source](https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/schedule/MapSchedule.java)) - Learning rate schedule based on a user-provided map. Note that the provided map must have a value for iteration/epoch 0. Has a builder class to conveniently define a schedule.
-* **PolySchedule** - ([Source](https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/schedule/PolySchedule.java)) - Implements ```value(i) = initialValue * (1 + i/maxIter)^(-power)```
-* **SigmoidSchedule** - ([Source](https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/schedule/SigmoidSchedule.java)) - Implements ```value(i) = initialValue * 1.0 / (1 + exp(-gamma * (iter - stepSize)))```
-* **StepSchedule** - ([Source](https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/schedule/StepSchedule.java)) - Implements ```value(i) = initialValue * gamma^( floor(iter/step) )```
+* **ExponentialSchedule** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/schedule/ExponentialSchedule.java)) - Implements ```value(i) = initialValue * gamma^i```
+* **InverseSchedule** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/schedule/InverseSchedule.java)) - Implements ```value(i) = initialValue * (1 + gamma * i)^(-power)```
+* **MapSchedule** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/schedule/MapSchedule.java)) - Learning rate schedule based on a user-provided map. Note that the provided map must have a value for iteration/epoch 0. Has a builder class to conveniently define a schedule.
+* **PolySchedule** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/schedule/PolySchedule.java)) - Implements ```value(i) = initialValue * (1 + i/maxIter)^(-power)```
+* **SigmoidSchedule** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/schedule/SigmoidSchedule.java)) - Implements ```value(i) = initialValue * 1.0 / (1 + exp(-gamma * (iter - stepSize)))```
+* **StepSchedule** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/schedule/StepSchedule.java)) - Implements ```value(i) = initialValue * gamma^( floor(iter/step) )```
 
 
 Note that custom schedules can be created by implementing the ISchedule interface.
@@ -312,10 +312,10 @@ L1 and L2 regularization is applied by default on the weight parameters only. Th
 
 All dropout types are applied at training time only. They are not applied at test time.
 
-* **Dropout** - ([Source](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/dropout/Dropout.java)) - Each input activation x is independently set to (0, with probability 1-p) or (x/p with probability p)<br>
-* **GaussianDropout** - ([Source](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/dropout/GaussianDropout.java)) - This is a multiplicative Gaussian noise (mean 1) on the input activations. Each input activation x is independently set to: ```x * y```, where ```y ~ N(1, stdev = sqrt((1-rate)/rate))```
-* **GaussianNoise** - ([Source](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/dropout/GaussianNoise.java)) - Applies additive, mean-zero Gaussian noise to the input - i.e., ```x = x + N(0,stddev)```
-* **AlphaDropout** - ([Source](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/dropout/AlphaDropout.java)) - AlphaDropout is a dropout technique proposed by [Klaumbauer et al. 2017 - Self-Normalizing Neural Networks](https://arxiv.org/abs/1706.02515). Designed for self-normalizing neural networks (SELU activation, NORMAL weight init). Attempts to keep both the mean and variance of the post-dropout activations to the same (in expectation) as before alpha dropout was applied
+* **Dropout** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/dropout/Dropout.java)) - Each input activation x is independently set to (0, with probability 1-p) or (x/p with probability p)<br>
+* **GaussianDropout** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/dropout/GaussianDropout.java)) - This is a multiplicative Gaussian noise (mean 1) on the input activations. Each input activation x is independently set to: ```x * y```, where ```y ~ N(1, stdev = sqrt((1-rate)/rate))```
+* **GaussianNoise** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/dropout/GaussianNoise.java)) - Applies additive, mean-zero Gaussian noise to the input - i.e., ```x = x + N(0,stddev)```
+* **AlphaDropout** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/dropout/AlphaDropout.java)) - AlphaDropout is a dropout technique proposed by [Klaumbauer et al. 2017 - Self-Normalizing Neural Networks](https://arxiv.org/abs/1706.02515). Designed for self-normalizing neural networks (SELU activation, NORMAL weight init). Attempts to keep both the mean and variance of the post-dropout activations to the same (in expectation) as before alpha dropout was applied
 
 Note that (as of current master - but not 0.9.1) the dropout parameters can also be specified according to any of the schedule classes mentioned in the Learning Rate Schedules section.
 
@@ -323,17 +323,17 @@ Note that (as of current master - but not 0.9.1) the dropout parameters can also
 
 As per dropout, dropconnect / weight noise is applied only at training time
 
-* **DropConnect** - ([Source](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/weightnoise/DropConnect.java)) - DropConnect is similar to dropout, but applied to the parameters of a network (instead of the input activations). [Reference](https://cs.nyu.edu/~wanli/dropc/dropc.pdf)
-* **WeightNoise** - ([Source](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/weightnoise/WeightNoise.java)) - Apply noise of the specified distribution to the weights at training time. Both additive and multiplicative modes are supported - when additive, noise should be mean 0, when multiplicative, noise should be mean 1
+* **DropConnect** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/weightnoise/DropConnect.java)) - DropConnect is similar to dropout, but applied to the parameters of a network (instead of the input activations). [Reference](https://cs.nyu.edu/~wanli/dropc/dropc.pdf)
+* **WeightNoise** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/weightnoise/WeightNoise.java)) - Apply noise of the specified distribution to the weights at training time. Both additive and multiplicative modes are supported - when additive, noise should be mean 0, when multiplicative, noise should be mean 1
 
 ### <a name="config-constraints">Constraints</a>
 
 Constraints are deterministic limitations that are placed on a model's parameters at the end of each iteration (after the parameter update has occurred). They can be thought of as a type of regularization.
 
-* **MaxNormConstraint** - ([Source](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/constraint/MaxNormConstraint.java)) - Constrain the maximum L2 norm of the incoming weights for each unit to be less than or equal to the specified value. If the L2 norm exceeds the specified value, the weights will be scaled down to satisfy the constraint.
-* **MinMaxNormConstraint** - ([Source](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/constraint/MinMaxNormConstraint.java)) - Constrain the minimum AND maximum L2 norm of the incoming weights for each unit to be between the specified values. Weights will be scaled up/down if required.
-* **NonNegativeConstraint** - ([Source](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/constraint/NonNegativeConstraint.java)) - Constrain all parameters to be non-negative. Negative parameters will be replaced with 0.
-* **UnitNormConstraint** - ([Source](https://github.com/deeplearning4j/deeplearning4j/blob/b841c0f549194dbdf88b42836df662d9b8ea8c6d/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/constraint/UnitNormConstraint.java)) - Constrain the L2 norm of the incoming weights for each unit to be 1.0.
+* **MaxNormConstraint** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/constraint/MaxNormConstraint.java)) - Constrain the maximum L2 norm of the incoming weights for each unit to be less than or equal to the specified value. If the L2 norm exceeds the specified value, the weights will be scaled down to satisfy the constraint.
+* **MinMaxNormConstraint** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/constraint/MinMaxNormConstraint.java)) - Constrain the minimum AND maximum L2 norm of the incoming weights for each unit to be between the specified values. Weights will be scaled up/down if required.
+* **NonNegativeConstraint** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/constraint/NonNegativeConstraint.java)) - Constrain all parameters to be non-negative. Negative parameters will be replaced with 0.
+* **UnitNormConstraint** - ([Source](https://github.com/eclipse/deeplearning4j/blob/b841c0f549194dbdf88b42836df662d9b8ea8c6d/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/constraint/UnitNormConstraint.java)) - Constrain the L2 norm of the incoming weights for each unit to be 1.0.
 
 
 ## <a name="data">Data Classes</a>
@@ -348,44 +348,44 @@ MultiDataSetIterator is similar to DataSetIterator, but returns MultiDataSet obj
 
 These iterators download their data as required. The actual datasets they return are not customizable.
 
-* **MnistDataSetIterator** - ([Source](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-data/deeplearning4j-datasets/src/main/java/org/deeplearning4j/datasets/iterator/impl/MnistDataSetIterator.java)) - DataSetIterator for the well-known MNIST digits dataset. By default, returns a row vector (1x784), with values normalized to 0 to 1 range. Use ```.setInputType(InputType.convolutionalFlat())``` to use with CNNs.
-* **EmnistDataSetIterator** - ([Source](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-data/deeplearning4j-datasets/src/main/java/org/deeplearning4j/datasets/iterator/impl/EmnistDataSetIterator.java)) - Similar to the MNIST digits dataset, but with more examples, and also letters. Includes multiple different splits (letters only, digits only, letters + digits, etc). Same 1x784 format as MNIST, hence (other than different number of labels for some splits) can be used as a drop-in replacement for MnistDataSetIterator. [Reference 1](https://www.nist.gov/itl/iad/image-group/emnist-dataset), [Reference 2](https://arxiv.org/abs/1702.05373)
-* **IrisDataSetIterator** - ([Source](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-data/deeplearning4j-datasets/src/main/java/org/deeplearning4j/datasets/iterator/impl/IrisDataSetIterator.java)) - An iterator for the well known Iris dataset. 4 features, 3 output classes.
-* **CifarDataSetIterator** - ([Source](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-data/deeplearning4j-datasets/src/main/java/org/deeplearning4j/datasets/iterator/impl/CifarDataSetIterator.java)) - An iterator for the CIFAR images dataset. 10 classes, 4d features/activations format for CNNs in DL4J: ```[minibatch,channels,height,width] = [minibatch,3,32,32]```. Features are *not* normalized - instead, are in the range 0 to 255.
+* **MnistDataSetIterator** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-data/deeplearning4j-datasets/src/main/java/org/deeplearning4j/datasets/iterator/impl/MnistDataSetIterator.java)) - DataSetIterator for the well-known MNIST digits dataset. By default, returns a row vector (1x784), with values normalized to 0 to 1 range. Use ```.setInputType(InputType.convolutionalFlat())``` to use with CNNs.
+* **EmnistDataSetIterator** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-data/deeplearning4j-datasets/src/main/java/org/deeplearning4j/datasets/iterator/impl/EmnistDataSetIterator.java)) - Similar to the MNIST digits dataset, but with more examples, and also letters. Includes multiple different splits (letters only, digits only, letters + digits, etc). Same 1x784 format as MNIST, hence (other than different number of labels for some splits) can be used as a drop-in replacement for MnistDataSetIterator. [Reference 1](https://www.nist.gov/itl/iad/image-group/emnist-dataset), [Reference 2](https://arxiv.org/abs/1702.05373)
+* **IrisDataSetIterator** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-data/deeplearning4j-datasets/src/main/java/org/deeplearning4j/datasets/iterator/impl/IrisDataSetIterator.java)) - An iterator for the well known Iris dataset. 4 features, 3 output classes.
+* **CifarDataSetIterator** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-data/deeplearning4j-datasets/src/main/java/org/deeplearning4j/datasets/iterator/impl/CifarDataSetIterator.java)) - An iterator for the CIFAR images dataset. 10 classes, 4d features/activations format for CNNs in DL4J: ```[minibatch,channels,height,width] = [minibatch,3,32,32]```. Features are *not* normalized - instead, are in the range 0 to 255.
 * **LFWDataSetIterator** - ([Source]())
-* **TinyImageNetDataSetIterator** ([Source](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-data/deeplearning4j-datasets/src/main/java/org/deeplearning4j/datasets/iterator/impl/TinyImageNetDataSetIterator.java)) - A subset of the standard imagenet dataset; 200 classes, 500 images per class
-* **UciSequenceDataSetIterator** ([Source](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-data/deeplearning4j-datasets/src/main/java/org/deeplearning4j/datasets/iterator/impl/UciSequenceDataSetIterator.java)) - UCI synthetic control time series dataset
+* **TinyImageNetDataSetIterator** ([Source](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-data/deeplearning4j-datasets/src/main/java/org/deeplearning4j/datasets/iterator/impl/TinyImageNetDataSetIterator.java)) - A subset of the standard imagenet dataset; 200 classes, 500 images per class
+* **UciSequenceDataSetIterator** ([Source](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-data/deeplearning4j-datasets/src/main/java/org/deeplearning4j/datasets/iterator/impl/UciSequenceDataSetIterator.java)) - UCI synthetic control time series dataset
 
 #### <a name="data-iter-user">Iterators - User Provided Data</a>
 
 The iterators in this subsection are used with user-provided data.
 
-* **RecordReaderDataSetIterator** - ([Source](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-data/deeplearning4j-datavec-iterators/src/main/java/org/deeplearning4j/datasets/datavec/RecordReaderDataSetIterator.java)) - an iterator that takes a DataVec record reader (such as CsvRecordReader or ImageRecordReader) and handles conversion to DataSets, batching, masking, etc. One of the most commonly used iterators in DL4J. Handles non-sequence data only, as input (i.e., RecordReader, no SequenceeRecordReader).
-* **RecordReaderMultiDataSetIterator** - ([Source](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-data/deeplearning4j-datavec-iterators/src/main/java/org/deeplearning4j/datasets/datavec/RecordReaderMultiDataSetIterator.java)) - the MultiDataSet version of RecordReaderDataSetIterator, that supports multiple readers. Has a builder pattern for creating more complex data pipelines (such as different subsets of a reader's output to different input/output arrays, conversion to one-hot, etc). Handles both sequence and non-sequence data as input.
-* **SequenceRecordReaderDataSetIterator** - ([Source](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-data/deeplearning4j-datavec-iterators/src/main/java/org/deeplearning4j/datasets/datavec/SequenceRecordReaderDataSetIterator.java)) - The sequence (SequenceRecordReader) version of RecordReaderDataSetIterator. Users may be better off using RecordReaderMultiDataSetIterator, in conjunction with
-* **DoublesDataSetIterator** - ([Source](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-data/deeplearning4j-utility-iterators/src/main/java/org/deeplearning4j/datasets/iterator/DoublesDataSetIterator.java))
-* **FloatsDataSetIterator** - ([Source](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-data/deeplearning4j-utility-iterators/src/main/java/org/deeplearning4j/datasets/iterator/FloatsDataSetIterator.java))
-* **INDArrayDataSetIterator** - ([Source](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-data/deeplearning4j-utility-iterators/src/main/java/org/deeplearning4j/datasets/iterator/INDArrayDataSetIterator.java))
+* **RecordReaderDataSetIterator** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-data/deeplearning4j-datavec-iterators/src/main/java/org/deeplearning4j/datasets/datavec/RecordReaderDataSetIterator.java)) - an iterator that takes a DataVec record reader (such as CsvRecordReader or ImageRecordReader) and handles conversion to DataSets, batching, masking, etc. One of the most commonly used iterators in DL4J. Handles non-sequence data only, as input (i.e., RecordReader, no SequenceeRecordReader).
+* **RecordReaderMultiDataSetIterator** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-data/deeplearning4j-datavec-iterators/src/main/java/org/deeplearning4j/datasets/datavec/RecordReaderMultiDataSetIterator.java)) - the MultiDataSet version of RecordReaderDataSetIterator, that supports multiple readers. Has a builder pattern for creating more complex data pipelines (such as different subsets of a reader's output to different input/output arrays, conversion to one-hot, etc). Handles both sequence and non-sequence data as input.
+* **SequenceRecordReaderDataSetIterator** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-data/deeplearning4j-datavec-iterators/src/main/java/org/deeplearning4j/datasets/datavec/SequenceRecordReaderDataSetIterator.java)) - The sequence (SequenceRecordReader) version of RecordReaderDataSetIterator. Users may be better off using RecordReaderMultiDataSetIterator, in conjunction with
+* **DoublesDataSetIterator** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-data/deeplearning4j-utility-iterators/src/main/java/org/deeplearning4j/datasets/iterator/DoublesDataSetIterator.java))
+* **FloatsDataSetIterator** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-data/deeplearning4j-utility-iterators/src/main/java/org/deeplearning4j/datasets/iterator/FloatsDataSetIterator.java))
+* **INDArrayDataSetIterator** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-data/deeplearning4j-utility-iterators/src/main/java/org/deeplearning4j/datasets/iterator/INDArrayDataSetIterator.java))
 
 
 #### <a name="data-iter-util">Iterators - Adapter and Utility Iterators</a>
 
-* **MultiDataSetIteratorAdapter** - ([Source](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/datasets/iterator/impl/MultiDataSetIteratorAdapter.java)) - Wrap a DataSetIterator to convert it to a MultiDataSetIterator
-* **SingletonMultiDataSetIterator** - ([Source](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-data/deeplearning4j-utility-iterators/src/main/java/org/deeplearning4j/datasets/iterator/impl/SingletonMultiDataSetIterator.java)) - Wrap a MultiDataSet into a MultiDataSetIterator that returns one MultiDataSet (i.e., the wrapped MultiDataSet is *not* split up)
-* **AsyncDataSetIterator** - ([Source](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-data/deeplearning4j-utility-iterators/src/main/java/org/deeplearning4j/datasets/iterator/AsyncDataSetIterator.java)) - Used automatically by MultiLayerNetwork and ComputationGraph where appropriate. Implements asynchronous prefetching of datasets to improve performance.
-* **AsyncMultiDataSetIterator**  - ([Source](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-data/deeplearning4j-utility-iterators/src/main/java/org/deeplearning4j/datasets/iterator/AsyncMultiDataSetIterator.java)) - Used automatically by ComputationGraph where appropriate. Implements asynchronous prefetching of MultiDataSets to improve performance.
-* **AsyncShieldDataSetIterator** - ([Source](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-data/deeplearning4j-utility-iterators/src/main/java/org/deeplearning4j/datasets/iterator/AsyncShieldDataSetIterator.java)) - Generally used only for debugging. Stops MultiLayerNetwork and ComputationGraph from using an AsyncDataSetIterator.
-* **AsyncShieldMultiDataSetIterator** - ([Source](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-data/deeplearning4j-utility-iterators/src/main/java/org/deeplearning4j/datasets/iterator/AsyncShieldMultiDataSetIterator.java)) - The MultiDataSetIterator version of AsyncShieldDataSetIterator
-* **EarlyTerminationDataSetIterator** - ([Source](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-data/deeplearning4j-utility-iterators/src/main/java/org/deeplearning4j/datasets/iterator/EarlyTerminationDataSetIterator.java)) - Wraps another DataSetIterator, ensuring that only a specified (maximum) number of minibatches (DataSet) objects are returned between resets. Can be used to 'cut short' an iterator, returning only the first N DataSets.
-* **EarlyTerminationMultiDataSetIterator** - ([Source](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-data/deeplearning4j-utility-iterators/src/main/java/org/deeplearning4j/datasets/iterator/EarlyTerminationMultiDataSetIterator.java)) - The MultiDataSetIterator version of EarlyTerminationDataSetIterator
-* **ExistingDataSetIterator** - ([Source](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-data/deeplearning4j-utility-iterators/src/main/java/org/deeplearning4j/datasets/iterator/ExistingDataSetIterator.java)) - Convert an ```Iterator<DataSet>``` or ```Iterable<DataSet>``` to a DataSetIterator. Does not split the underlying DataSet objects
-* **FileDataSetIterator** - ([Source](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-data/deeplearning4j-utility-iterators/src/main/java/org/deeplearning4j/datasets/iterator/file/FileDataSetIterator.java)) - An iterator that iterates over DataSet files that have been previously saved with ```DataSet.save(File)```. Supports randomization, filtering, different output batch size vs. saved DataSet batch size, etc.
-* **FileMultiDataSetIterator** - ([Source](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-data/deeplearning4j-utility-iterators/src/main/java/org/deeplearning4j/datasets/iterator/file/FileMultiDataSetIterator.java)) - A MultiDataSet version of FileDataSetIterator
-* **IteratorDataSetIterator** - ([Source](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-data/deeplearning4j-utility-iterators/src/main/java/org/deeplearning4j/datasets/iterator/IteratorDataSetIterator.java)) - Convert an ```Iterator<DataSet>``` to a DataSetIterator. Unlike ExistingDataSetIterator, the underlying DataSet objects may be split/combined - i.e., the minibatch size may differ for the output, vs. the input iterator.
-* **IteratorMultiDataSetIterator** - ([Source](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-data/deeplearning4j-utility-iterators/src/main/java/org/deeplearning4j/datasets/iterator/IteratorMultiDataSetIterator.java)) - The ```Iterator<MultiDataSet>``` version of IteratorDataSetIterator
-* **MultiDataSetWrapperIterator** - ([Source](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-data/deeplearning4j-utility-iterators/src/main/java/org/deeplearning4j/datasets/iterator/MultiDataSetWrapperIterator.java)) - Convert a MultiDataSetIterator to a DataSetIterator. Note that this is only possible if the number of features and labels arrays is equal to 1.
-* **MultipleEpochsIterator** - ([Source](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-data/deeplearning4j-utility-iterators/src/main/java/org/deeplearning4j/datasets/iterator/MultipleEpochsIterator.java)) - Treat multiple passes (epochs) of the underlying iterator as a single epoch, when training.
-* **WorkspaceShieldDataSetIterator** - ([Source](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-data/deeplearning4j-utility-iterators/src/main/java/org/deeplearning4j/datasets/iterator/WorkspacesShieldDataSetIterator.java)) - Generally used only for debugging, and not usually by users. Detaches/migrates DataSets coming out of the underlying DataSetIterator.
+* **MultiDataSetIteratorAdapter** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/datasets/iterator/impl/MultiDataSetIteratorAdapter.java)) - Wrap a DataSetIterator to convert it to a MultiDataSetIterator
+* **SingletonMultiDataSetIterator** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-data/deeplearning4j-utility-iterators/src/main/java/org/deeplearning4j/datasets/iterator/impl/SingletonMultiDataSetIterator.java)) - Wrap a MultiDataSet into a MultiDataSetIterator that returns one MultiDataSet (i.e., the wrapped MultiDataSet is *not* split up)
+* **AsyncDataSetIterator** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-data/deeplearning4j-utility-iterators/src/main/java/org/deeplearning4j/datasets/iterator/AsyncDataSetIterator.java)) - Used automatically by MultiLayerNetwork and ComputationGraph where appropriate. Implements asynchronous prefetching of datasets to improve performance.
+* **AsyncMultiDataSetIterator**  - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-data/deeplearning4j-utility-iterators/src/main/java/org/deeplearning4j/datasets/iterator/AsyncMultiDataSetIterator.java)) - Used automatically by ComputationGraph where appropriate. Implements asynchronous prefetching of MultiDataSets to improve performance.
+* **AsyncShieldDataSetIterator** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-data/deeplearning4j-utility-iterators/src/main/java/org/deeplearning4j/datasets/iterator/AsyncShieldDataSetIterator.java)) - Generally used only for debugging. Stops MultiLayerNetwork and ComputationGraph from using an AsyncDataSetIterator.
+* **AsyncShieldMultiDataSetIterator** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-data/deeplearning4j-utility-iterators/src/main/java/org/deeplearning4j/datasets/iterator/AsyncShieldMultiDataSetIterator.java)) - The MultiDataSetIterator version of AsyncShieldDataSetIterator
+* **EarlyTerminationDataSetIterator** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-data/deeplearning4j-utility-iterators/src/main/java/org/deeplearning4j/datasets/iterator/EarlyTerminationDataSetIterator.java)) - Wraps another DataSetIterator, ensuring that only a specified (maximum) number of minibatches (DataSet) objects are returned between resets. Can be used to 'cut short' an iterator, returning only the first N DataSets.
+* **EarlyTerminationMultiDataSetIterator** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-data/deeplearning4j-utility-iterators/src/main/java/org/deeplearning4j/datasets/iterator/EarlyTerminationMultiDataSetIterator.java)) - The MultiDataSetIterator version of EarlyTerminationDataSetIterator
+* **ExistingDataSetIterator** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-data/deeplearning4j-utility-iterators/src/main/java/org/deeplearning4j/datasets/iterator/ExistingDataSetIterator.java)) - Convert an ```Iterator<DataSet>``` or ```Iterable<DataSet>``` to a DataSetIterator. Does not split the underlying DataSet objects
+* **FileDataSetIterator** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-data/deeplearning4j-utility-iterators/src/main/java/org/deeplearning4j/datasets/iterator/file/FileDataSetIterator.java)) - An iterator that iterates over DataSet files that have been previously saved with ```DataSet.save(File)```. Supports randomization, filtering, different output batch size vs. saved DataSet batch size, etc.
+* **FileMultiDataSetIterator** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-data/deeplearning4j-utility-iterators/src/main/java/org/deeplearning4j/datasets/iterator/file/FileMultiDataSetIterator.java)) - A MultiDataSet version of FileDataSetIterator
+* **IteratorDataSetIterator** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-data/deeplearning4j-utility-iterators/src/main/java/org/deeplearning4j/datasets/iterator/IteratorDataSetIterator.java)) - Convert an ```Iterator<DataSet>``` to a DataSetIterator. Unlike ExistingDataSetIterator, the underlying DataSet objects may be split/combined - i.e., the minibatch size may differ for the output, vs. the input iterator.
+* **IteratorMultiDataSetIterator** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-data/deeplearning4j-utility-iterators/src/main/java/org/deeplearning4j/datasets/iterator/IteratorMultiDataSetIterator.java)) - The ```Iterator<MultiDataSet>``` version of IteratorDataSetIterator
+* **MultiDataSetWrapperIterator** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-data/deeplearning4j-utility-iterators/src/main/java/org/deeplearning4j/datasets/iterator/MultiDataSetWrapperIterator.java)) - Convert a MultiDataSetIterator to a DataSetIterator. Note that this is only possible if the number of features and labels arrays is equal to 1.
+* **MultipleEpochsIterator** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-data/deeplearning4j-utility-iterators/src/main/java/org/deeplearning4j/datasets/iterator/MultipleEpochsIterator.java)) - Treat multiple passes (epochs) of the underlying iterator as a single epoch, when training.
+* **WorkspaceShieldDataSetIterator** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-data/deeplearning4j-utility-iterators/src/main/java/org/deeplearning4j/datasets/iterator/WorkspacesShieldDataSetIterator.java)) - Generally used only for debugging, and not usually by users. Detaches/migrates DataSets coming out of the underlying DataSetIterator.
 
 
 ### <a name="data-norm">Data Normalization</a>
@@ -403,32 +403,32 @@ In general, you should fit *only* on the training data, and do ```trainData.setP
 
 Note that where appropriate (NormalizerStandardize, NormalizerMinMaxScaler) statistics such as mean/standard-deviation/min/max are shared across time (for time series) and across image x/y locations (but not depth/channels - for image data).
 
-Data normalization example: [link](https://github.com/deeplearning4j/dl4j-examples/blob/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/dataexamples/PreprocessNormalizerExample.java)
+Data normalization example: [link](https://github.com/eclipse/deeplearning4j-examples/blob/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/dataexamples/PreprocessNormalizerExample.java)
 
 **Available normalizers: DataSet / DataSetIterator**
 
-* **ImagePreProcessingScaler** - ([Source](https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/dataset/api/preprocessor/ImagePreProcessingScaler.java)) - Applies min-max scaling to image activations. Default settings do 0-255 input to 0-1 output (but is configurable). Note that unlike the other normalizers here, this one does not rely on statistics (mean/min/max etc) collected from the data, hence the ```normalizer.fit(trainData)``` step is unnecessary (is a no-op).
-* **NormalizerStandardize** - ([Source](https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/dataset/api/preprocessor/NormalizerStandardize.java)) - normalizes each feature value independently (and optionally label values) to have 0 mean and a standard deviation of 1
-* **NormalizerMinMaxScaler** - ([Source](https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/dataset/api/preprocessor/NormalizerMinMaxScaler.java)) - normalizes each feature value independently (and optionally label values) to lie between a minimum and maximum value (by default between 0 and 1)
-* **VGG16ImagePreProcessor** - ([Source](https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/dataset/api/preprocessor/VGG16ImagePreProcessor.java)) - This is a preprocessor specifically for VGG16. It subtracts the mean RGB value, computed on the training set, from each pixel as reported in [Link](https://arxiv.org/pdf/1409.1556.pdf)
+* **ImagePreProcessingScaler** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/dataset/api/preprocessor/ImagePreProcessingScaler.java)) - Applies min-max scaling to image activations. Default settings do 0-255 input to 0-1 output (but is configurable). Note that unlike the other normalizers here, this one does not rely on statistics (mean/min/max etc) collected from the data, hence the ```normalizer.fit(trainData)``` step is unnecessary (is a no-op).
+* **NormalizerStandardize** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/dataset/api/preprocessor/NormalizerStandardize.java)) - normalizes each feature value independently (and optionally label values) to have 0 mean and a standard deviation of 1
+* **NormalizerMinMaxScaler** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/dataset/api/preprocessor/NormalizerMinMaxScaler.java)) - normalizes each feature value independently (and optionally label values) to lie between a minimum and maximum value (by default between 0 and 1)
+* **VGG16ImagePreProcessor** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/dataset/api/preprocessor/VGG16ImagePreProcessor.java)) - This is a preprocessor specifically for VGG16. It subtracts the mean RGB value, computed on the training set, from each pixel as reported in [Link](https://arxiv.org/pdf/1409.1556.pdf)
 
 
 **Available normalizers: MultiDataSet / MultiDataSetIterator**
 
-* **ImageMultiPreProcessingScaler** - ([Source](https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/dataset/api/preprocessor/ImageMultiPreProcessingScaler.java)) - A MultiDataSet/MultiDataSetIterator version of ImagePreProcessingScaler
-* **MultiNormalizerStandardize** - ([Source](https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/dataset/api/preprocessor/MultiNormalizerStandardize.java)) - MultiDataSet/MultiDataSetIterator version of NormalizerStandardize
-* **MultiNormalizerMinMaxScaler** - ([Source](https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/dataset/api/preprocessor/MultiNormalizerMinMaxScaler.java)) - MultiDataSet/MultiDataSetIterator version of NormalizerMinMaxScaler
-* **MultiNormalizerHybrid** - ([Source](https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/dataset/api/preprocessor/MultiNormalizerHybrid.java)) - A MultiDataSet normalizer that can combine different normalization types (standardize, min/max etc) for different input/feature and output/label arrays.
+* **ImageMultiPreProcessingScaler** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/dataset/api/preprocessor/ImageMultiPreProcessingScaler.java)) - A MultiDataSet/MultiDataSetIterator version of ImagePreProcessingScaler
+* **MultiNormalizerStandardize** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/dataset/api/preprocessor/MultiNormalizerStandardize.java)) - MultiDataSet/MultiDataSetIterator version of NormalizerStandardize
+* **MultiNormalizerMinMaxScaler** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/dataset/api/preprocessor/MultiNormalizerMinMaxScaler.java)) - MultiDataSet/MultiDataSetIterator version of NormalizerMinMaxScaler
+* **MultiNormalizerHybrid** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/dataset/api/preprocessor/MultiNormalizerHybrid.java)) - A MultiDataSet normalizer that can combine different normalization types (standardize, min/max etc) for different input/feature and output/label arrays.
 
 
 ### <a name="transfer">Transfer Learning</a>
 
 Deeplearning4j has classes/utilities for performing transfer learning - i.e., taking an existing network, and modifying some of the layers (optionally freezing others so their parameters don't change). For example, an image classifier could be trained on ImageNet, then applied to a new/different dataset. Both MultiLayerNetwork and ComputationGraph can be used with transfer learning - frequently starting from a pre-trained model from the model zoo (see next section), though any MultiLayerNetwork/ComputationGraph can be used.
 
-Link: [Transfer learning examples](https://github.com/deeplearning4j/dl4j-examples/tree/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/transferlearning/vgg16)
+Link: [Transfer learning examples](https://github.com/eclipse/deeplearning4j-examples/tree/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/transferlearning/vgg16)
 
-The main class for transfer learning is [TransferLearning](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/transferlearning/TransferLearning.java). This class has a builder pattern that can be used to add/remove layers, freeze layers, etc.
-[FineTuneConfiguration](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/transferlearning/FineTuneConfiguration.java) can be used here to specify the learning rate and other settings for the non-frozen layers.
+The main class for transfer learning is [TransferLearning](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/transferlearning/TransferLearning.java). This class has a builder pattern that can be used to add/remove layers, freeze layers, etc.
+[FineTuneConfiguration](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/transferlearning/FineTuneConfiguration.java) can be used here to specify the learning rate and other settings for the non-frozen layers.
 
 
 ### <a name="zoo">Trained Model Library - Model Zoo</a>
@@ -439,17 +439,17 @@ Link: [Deeplearning4j Model Zoo](https://deeplearning4j.org/model-zoo)
 
 Models available in DL4J's model zoo:
 
-* **AlexNet** - ([Source](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-zoo/src/main/java/org/deeplearning4j/zoo/model/AlexNet.java))
-* **Darknet19** - ([Source](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-zoo/src/main/java/org/deeplearning4j/zoo/model/Darknet19.java))
-* **FaceNetNN4Small2** - ([Source](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-zoo/src/main/java/org/deeplearning4j/zoo/model/FaceNetNN4Small2.java))
-* **InceptionResNetV1** - ([Source](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-zoo/src/main/java/org/deeplearning4j/zoo/model/InceptionResNetV1.java))
-* **LeNet** - ([Source](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-zoo/src/main/java/org/deeplearning4j/zoo/model/LeNet.java))
-* **ResNet50** - ([Source](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-zoo/src/main/java/org/deeplearning4j/zoo/model/ResNet50.java))
-* **SimpleCNN** - ([Source](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-zoo/src/main/java/org/deeplearning4j/zoo/model/SimpleCNN.java))
-* **TextGenerationLSTM** - ([Source](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-zoo/src/main/java/org/deeplearning4j/zoo/model/TextGenerationLSTM.java))
-* **TinyYOLO** - ([Source](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-zoo/src/main/java/org/deeplearning4j/zoo/model/TinyYOLO.java))
-* **VGG16** - ([Source](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-zoo/src/main/java/org/deeplearning4j/zoo/model/VGG16.java))
-* **VGG19** - ([Source](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-zoo/src/main/java/org/deeplearning4j/zoo/model/VGG19.java))
+* **AlexNet** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-zoo/src/main/java/org/deeplearning4j/zoo/model/AlexNet.java))
+* **Darknet19** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-zoo/src/main/java/org/deeplearning4j/zoo/model/Darknet19.java))
+* **FaceNetNN4Small2** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-zoo/src/main/java/org/deeplearning4j/zoo/model/FaceNetNN4Small2.java))
+* **InceptionResNetV1** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-zoo/src/main/java/org/deeplearning4j/zoo/model/InceptionResNetV1.java))
+* **LeNet** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-zoo/src/main/java/org/deeplearning4j/zoo/model/LeNet.java))
+* **ResNet50** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-zoo/src/main/java/org/deeplearning4j/zoo/model/ResNet50.java))
+* **SimpleCNN** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-zoo/src/main/java/org/deeplearning4j/zoo/model/SimpleCNN.java))
+* **TextGenerationLSTM** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-zoo/src/main/java/org/deeplearning4j/zoo/model/TextGenerationLSTM.java))
+* **TinyYOLO** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-zoo/src/main/java/org/deeplearning4j/zoo/model/TinyYOLO.java))
+* **VGG16** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-zoo/src/main/java/org/deeplearning4j/zoo/model/VGG16.java))
+* **VGG19** - ([Source](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-zoo/src/main/java/org/deeplearning4j/zoo/model/VGG19.java))
 
 
 **Note*: Trained Keras models (not provided by DL4J) may also be imported, using Deeplearning4j's Keras model import functionality.
@@ -635,7 +635,7 @@ TransformProcess tp = new TransformProcess.Builder(schema)
 JavaRDD<List<Writable>> processedData = SparkTransformExecutor.execute(parsedInputData, tp);
 ```
 
-We recommend having a look at the [DataVec examples](https://github.com/deeplearning4j/dl4j-examples/tree/master/datavec-examples/src/main/java/org/datavec/transform) before creating more complex transformations.
+We recommend having a look at the [DataVec examples](https://github.com/eclipse/deeplearning4j-examples/tree/master/datavec-examples/src/main/java/org/datavec/transform) before creating more complex transformations.
 
 
 ### Evaluation
diff --git a/docs/deeplearning4j/templates/concepts.md b/docs/deeplearning4j/templates/concepts.md
index 9912fffb3..f04d17782 100644
--- a/docs/deeplearning4j/templates/concepts.md
+++ b/docs/deeplearning4j/templates/concepts.md
@@ -18,7 +18,7 @@ Unlike other machine learning or deep learning frameworks, DL4J treats the tasks
 
 Before the algorithm can start learning, you have to prepare the data, even if you already have a trained model. Preparing data means loading it and putting it in the right shape and value range (e.g. normalization, zero-mean and unit variance). Building these processes from scratch is error prone, so use DataVec wherever possible.
 
-Deeplearning4j works with a lot of different data types, such as images, CSV, plain text and, with [Apache Camel](https://camel.apache.org/) [integration](https://github.com/deeplearning4j/DataVec/tree/master/datavec-camel), pretty much any other data type you can think of.
+Deeplearning4j works with a lot of different data types, such as images, CSV, plain text and, with [Apache Camel](https://camel.apache.org/) [integration](https://github.com/eclipse/deeplearning4j/tree/master/datavec/tree/master/datavec-camel), pretty much any other data type you can think of.
 
 To use DataVec, you will need one of the implementations of the [RecordReader](/api/{{page.version}}/org/datavec/api/records/reader/RecordReader.html) interface along with the [RecordReaderDataSetIterator](/api/{{page.version}}/org/deeplearning4j/datasets/datavec/RecordReaderDataSetIterator.html).
 
@@ -95,7 +95,7 @@ Also note that DL4J does not only support training just `MultiLayerNetworks`, bu
 As you train your model, you will want to test how well it performs. For that test, you will need a dedicated data set that will not be used for training but instead will only be used for evaluating your model. This data should have the same distribution as the real-world data you want to make predictions about with your model. The reason you can't simply use your training data for evaluation is because machine learning methods are prone to overfitting (getting good at making predictions about the training set, but not performing well on larger datasets).
 
 The [Evaluation](/api/{{page.version}}/org/deeplearning4j/eval/Evaluation.html)
-class is used for evaluation. Slightly different methods apply to evaluating a normal feed forward networks or recurrent networks. For more details on using it, take a look at the corresponding [examples](https://github.com/deeplearning4j/dl4j-examples).
+class is used for evaluation. Slightly different methods apply to evaluating a normal feed forward networks or recurrent networks. For more details on using it, take a look at the corresponding [examples](https://github.com/eclipse/deeplearning4j-examples).
 
 ## Troubleshooting a Neural Net Model
 
diff --git a/docs/deeplearning4j/templates/config-performance-debugging.md b/docs/deeplearning4j/templates/config-performance-debugging.md
index 37bfaf2cd..6dafd13b7 100644
--- a/docs/deeplearning4j/templates/config-performance-debugging.md
+++ b/docs/deeplearning4j/templates/config-performance-debugging.md
@@ -260,7 +260,7 @@ For inference, avoid using minibatch size of 1, as throughput will suffer. Unles
 
 For training, you should never use a minibatch size of 1 as overall performance and hardware utilization will be reduced. Network convergence may also suffer. Start with a minibatch size of 32-128, if memory will allow this to be used.
 
-For serving predictions in multi-threaded applications (such as a web server), [ParallelInference](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-scaleout/deeplearning4j-scaleout-parallelwrapper/src/main/java/org/deeplearning4j/parallelism/ParallelInference.java) should be used.
+For serving predictions in multi-threaded applications (such as a web server), [ParallelInference](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-scaleout/deeplearning4j-scaleout-parallelwrapper/src/main/java/org/deeplearning4j/parallelism/ParallelInference.java) should be used.
 
 
 ## Step 6: Ensure you are not using a single MultiLayerNetwork/ComputationGraph for inference from multiple threads
@@ -270,7 +270,7 @@ That said, most operations such as fit, output, etc use synchronized blocks. The
 In summary, using the one network from multiple threads should be avoided as it is not thread safe and can be a performance bottleneck.
 
 
-For inference from multiple threads, you should use one model per thread (as this avoids locks) or for serving predictions in multi-threaded applications (such as a web server), use [ParallelInference](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-scaleout/deeplearning4j-scaleout-parallelwrapper/src/main/java/org/deeplearning4j/parallelism/ParallelInference.java).
+For inference from multiple threads, you should use one model per thread (as this avoids locks) or for serving predictions in multi-threaded applications (such as a web server), use [ParallelInference](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-scaleout/deeplearning4j-scaleout-parallelwrapper/src/main/java/org/deeplearning4j/parallelism/ParallelInference.java).
 
 ## Step 7: Check Data Types
 
@@ -386,7 +386,7 @@ In summary, in ND4J we use OpenMP pallelism at the c++ level to increase operati
 
 This also applies if the CPU resources are shared with other computationally demanding processes.
 
-In either case, you may see better overall throughput by reducing the number of OpenMP threads by setting the OMP_NUM_THREADS environment variable - see [ND4JEnvironmentVars](https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-common/src/main/java/org/nd4j/config/ND4JEnvironmentVars.java) for details.
+In either case, you may see better overall throughput by reducing the number of OpenMP threads by setting the OMP_NUM_THREADS environment variable - see [ND4JEnvironmentVars](https://github.com/eclipse/deeplearning4j/blob/master/nd4j/nd4j-common/src/main/java/org/nd4j/config/ND4JEnvironmentVars.java) for details.
 
 One reason for reducing OMP_NUM_THREADS improving overall performance is due to reduced [cache thrashing](https://en.wikipedia.org/wiki/Thrashing_(computer_science)).
 
diff --git a/docs/deeplearning4j/templates/config-snapshots.md b/docs/deeplearning4j/templates/config-snapshots.md
index 4eecc8451..7bf6b3a00 100644
--- a/docs/deeplearning4j/templates/config-snapshots.md
+++ b/docs/deeplearning4j/templates/config-snapshots.md
@@ -71,7 +71,7 @@ to
 **Sample pom.xml using Snapshots**
 
 A sample pom.xml is provided here: [sample pom.xml using snapshots](https://gist.github.com/AlexDBlack/28b0c9a72bce562c8782be326a6e2aaa)
-This has been taken from the DL4J standalone sample project and modified using step 1 and 2 above. The original (using the last release) can be found [here](https://github.com/deeplearning4j/dl4j-examples/blob/master/standalone-sample-project/pom.xml)
+This has been taken from the DL4J standalone sample project and modified using step 1 and 2 above. The original (using the last release) can be found [here](https://github.com/eclipse/deeplearning4j-examples/blob/master/standalone-sample-project/pom.xml)
 
 
 ## <a name="Limitations">Limitations</a>
@@ -129,4 +129,4 @@ dependencies {
 
 should work in theory, but it does not. This is due to [a bug in Gradle](https://github.com/gradle/gradle/issues/2882). Gradle with snapshots *and* Maven classifiers appears to be a problem.
 
- Of note when using the nd4j-native backend on Gradle (and SBT - but not Maven), you need to add openblas as a dependency. We do this for you in the -platform pom. Reference the -platform pom [here](https://github.com/deeplearning4j/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-backend-impls/nd4j-native-platform/pom.xml#L19) to double check your dependencies. Note that these are version properties. See the ```<properties>``` section of the pom for current versions of the openblas and javacpp presets required to run nd4j-native.
+ Of note when using the nd4j-native backend on Gradle (and SBT - but not Maven), you need to add openblas as a dependency. We do this for you in the -platform pom. Reference the -platform pom [here](https://github.com/eclipse/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-backend-impls/nd4j-native-platform/pom.xml#L19) to double check your dependencies. Note that these are version properties. See the ```<properties>``` section of the pom for current versions of the openblas and javacpp presets required to run nd4j-native.
diff --git a/docs/deeplearning4j/templates/contribute.md b/docs/deeplearning4j/templates/contribute.md
index f101db1bc..735d58c36 100644
--- a/docs/deeplearning4j/templates/contribute.md
+++ b/docs/deeplearning4j/templates/contribute.md
@@ -8,14 +8,14 @@ weight: 10
 
 ## Prerequisites
 
-Before contributing, make sure you know the structure of all of the Eclipse Deeplearning4j libraries. As of early 2018, all libraries now live in the Deeplearning4j [monorepo](https://github.com/deeplearning4j/deeplearning4j). These include:
+Before contributing, make sure you know the structure of all of the Eclipse Deeplearning4j libraries. As of early 2018, all libraries now live in the Deeplearning4j [monorepo](https://github.com/eclipse/deeplearning4j). These include:
 
 - DeepLearning4J: Contains all of the code for learning neural networks, both on a single machine and distributed.
 - ND4J: “N-Dimensional Arrays for Java”. ND4J is the mathematical backend upon which DL4J is built. All of DL4J’s neural networks are built using the operations (matrix multiplications, vector operations, etc) in ND4J. ND4J is how DL4J supports both CPU and GPU training of networks, without any changes to the networks themselves. Without ND4J, there would be no DL4J.
 - DataVec: DataVec handles the data import and conversion side of the pipeline. If you want to import images, video, audio or simply CSV data into DL4J: you probably want to use DataVec to do this.
 - Arbiter: Arbiter is a package for (amongst other things) hyperparameter optimization of neural networks. Hyperparameter optimization refers to the process of automating the selection of network hyperparameters (learning rate, number of layers, etc) in order to obtain good performance.
 
-We also have an extensive examples repository at [dl4j-examples](https://github.com/deeplearning4j/dl4j-examples).
+We also have an extensive examples repository at [dl4j-examples](https://github.com/eclipse/deeplearning4j-examples).
 
 
 ## Ways to contribute
@@ -34,8 +34,8 @@ There are numerous ways to contribute to DeepLearning4J (and related projects),
 There are a number of different ways to find things to work on. These include:
 
 - Looking at the issue trackers:
-https://github.com/deeplearning4j/deeplearning4j/issues
-https://github.com/deeplearning4j/dl4j-examples/issues
+https://github.com/eclipse/deeplearning4j/issues
+https://github.com/eclipse/deeplearning4j-examples/issues
 - Reviewing our Roadmap
 - Talking to the developers on Gitter, especially our early adopters channel
 - Reviewing recent papers and blog posts on training features, network architectures and applications
diff --git a/docs/deeplearning4j/templates/examples-tour.md b/docs/deeplearning4j/templates/examples-tour.md
index 2bb3312d8..2aa5dd29b 100644
--- a/docs/deeplearning4j/templates/examples-tour.md
+++ b/docs/deeplearning4j/templates/examples-tour.md
@@ -18,31 +18,31 @@ Most of the examples make use of DataVec, a toolkit for preprocessing and clearn
 
 This example takes the canonical Iris dataset of the flower species of the same name, whose relevant measurements are sepal length, sepal width, petal length and petal width. It builds a Spark RDD from the relatively small dataset and runs an analysis against it. 
 
-[Show me the code](http://github.com/deeplearning4j/dl4j-examples/blob/master/datavec-examples/src/main/java/org/datavec/transform/analysis/IrisAnalysis.java)
+[Show me the code](http://github.com/eclipse/deeplearning4j-examples/blob/master/datavec-examples/src/main/java/org/datavec/transform/analysis/IrisAnalysis.java)
 
 ### BasicDataVecExample.java
 
 This example loads data into a Spark RDD. All DataVec transform operations use Spark RDDs. Here, we use DataVec to filter data, apply time transformations and remove columns.
 
-[Show me the code](http://github.com/deeplearning4j/dl4j-examples/blob/master/datavec-examples/src/main/java/org/datavec/transform/basic/BasicDataVecExample.java)
+[Show me the code](http://github.com/eclipse/deeplearning4j-examples/blob/master/datavec-examples/src/main/java/org/datavec/transform/basic/BasicDataVecExample.java)
 
 ### PrintSchemasAtEachStep.java
 
 This example shows the print Schema tools that are useful to visualize and to ensure that the code for the transform is behaving as expected. 
 
-[Show me the code](http://github.com/deeplearning4j/dl4j-examples/blob/master/datavec-examples/src/main/java/org/datavec/transform/debugging/PrintSchemasAtEachStep.java)
+[Show me the code](http://github.com/eclipse/deeplearning4j-examples/blob/master/datavec-examples/src/main/java/org/datavec/transform/debugging/PrintSchemasAtEachStep.java)
 
 ### JoinExample.java
 
 You may need to join datasets before passing to a neural network. You can do that in DataVec, and this example shows you how. 
 
-[Show me the code](http://github.com/deeplearning4j/dl4j-examples/blob/master/datavec-examples/src/main/java/org/datavec/transform/join/JoinExample.java)
+[Show me the code](http://github.com/eclipse/deeplearning4j-examples/blob/master/datavec-examples/src/main/java/org/datavec/transform/join/JoinExample.java)
 
 ### LogDataExample.java
 
 This is an example of parsing log data using DataVec. The obvious use cases are cybersecurity and customer relationship management. 
 
-[Show me the code](http://github.com/deeplearning4j/dl4j-examples/blob/master/datavec-examples/src/main/java/org/datavec/transform/logdata/LogDataExample.java)
+[Show me the code](http://github.com/eclipse/deeplearning4j-examples/blob/master/datavec-examples/src/main/java/org/datavec/transform/logdata/LogDataExample.java)
 
 ### MnistImagePipelineExample.java
 
@@ -50,19 +50,19 @@ This example is from the video below, which demonstrates the ParentPathLabelGene
 
 <iframe width="560" height="315" src="http://www.youtube.com/embed/GLC8CIoHDnI" frameborder="0" allowfullscreen></iframe>
 
-[Show me the code](http://github.com/deeplearning4j/dl4j-examples/blob/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/dataExamples/MnistImagePipelineExample.java)
+[Show me the code](http://github.com/eclipse/deeplearning4j-examples/blob/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/dataExamples/MnistImagePipelineExample.java)
 
 ### PreprocessNormalizerExample.java
 
 This example demonstrates preprocessing features available in DataVec.
 
-[Show me the code](https://github.com/deeplearning4j/dl4j-examples/blob/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/dataexamples/PreprocessNormalizerExample.java)
+[Show me the code](https://github.com/eclipse/deeplearning4j-examples/blob/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/dataexamples/PreprocessNormalizerExample.java)
 
 ### CSVExampleEvaluationMetaData.java
 
 DataMeta data tracking - i.e. seeing where data for each example comes from - is useful when tracking down malformed data that causes errors and other issues. This example demostrates the functionality in the RecordMetaData class. 
 
-[Show me the code](https://github.com/deeplearning4j/dl4j-examples/blob/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/dataexamples/CSVExampleEvaluationMetaData.java)
+[Show me the code](https://github.com/eclipse/deeplearning4j-examples/blob/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/dataexamples/CSVExampleEvaluationMetaData.java)
 
 ---
 
@@ -78,13 +78,13 @@ MNIST is the "Hello World" of deep learning. Simple, straightforward, and focuss
 
 This is a Single Layer Perceptron for recognizing digits. Note that this pulls the images from a binary package containing the dataset, a rather special case for data ingestion.
 
-[Show me the code](http://github.com/deeplearning4j/dl4j-examples/blob/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/feedforward/mnist/MLPMnistSingleLayerExample.java)
+[Show me the code](http://github.com/eclipse/deeplearning4j-examples/blob/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/feedforward/mnist/MLPMnistSingleLayerExample.java)
 
 ### MLPMnistTwoLayerExample.java
 
 A two-layer perceptron for MNIST, showing there is more than one useful network for a given dataset. 
 
-[Show me the code](http://github.com/deeplearning4j/dl4j-examples/blob/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/feedforward/mnist/MLPMnistTwoLayerExample.java)
+[Show me the code](http://github.com/eclipse/deeplearning4j-examples/blob/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/feedforward/mnist/MLPMnistTwoLayerExample.java)
 
 ### Feedforward Examples
 
@@ -92,7 +92,7 @@ Data flows through feed-forward neural networks in a single pass from input via
 
 These networks can be used for a wide range of tasks depending on they are configured. Along with image classification over MNIST data, this directory has examples demonstrating regression, classification, and anomoly detection.
 
-[Show me the code](http://github.com/deeplearning4j/dl4j-examples/tree/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/feedforward)
+[Show me the code](http://github.com/eclipse/deeplearning4j-examples/tree/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/feedforward)
 
 ### Convolutional Neural Networks
 
@@ -102,7 +102,7 @@ Convolutional Neural Networks are mainly used for image recognition, although th
 
 This example can be run using either LeNet or AlexNet. 
 
-[Show me the code](http://github.com/deeplearning4j/dl4j-examples/blob/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/convolution/AnimalsClassification.java)
+[Show me the code](http://github.com/eclipse/deeplearning4j-examples/blob/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/convolution/AnimalsClassification.java)
 
 ---
 
@@ -115,7 +115,7 @@ load the model for later training or inference.
 
 This demonstrates saving and loading a network build using the class ComputationGraph.
 
-[Show me the code](http://github.com/deeplearning4j/dl4j-examples/blob/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/misc/modelsaving/SaveLoadComputationGraph.java)
+[Show me the code](http://github.com/eclipse/deeplearning4j-examples/blob/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/misc/modelsaving/SaveLoadComputationGraph.java)
 
 ### SaveLoadMultiLayerNetwork.java
 
@@ -135,11 +135,11 @@ Do you need to add a Loss Function that is not available or prebuilt yet? Check
 
 ### CustomLossExample.java
 
-[Show me the code](http://github.com/deeplearning4j/dl4j-examples/blob/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/misc/lossfunctions/CustomLossExample.java)
+[Show me the code](http://github.com/eclipse/deeplearning4j-examples/blob/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/misc/lossfunctions/CustomLossExample.java)
 
 ### CustomLossL1L2.java
 
-[Show me the code](http://github.com/deeplearning4j/dl4j-examples/blob/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/misc/lossfunctions/CustomLossL1L2.java)
+[Show me the code](http://github.com/eclipse/deeplearning4j-examples/blob/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/misc/lossfunctions/CustomLossL1L2.java)
 
 ### Custom Layer
 
@@ -147,7 +147,7 @@ Do you need to add a layer with features that aren't available in DeepLearning4J
 
 ### CustomLayerExample.java
 
-[Show me the code](http://github.com/deeplearning4j/dl4j-examples/blob/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/misc/customlayers/CustomLayerExample.java)
+[Show me the code](http://github.com/eclipse/deeplearning4j-examples/blob/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/misc/customlayers/CustomLayerExample.java)
 
 ---
 
@@ -159,25 +159,25 @@ Neural Networks for NLP? We have those, too.
 
 Global Vectors for Word Representation are useful for detecting relationships between words. 
 
-[Show me the code](http://github.com/deeplearning4j/dl4j-examples/blob/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/nlp/glove/GloVeExample.java)
+[Show me the code](http://github.com/eclipse/deeplearning4j-examples/blob/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/nlp/glove/GloVeExample.java)
 
 ### Paragraph Vectors
 
 A vectorized representation of words. Described [here](https://cs.stanford.edu/~quocle/paragraph_vector.pdf)
 
-[Show me the code](http://github.com/deeplearning4j/dl4j-examples/blob/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/nlp/paragraphvectors/ParagraphVectorsClassifierExample.java)
+[Show me the code](http://github.com/eclipse/deeplearning4j-examples/blob/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/nlp/paragraphvectors/ParagraphVectorsClassifierExample.java)
 
 ### Sequence Vectors
 
 One way to represent sentences is as a sequence of words. 
 
-[Show me the code](http://github.com/deeplearning4j/dl4j-examples/blob/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/nlp/sequencevectors/SequenceVectorsTextExample.java)
+[Show me the code](http://github.com/eclipse/deeplearning4j-examples/blob/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/nlp/sequencevectors/SequenceVectorsTextExample.java)
 
 ### Word2Vec
 
 Described [here](https://deeplearning4j.org/word2vec.html)
 
-[Show me the code](http://github.com/deeplearning4j/dl4j-examples/blob/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/nlp/word2vec/Word2VecRawTextExample.java)
+[Show me the code](http://github.com/eclipse/deeplearning4j-examples/blob/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/nlp/word2vec/Word2VecRawTextExample.java)
 
 ---
 
@@ -185,7 +185,7 @@ Described [here](https://deeplearning4j.org/word2vec.html)
 
 t-Distributed Stochastic Neighbor Embedding (t-SNE) is useful for data visualization. We include an example in the NLP section since word similarity visualization is a common use. 
 
-[Show me the code](http://github.com/deeplearning4j/dl4j-examples/blob/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/nlp/tsne/TSNEStandardExample.java)
+[Show me the code](http://github.com/eclipse/deeplearning4j-examples/blob/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/nlp/tsne/TSNEStandardExample.java)
 
 ---
 
@@ -199,37 +199,37 @@ The examples folder for Recurrent Neural Networks has the following:
 
 An RNN learns a string of characters.
 
-[Show me the code](http://github.com/deeplearning4j/dl4j-examples/blob/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/recurrent/basic/BasicRNNExample.java)
+[Show me the code](http://github.com/eclipse/deeplearning4j-examples/blob/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/recurrent/basic/BasicRNNExample.java)
 
 ### GravesLSTMCharModellingExample.java
 
 Takes the complete works of Shakespeare as a sequence of characters and Trains a Neural Net to generate "Shakespeare" one character at a time.
 
-[Show me the code](http://github.com/deeplearning4j/dl4j-examples/blob/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/recurrent/character/GravesLSTMCharModellingExample.java)
+[Show me the code](http://github.com/eclipse/deeplearning4j-examples/blob/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/recurrent/character/GravesLSTMCharModellingExample.java)
 
 ### SingleTimestepRegressionExample.java
 
 Regression with an LSTM (Long Short Term Memory) Recurrent Neural Network. 
 
-[Show me the code](http://github.com/deeplearning4j/dl4j-examples/blob/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/recurrent/regression/SingleTimestepRegressionExample.java)
+[Show me the code](http://github.com/eclipse/deeplearning4j-examples/blob/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/recurrent/regression/SingleTimestepRegressionExample.java)
 
 ### AdditionRNN.java
 
 This example trains a neural network to do addition. 
 
-[Show me the code](https://github.com/deeplearning4j/dl4j-examples/blob/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/recurrent/seq2seq/AdditionRNN.java)
+[Show me the code](https://github.com/eclipse/deeplearning4j-examples/blob/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/recurrent/seq2seq/AdditionRNN.java)
 
 ### RegressionMathFunctions.java
 
 This example trains a neural network to perform various math operations. 
 
-[Show me the code](https://github.com/deeplearning4j/dl4j-examples/blob/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/feedforward/regression/RegressionMathFunctions.java)
+[Show me the code](https://github.com/eclipse/deeplearning4j-examples/blob/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/feedforward/regression/RegressionMathFunctions.java)
 
 ### UCISequenceClassificationExample.java
 
 A publicly available dataset of time series data of six classes, cyclic, up-trending, etc. Example of an RNN learning to classify the time series. 
 
-[Show me the code](https://github.com/deeplearning4j/dl4j-examples/blob/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/recurrent/seqclassification/UCISequenceClassificationExample.java)
+[Show me the code](https://github.com/eclipse/deeplearning4j-examples/blob/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/recurrent/seqclassification/UCISequenceClassificationExample.java)
 
 ### VideoClassificationExample.java
 
@@ -237,13 +237,13 @@ How do autonomous vehicles distinguish between a pedestrian, a stop sign and a g
 
 This example is similar, but simplified. It combines convolutional, max pooling, dense (feed forward) and recurrent (LSTM) layers to classify frames in a video. 
 
-[Show me the code](https://github.com/deeplearning4j/dl4j-examples/blob/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/recurrent/video/VideoClassificationExample.java)
+[Show me the code](https://github.com/eclipse/deeplearning4j-examples/blob/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/recurrent/video/VideoClassificationExample.java)
 
 ### SentimentExampleIterator.java
 
 This sentiment analysis example classifies sentiment as positive or negative using word vectors and a Recurrent Neural Network. 
 
-[Show me the code](https://github.com/deeplearning4j/dl4j-examples/blob/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/recurrent/word2vecsentiment/Word2VecSentimentRNN.java)
+[Show me the code](https://github.com/eclipse/deeplearning4j-examples/blob/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/recurrent/word2vecsentiment/Word2VecSentimentRNN.java)
 
 ---
 
@@ -254,13 +254,13 @@ DeepLearning4j supports using a Spark Cluster for network training. Here are the
 ### MnistMLPExample.java
 
 This is an example of a Multi-Layer Perceptron training on the Mnist data set of handwritten digits. 
-[Show me the code](http://github.com/deeplearning4j/dl4j-examples/blob/master/dl4j-spark-examples/dl4j-spark/src/main/java/org/deeplearning4j/mlp/MnistMLPExample.java)
+[Show me the code](http://github.com/eclipse/deeplearning4j-examples/blob/master/dl4j-spark-examples/dl4j-spark/src/main/java/org/deeplearning4j/mlp/MnistMLPExample.java)
 
 ### SparkLSTMCharacterExample.java
 
 An LSTM recurrent Network in Spark. 
 
-[Show me the code](http://github.com/deeplearning4j/dl4j-examples/blob/master/dl4j-spark-examples/dl4j-spark/src/main/java/org/deeplearning4j/rnn/SparkLSTMCharacterExample.java)
+[Show me the code](http://github.com/eclipse/deeplearning4j-examples/blob/master/dl4j-spark-examples/dl4j-spark/src/main/java/org/deeplearning4j/rnn/SparkLSTMCharacterExample.java)
 
 ---
 
@@ -274,7 +274,7 @@ The learning algorithms and loss functions are executed as ND4J operations.
 
 This is a directory with examples for creating and manipulating NDArrays.
 
-[Show me the code](http://github.com/deeplearning4j/dl4j-examples/tree/master/nd4j-examples/src/main/java/org/nd4j/examples)
+[Show me the code](http://github.com/eclipse/deeplearning4j-examples/tree/master/nd4j-examples/src/main/java/org/nd4j/examples)
 
 ---
 
@@ -282,4 +282,4 @@ This is a directory with examples for creating and manipulating NDArrays.
 
 Deep learning algorithms have learned to play Space Invaders and Doom using reinforcement learning. DeepLearning4J/RL4J examples of Reinforcement Learning are available here: 
 
-[Show me the code](http://github.com/deeplearning4j/dl4j-examples/tree/master/rl4j-examples)
\ No newline at end of file
+[Show me the code](http://github.com/eclipse/deeplearning4j-examples/tree/master/rl4j-examples)
\ No newline at end of file
diff --git a/docs/deeplearning4j/templates/quickstart.md b/docs/deeplearning4j/templates/quickstart.md
index 07214592f..bcc042f07 100644
--- a/docs/deeplearning4j/templates/quickstart.md
+++ b/docs/deeplearning4j/templates/quickstart.md
@@ -109,7 +109,7 @@ This can be fixed by running:
 1. Use the command line to enter the following:
 
 ```shell
-$ git clone https://github.com/deeplearning4j/dl4j-examples.git
+$ git clone https://github.com/eclipse/deeplearning4j-examples.git
 $ cd dl4j-examples/
 $ mvn clean install
 ```
@@ -135,9 +135,9 @@ To run DL4J in your own projects, we highly recommend using Maven for Java users
 - `nd4j-native-platform`, the CPU version of the ND4J library that powers DL4J
 - `datavec-api` - Datavec is our library vectorizing and loading data
 
-Every Maven project has a POM file. Here is [how the POM file should appear](https://github.com/deeplearning4j/dl4j-examples/blob/master/pom.xml) when you run your examples.
+Every Maven project has a POM file. Here is [how the POM file should appear](https://github.com/eclipse/deeplearning4j-examples/blob/master/pom.xml) when you run your examples.
 
-Within IntelliJ, you will need to choose the first Deeplearning4j example you're going to run. We suggest `MLPClassifierLinear`, as you will almost immediately see the network classify two groups of data in our UI. The file on [Github can be found here](https://github.com/deeplearning4j/dl4j-examples/blob/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/feedforward/classification/MLPClassifierLinear.java).
+Within IntelliJ, you will need to choose the first Deeplearning4j example you're going to run. We suggest `MLPClassifierLinear`, as you will almost immediately see the network classify two groups of data in our UI. The file on [Github can be found here](https://github.com/eclipse/deeplearning4j-examples/blob/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/feedforward/classification/MLPClassifierLinear.java).
 
 To run the example, right click on it and select the green button in the drop-down menu. You will see, in IntelliJ's bottom window, a series of scores. The rightmost number is the error score for the network's classifications. If your network is learning, then that number will decrease over time with each batch it processes. At the end, this window will tell you how accurate your neural-network model has become:
 
@@ -225,11 +225,11 @@ Deeplearning4j is a framework that lets you pick and choose with everything avai
 
 If you'd like to deploy models to production, you might like our [model import from Keras](./keras-import-get-started).
 
-Deeplearning4j has several submodules. These range from a visualization UI to distributed training on Spark. For an overview of these modules, please look at the [**Deeplearning4j examples on Github**](https://github.com/deeplearning4j/dl4j-examples).
+Deeplearning4j has several submodules. These range from a visualization UI to distributed training on Spark. For an overview of these modules, please look at the [**Deeplearning4j examples on Github**](https://github.com/eclipse/deeplearning4j-examples).
 
-To get started with a simple desktop app, you need two things: An [nd4j backend](http://nd4j.org/backend.html) and `deeplearning4j-core`. For more code, see the [simpler examples submodule](https://github.com/deeplearning4j/dl4j-examples/blob/master/dl4j-examples/pom.xml#L64).
+To get started with a simple desktop app, you need two things: An [nd4j backend](http://nd4j.org/backend.html) and `deeplearning4j-core`. For more code, see the [simpler examples submodule](https://github.com/eclipse/deeplearning4j-examples/blob/master/dl4j-examples/pom.xml#L64).
 
-If you want a flexible deep-learning API, there are two ways to go.  You can use nd4j standalone See our [nd4j examples](https://github.com/deeplearning4j/dl4j-examples/tree/master/nd4j-examples) or the [computation graph API](http://deeplearning4j.org/compgraph).
+If you want a flexible deep-learning API, there are two ways to go.  You can use nd4j standalone See our [nd4j examples](https://github.com/eclipse/deeplearning4j-examples/tree/master/nd4j-examples) or the [computation graph API](http://deeplearning4j.org/compgraph).
 
 If you want distributed training on Spark, you can see our [Spark page](http://deeplearning4j.org/spark)
 Keep in mind that we cannot setup Spark for you. If you want to set up distributed Spark and GPUs, that is largely up to you. Deeplearning4j simply deploys as a JAR file on an existing Spark cluster.
@@ -239,13 +239,13 @@ If you want Spark with GPUs, we recommend [Spark with Mesos](https://spark.apach
 If you want to deploy on mobile, you can see our [Android page](./deeplearning4j-android).
 
 We deploy optimized code for various hardware architectures natively. We use C++ based for loops just like everybody else.
-For that, please see our [C++ framework libnd4j](https://github.com/deeplearning4j/libnd4j).
+For that, please see our [C++ framework libnd4j](https://github.com/eclipse/deeplearning4j/tree/master/libnd4j).
 
 Deeplearning4j has two other notable components:
 
 * [Arbiter: hyperparameter optimization and model evaluation](./arbiter-overview)
 * [DataVec: built-in ETL for machine-learning data pipelines](./datavec-overview)
 
-Deeplearning4j is meant to be an end-to-end platform for building real applications, not just a tensor library with automatic differentiation. If you want a tensor library with autodiff, please see ND4J and [samediff](https://github.com/deeplearning4j/deeplearning4j/tree/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff). Samediff is still in alpha, but if you want to contribute, please join our [live chat on Gitter](https://gitter.im/deeplearning4j/deeplearning4j).
+Deeplearning4j is meant to be an end-to-end platform for building real applications, not just a tensor library with automatic differentiation. If you want a tensor library with autodiff, please see ND4J and [samediff](https://github.com/eclipse/deeplearning4j/tree/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff). Samediff is still in alpha, but if you want to contribute, please join our [live chat on Gitter](https://gitter.im/deeplearning4j/deeplearning4j).
 
 Lastly, if you are benchmarking Deeplearnin4j, please consider coming in to our live chat and asking for tips. Deeplearning4j has [all the knobs](./deeplearning4j-config-gpu-cpu), but some may not work exactly like the Python frameworks to do. You have to build Deeplearning4j from source for some applications.
diff --git a/docs/deeplearning4j/templates/troubleshooting-training.md b/docs/deeplearning4j/templates/troubleshooting-training.md
index 80cb04695..caa689c01 100644
--- a/docs/deeplearning4j/templates/troubleshooting-training.md
+++ b/docs/deeplearning4j/templates/troubleshooting-training.md
@@ -61,7 +61,7 @@ For training neural networks in a distributed manner, you may need a different (
 
 ### Policies and Scheduling
 
-You can optionally define a learning rate policy for your neural network. A policy will change the learning rate over time, achieving better results since the learning rate can "slow down" to find closer local minima for convergence. A common policy used is scheduling. See the [LeNet example](https://github.com/deeplearning4j/dl4j-examples/blob/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/convolution/LenetMnistExample.java) for a learning rate schedule used in practice.
+You can optionally define a learning rate policy for your neural network. A policy will change the learning rate over time, achieving better results since the learning rate can "slow down" to find closer local minima for convergence. A common policy used is scheduling. See the [LeNet example](https://github.com/eclipse/deeplearning4j-examples/blob/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/convolution/LenetMnistExample.java) for a learning rate schedule used in practice.
 
 Note that if you're using multiple GPUs, this will affect your scheduling. For example, if you have 2x GPUs, then you will need to divide the iterations in your schedule by 2, since the throughput of your training process will be double, and the learning rate schedule is only applicable to the local GPU.
 
@@ -109,7 +109,7 @@ A good default choice in most cases is to use the stochastic gradient descent op
 
 ## <a name="normalization">Gradient Normalization</a>
 
-When training a neural network, it can sometimes be helpful to apply gradient normalization, to avoid the gradients being too large (the so-called exploding gradient problem, common in recurrent neural networks) or too small. This can be applied using the .gradientNormalization(GradientNormalization) and .gradientNormalizationThreshould(double) methods. For an example of gradient normalization see, [GradientNormalization.java](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/GradientNormalization.java). The test code for that example is [here](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/nn/updater/TestGradientNormalization.java).
+When training a neural network, it can sometimes be helpful to apply gradient normalization, to avoid the gradients being too large (the so-called exploding gradient problem, common in recurrent neural networks) or too small. This can be applied using the .gradientNormalization(GradientNormalization) and .gradientNormalizationThreshould(double) methods. For an example of gradient normalization see, [GradientNormalization.java](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/GradientNormalization.java). The test code for that example is [here](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/nn/updater/TestGradientNormalization.java).
 
 ## <a name="rnn">Recurrent Neural Networks: Truncated Backpropagation through Time</a>
 
diff --git a/docs/keras-import/templates/activations.md b/docs/keras-import/templates/activations.md
index 2e17a085d..b4b589389 100644
--- a/docs/keras-import/templates/activations.md
+++ b/docs/keras-import/templates/activations.md
@@ -21,4 +21,4 @@ We support all [Keras activation functions](https://keras.io/activations), namel
 * <i class="fa fa-check-square-o"></i> hard_sigmoid
 * <i class="fa fa-check-square-o"></i> linear
 
-The mapping of Keras to DL4J activation functions is defined in [KerasActivationUtils](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/utils/KerasActivationUtils.java)
+The mapping of Keras to DL4J activation functions is defined in [KerasActivationUtils](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/utils/KerasActivationUtils.java)
diff --git a/docs/keras-import/templates/constraints.md b/docs/keras-import/templates/constraints.md
index 9116e76f0..2a1dc7936 100644
--- a/docs/keras-import/templates/constraints.md
+++ b/docs/keras-import/templates/constraints.md
@@ -15,4 +15,4 @@ All [Keras constraints](https://keras.io/constraints) are supported:
 * <i class="fa fa-check-square-o"></i> unit_norm
 * <i class="fa fa-check-square-o"></i> min_max_norm
 
-Mapping Keras to DL4J constraints happens in [KerasConstraintUtils](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/utils/KerasConstraintUtils.java).
+Mapping Keras to DL4J constraints happens in [KerasConstraintUtils](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/utils/KerasConstraintUtils.java).
diff --git a/docs/keras-import/templates/initializers.md b/docs/keras-import/templates/initializers.md
index 8f8ff11f2..780cd1601 100644
--- a/docs/keras-import/templates/initializers.md
+++ b/docs/keras-import/templates/initializers.md
@@ -26,4 +26,4 @@ DL4J supports all available [Keras initializers](https://keras.io/initializers),
 * <i class="fa fa-check-square-o"></i> he_normal
 * <i class="fa fa-check-square-o"></i> he_uniform
 
-The mapping of Keras to DL4J initializers can be found in [KerasInitilizationUtils](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/utils/KerasInitilizationUtils.java).
\ No newline at end of file
+The mapping of Keras to DL4J initializers can be found in [KerasInitilizationUtils](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/utils/KerasInitilizationUtils.java).
\ No newline at end of file
diff --git a/docs/keras-import/templates/losses.md b/docs/keras-import/templates/losses.md
index ba707c04b..c86298129 100644
--- a/docs/keras-import/templates/losses.md
+++ b/docs/keras-import/templates/losses.md
@@ -25,4 +25,4 @@ DL4J supports all available [Keras losses](https://keras.io/losses) (except for
 * <i class="fa fa-check-square-o"></i> poisson
 * <i class="fa fa-check-square-o"></i> cosine_proximity
 
-The mapping of Keras loss functions can be found in [KerasLossUtils](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/utils/KerasLossUtils.java).
\ No newline at end of file
+The mapping of Keras loss functions can be found in [KerasLossUtils](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/utils/KerasLossUtils.java).
\ No newline at end of file
diff --git a/docs/keras-import/templates/overview.md b/docs/keras-import/templates/overview.md
index c45583326..098d21917 100644
--- a/docs/keras-import/templates/overview.md
+++ b/docs/keras-import/templates/overview.md
@@ -8,7 +8,7 @@ weight: 0
 
 ## Deeplearing4j: Keras model import
 
-[Keras model import](https://github.com/deeplearning4j/deeplearning4j/tree/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras) 
+[Keras model import](https://github.com/eclipse/deeplearning4j/tree/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras) 
 provides routines for importing neural network models originally configured and trained 
 using [Keras](https://keras.io/), a popular Python deep learning library. 
 
@@ -61,7 +61,7 @@ Here's how you do training in DL4J for your imported model:
 model.fit(input, output);
 ``` 
 
-The full example just shown can be found in our [DL4J examples](https://github.com/deeplearning4j/dl4j-examples/blob/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/modelimport/keras/basic/SimpleSequentialMlpImport.java).
+The full example just shown can be found in our [DL4J examples](https://github.com/eclipse/deeplearning4j-examples/blob/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/modelimport/keras/basic/SimpleSequentialMlpImport.java).
 
 
 ## Project setup
@@ -78,12 +78,12 @@ dependency to your pom.xml.
 ```
 
 If you need a project to get started in the first place, consider cloning 
-[DL4J examples](https://github.com/deeplearning4j/dl4j-examples) and follow
+[DL4J examples](https://github.com/eclipse/deeplearning4j-examples) and follow
 the instructions in the repository to build the project.
 
 ## Popular models and applications
 
-We support import for a growing number of applications, check [here](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-modelimport/src/test/java/org/deeplearning4j/nn/modelimport/keras/e2e/KerasModelEndToEndTest.java)
+We support import for a growing number of applications, check [here](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-modelimport/src/test/java/org/deeplearning4j/nn/modelimport/keras/e2e/KerasModelEndToEndTest.java)
 for a full list of currently covered models. These applications include
 
 - Deep convolutional and Wasserstein GANs
@@ -105,7 +105,7 @@ Once you have imported your model, we recommend our own `ModelSerializer` class
 saving and reloading of your model.
 
 You can inquire further by visiting the [DL4J gitter channel](https://gitter.im/deeplearning4j/deeplearning4j). You might consider filing 
-a [feature request via Github](https://github.com/deeplearning4j/deeplearning4j/issues) 
+a [feature request via Github](https://github.com/eclipse/deeplearning4j/issues) 
 so that this missing functionality can be placed on the DL4J development roadmap or even 
 sending us a pull request with the necessary changes!
 
diff --git a/docs/keras-import/templates/regularizers.md b/docs/keras-import/templates/regularizers.md
index 01ef7c9f5..4f3cb7d1a 100644
--- a/docs/keras-import/templates/regularizers.md
+++ b/docs/keras-import/templates/regularizers.md
@@ -14,4 +14,4 @@ All [Keras regularizers] are supported by DL4J model import:
 * <i class="fa fa-check-square-o"></i> l2
 * <i class="fa fa-check-square-o"></i> l1_l2
 
-Mapping of regularizers can be found in [KerasRegularizerUtils](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/utils/KerasRegularizerUtils.java).
+Mapping of regularizers can be found in [KerasRegularizerUtils](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/utils/KerasRegularizerUtils.java).
diff --git a/docs/keras-import/templates/supported-features.md b/docs/keras-import/templates/supported-features.md
index 89234f27a..9438b64cd 100644
--- a/docs/keras-import/templates/supported-features.md
+++ b/docs/keras-import/templates/supported-features.md
@@ -15,7 +15,7 @@ Chollet, who's at Google.
 
 While not every concept in DL4J has an equivalent in Keras and vice versa, many of the
 key concepts can be matched. Importing keras models into DL4J is done in
-our [deeplearning4j-modelimport](https://github.com/deeplearning4j/deeplearning4j/tree/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras)
+our [deeplearning4j-modelimport](https://github.com/eclipse/deeplearning4j/tree/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras)
 module. Below is a comprehensive list of currently supported features.
 
 * [Layers](#layers)
@@ -29,73 +29,73 @@ module. Below is a comprehensive list of currently supported features.
 
 
 ## <a name="layers">Layers</a>
-Mapping keras to DL4J layers is done in the [layers](https://github.com/deeplearning4j/deeplearning4j/tree/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers) sub-module of model import. The structure of this project loosely reflects the structure of Keras.
+Mapping keras to DL4J layers is done in the [layers](https://github.com/eclipse/deeplearning4j/tree/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers) sub-module of model import. The structure of this project loosely reflects the structure of Keras.
 
-### [Core Layers](https://github.com/deeplearning4j/deeplearning4j/tree/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/core)
-* <i class="far fa-check-square" style="color:#008000"></i> [Dense](https://github.com/deeplearning4j/deeplearning4j/tree/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/core/KerasDense.java)
-* <i class="far fa-check-square" style="color:#008000"></i> [Activation](https://github.com/deeplearning4j/deeplearning4j/tree/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/core/KerasActivation.java)
-* <i class="far fa-check-square" style="color:#008000"></i> [Dropout](https://github.com/deeplearning4j/deeplearning4j/tree/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/core/KerasDropout.java)
-* <i class="far fa-check-square" style="color:#008000"></i> [Flatten](https://github.com/deeplearning4j/deeplearning4j/tree/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/core/KerasFlatten.java)
-* <i class="far fa-check-square" style="color:#008000"></i> [Reshape](https://github.com/deeplearning4j/deeplearning4j/tree/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/core/KerasReshape.java)
-* <i class="far fa-check-square" style="color:#008000"></i> [Merge](https://github.com/deeplearning4j/deeplearning4j/tree/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/core/KerasMerge.java)
-* <i class="far fa-check-square" style="color:#008000"></i> [Permute](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/core/KerasPermute.java)
-* <i class="far fa-check-square" style="color:#008000"></i> [RepeatVector](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/core/KerasRepeatVector.java)
-* <i class="far fa-check-square" style="color:#008000"></i> [Lambda](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/core/KerasLambda.java)
+### [Core Layers](https://github.com/eclipse/deeplearning4j/tree/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/core)
+* <i class="far fa-check-square" style="color:#008000"></i> [Dense](https://github.com/eclipse/deeplearning4j/tree/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/core/KerasDense.java)
+* <i class="far fa-check-square" style="color:#008000"></i> [Activation](https://github.com/eclipse/deeplearning4j/tree/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/core/KerasActivation.java)
+* <i class="far fa-check-square" style="color:#008000"></i> [Dropout](https://github.com/eclipse/deeplearning4j/tree/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/core/KerasDropout.java)
+* <i class="far fa-check-square" style="color:#008000"></i> [Flatten](https://github.com/eclipse/deeplearning4j/tree/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/core/KerasFlatten.java)
+* <i class="far fa-check-square" style="color:#008000"></i> [Reshape](https://github.com/eclipse/deeplearning4j/tree/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/core/KerasReshape.java)
+* <i class="far fa-check-square" style="color:#008000"></i> [Merge](https://github.com/eclipse/deeplearning4j/tree/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/core/KerasMerge.java)
+* <i class="far fa-check-square" style="color:#008000"></i> [Permute](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/core/KerasPermute.java)
+* <i class="far fa-check-square" style="color:#008000"></i> [RepeatVector](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/core/KerasRepeatVector.java)
+* <i class="far fa-check-square" style="color:#008000"></i> [Lambda](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/core/KerasLambda.java)
 * <i class="fas fa-times" style="color:#FF0000"></i> ActivityRegularization
-* <i class="far fa-check-square" style="color:#008000"></i> [Masking](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/core/KerasMasking.java)
-* <i class="far fa-check-square" style="color:#008000"></i> [SpatialDropout1D](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/core/KerasSpatialDropout.java)
-* <i class="far fa-check-square" style="color:#008000"></i> [SpatialDropout2D](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/core/KerasSpatialDropout.java)
-* <i class="far fa-check-square" style="color:#008000"></i> [SpatialDropout3D](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/core/KerasSpatialDropout.java)
+* <i class="far fa-check-square" style="color:#008000"></i> [Masking](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/core/KerasMasking.java)
+* <i class="far fa-check-square" style="color:#008000"></i> [SpatialDropout1D](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/core/KerasSpatialDropout.java)
+* <i class="far fa-check-square" style="color:#008000"></i> [SpatialDropout2D](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/core/KerasSpatialDropout.java)
+* <i class="far fa-check-square" style="color:#008000"></i> [SpatialDropout3D](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/core/KerasSpatialDropout.java)
 
-### [Convolutional Layers](https://github.com/deeplearning4j/deeplearning4j/tree/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/convolutional)
-* <i class="far fa-check-square" style="color:#008000"></i> [Conv1D](https://github.com/deeplearning4j/deeplearning4j/tree/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/convolutional/KerasConvolution1D.java)
-* <i class="far fa-check-square" style="color:#008000"></i> [Conv2D](https://github.com/deeplearning4j/deeplearning4j/tree/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/convolutional/KerasConvolution2D.java)
-* <i class="far fa-check-square" style="color:#008000"></i> [Conv3D](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/convolutional/KerasConvolution3D.java)
-* <i class="far fa-check-square" style="color:#008000"></i> [AtrousConvolution1D](https://github.com/deeplearning4j/deeplearning4j/tree/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/convolutional/KerasAtrousConvolution1D.java)
-* <i class="far fa-check-square" style="color:#008000"></i> [AtrousConvolution2D](https://github.com/deeplearning4j/deeplearning4j/tree/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/convolutional/KerasAtrousConvolution1D.java)
+### [Convolutional Layers](https://github.com/eclipse/deeplearning4j/tree/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/convolutional)
+* <i class="far fa-check-square" style="color:#008000"></i> [Conv1D](https://github.com/eclipse/deeplearning4j/tree/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/convolutional/KerasConvolution1D.java)
+* <i class="far fa-check-square" style="color:#008000"></i> [Conv2D](https://github.com/eclipse/deeplearning4j/tree/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/convolutional/KerasConvolution2D.java)
+* <i class="far fa-check-square" style="color:#008000"></i> [Conv3D](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/convolutional/KerasConvolution3D.java)
+* <i class="far fa-check-square" style="color:#008000"></i> [AtrousConvolution1D](https://github.com/eclipse/deeplearning4j/tree/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/convolutional/KerasAtrousConvolution1D.java)
+* <i class="far fa-check-square" style="color:#008000"></i> [AtrousConvolution2D](https://github.com/eclipse/deeplearning4j/tree/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/convolutional/KerasAtrousConvolution1D.java)
 * <i class="fas fa-times" style="color:#FF0000"></i> SeparableConv1D
-* <i class="far fa-check-square" style="color:#008000"></i> [SeparableConv2D](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/convolutional/KerasSeparableConvolution2D.java)
-* <i class="far fa-check-square" style="color:#008000"></i> [Conv2DTranspose](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/convolutional/KerasDeconvolution2D.java)
+* <i class="far fa-check-square" style="color:#008000"></i> [SeparableConv2D](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/convolutional/KerasSeparableConvolution2D.java)
+* <i class="far fa-check-square" style="color:#008000"></i> [Conv2DTranspose](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/convolutional/KerasDeconvolution2D.java)
 * <i class="fas fa-times" style="color:#FF0000"></i> Conv3DTranspose
-* <i class="far fa-check-square" style="color:#008000"></i> [Cropping1D](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/convolutional/KerasCropping1D.java)
-* <i class="far fa-check-square" style="color:#008000"></i> [Cropping2D](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/convolutional/KerasCropping2D.java)
-* <i class="far fa-check-square" style="color:#008000"></i> [Cropping3D](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/convolutional/KerasCropping3D.java)
-* <i class="far fa-check-square" style="color:#008000"></i> [UpSampling1D](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/convolutional/KerasUpsampling1D.java)
-* <i class="far fa-check-square" style="color:#008000"></i> [UpSampling2D](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/convolutional/KerasUpsampling2D.java)
-* <i class="far fa-check-square" style="color:#008000"></i> [UpSampling3D](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/convolutional/KerasUpsampling2D.java)
-* <i class="far fa-check-square" style="color:#008000"></i> [ZeroPadding1D](https://github.com/deeplearning4j/deeplearning4j/tree/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/convolutional/KerasZeroPadding1D.java)
-* <i class="far fa-check-square" style="color:#008000"></i> [ZeroPadding2D](https://github.com/deeplearning4j/deeplearning4j/tree/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/convolutional/KerasZeroPadding2D.java)
-* <i class="far fa-check-square" style="color:#008000"></i> [ZeroPadding3D](https://github.com/deeplearning4j/deeplearning4j/tree/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/convolutional/KerasZeroPadding3D.java)
+* <i class="far fa-check-square" style="color:#008000"></i> [Cropping1D](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/convolutional/KerasCropping1D.java)
+* <i class="far fa-check-square" style="color:#008000"></i> [Cropping2D](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/convolutional/KerasCropping2D.java)
+* <i class="far fa-check-square" style="color:#008000"></i> [Cropping3D](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/convolutional/KerasCropping3D.java)
+* <i class="far fa-check-square" style="color:#008000"></i> [UpSampling1D](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/convolutional/KerasUpsampling1D.java)
+* <i class="far fa-check-square" style="color:#008000"></i> [UpSampling2D](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/convolutional/KerasUpsampling2D.java)
+* <i class="far fa-check-square" style="color:#008000"></i> [UpSampling3D](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/convolutional/KerasUpsampling2D.java)
+* <i class="far fa-check-square" style="color:#008000"></i> [ZeroPadding1D](https://github.com/eclipse/deeplearning4j/tree/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/convolutional/KerasZeroPadding1D.java)
+* <i class="far fa-check-square" style="color:#008000"></i> [ZeroPadding2D](https://github.com/eclipse/deeplearning4j/tree/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/convolutional/KerasZeroPadding2D.java)
+* <i class="far fa-check-square" style="color:#008000"></i> [ZeroPadding3D](https://github.com/eclipse/deeplearning4j/tree/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/convolutional/KerasZeroPadding3D.java)
 
-### [Pooling Layers](https://github.com/deeplearning4j/deeplearning4j/tree/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/pooling)
-* <i class="far fa-check-square" style="color:#008000"></i> [MaxPooling1D](https://github.com/deeplearning4j/deeplearning4j/tree/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/pooling/KerasPooling1D.java)
-* <i class="far fa-check-square" style="color:#008000"></i> [MaxPooling2D](https://github.com/deeplearning4j/deeplearning4j/tree/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/pooling/KerasPooling2D.java)
-* <i class="far fa-check-square" style="color:#008000"></i> [MaxPooling3D](https://github.com/deeplearning4j/deeplearning4j/tree/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/pooling/KerasPooling3D.java)
-* <i class="far fa-check-square" style="color:#008000"></i> [AveragePooling1D](https://github.com/deeplearning4j/deeplearning4j/tree/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/pooling/KerasPooling1D.java)
-* <i class="far fa-check-square" style="color:#008000"></i> [AveragePooling2D](https://github.com/deeplearning4j/deeplearning4j/tree/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/pooling/KerasPooling2D.java)
-* <i class="far fa-check-square" style="color:#008000"></i> [AveragePooling3D](https://github.com/deeplearning4j/deeplearning4j/tree/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/pooling/KerasPooling3D.java)
-* <i class="far fa-check-square" style="color:#008000"></i> [GlobalMaxPooling1D](https://github.com/deeplearning4j/deeplearning4j/tree/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/pooling/KerasGlobalPooling.java)
-* <i class="far fa-check-square" style="color:#008000"></i> [GlobalMaxPooling2D](https://github.com/deeplearning4j/deeplearning4j/tree/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/pooling/KerasGlobalPooling.java)
-* <i class="far fa-check-square" style="color:#008000"></i> [GlobalMaxPooling3D](https://github.com/deeplearning4j/deeplearning4j/tree/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/pooling/KerasGlobalPooling.java)
-* <i class="far fa-check-square" style="color:#008000"></i> [GlobalAveragePooling1D](https://github.com/deeplearning4j/deeplearning4j/tree/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/pooling/KerasGlobalPooling.java)
-* <i class="far fa-check-square" style="color:#008000"></i> [GlobalAveragePooling2D](https://github.com/deeplearning4j/deeplearning4j/tree/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/pooling/KerasGlobalPooling.java)
-* <i class="far fa-check-square" style="color:#008000"></i> [GlobalAveragePooling3D](https://github.com/deeplearning4j/deeplearning4j/tree/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/pooling/KerasGlobalPooling.java)
+### [Pooling Layers](https://github.com/eclipse/deeplearning4j/tree/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/pooling)
+* <i class="far fa-check-square" style="color:#008000"></i> [MaxPooling1D](https://github.com/eclipse/deeplearning4j/tree/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/pooling/KerasPooling1D.java)
+* <i class="far fa-check-square" style="color:#008000"></i> [MaxPooling2D](https://github.com/eclipse/deeplearning4j/tree/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/pooling/KerasPooling2D.java)
+* <i class="far fa-check-square" style="color:#008000"></i> [MaxPooling3D](https://github.com/eclipse/deeplearning4j/tree/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/pooling/KerasPooling3D.java)
+* <i class="far fa-check-square" style="color:#008000"></i> [AveragePooling1D](https://github.com/eclipse/deeplearning4j/tree/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/pooling/KerasPooling1D.java)
+* <i class="far fa-check-square" style="color:#008000"></i> [AveragePooling2D](https://github.com/eclipse/deeplearning4j/tree/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/pooling/KerasPooling2D.java)
+* <i class="far fa-check-square" style="color:#008000"></i> [AveragePooling3D](https://github.com/eclipse/deeplearning4j/tree/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/pooling/KerasPooling3D.java)
+* <i class="far fa-check-square" style="color:#008000"></i> [GlobalMaxPooling1D](https://github.com/eclipse/deeplearning4j/tree/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/pooling/KerasGlobalPooling.java)
+* <i class="far fa-check-square" style="color:#008000"></i> [GlobalMaxPooling2D](https://github.com/eclipse/deeplearning4j/tree/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/pooling/KerasGlobalPooling.java)
+* <i class="far fa-check-square" style="color:#008000"></i> [GlobalMaxPooling3D](https://github.com/eclipse/deeplearning4j/tree/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/pooling/KerasGlobalPooling.java)
+* <i class="far fa-check-square" style="color:#008000"></i> [GlobalAveragePooling1D](https://github.com/eclipse/deeplearning4j/tree/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/pooling/KerasGlobalPooling.java)
+* <i class="far fa-check-square" style="color:#008000"></i> [GlobalAveragePooling2D](https://github.com/eclipse/deeplearning4j/tree/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/pooling/KerasGlobalPooling.java)
+* <i class="far fa-check-square" style="color:#008000"></i> [GlobalAveragePooling3D](https://github.com/eclipse/deeplearning4j/tree/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/pooling/KerasGlobalPooling.java)
 
-### [Locally-connected Layers](https://github.com/deeplearning4j/deeplearning4j/tree/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/local)
-* <i class="far fa-check-square" style="color:#008000"></i> [LocallyConnected1D](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/local/KerasLocallyConnected1D.java)
-* <i class="far fa-check-square" style="color:#008000"></i> [LocallyConnected2D](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/local/KerasLocallyConnected2D.java)
+### [Locally-connected Layers](https://github.com/eclipse/deeplearning4j/tree/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/local)
+* <i class="far fa-check-square" style="color:#008000"></i> [LocallyConnected1D](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/local/KerasLocallyConnected1D.java)
+* <i class="far fa-check-square" style="color:#008000"></i> [LocallyConnected2D](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/local/KerasLocallyConnected2D.java)
 
-### [Recurrent Layers](https://github.com/deeplearning4j/deeplearning4j/tree/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/recurrent)
-* <i class="far fa-check-square" style="color:#008000"></i> [SimpleRNN](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/recurrent/KerasSimpleRnn.java)
+### [Recurrent Layers](https://github.com/eclipse/deeplearning4j/tree/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/recurrent)
+* <i class="far fa-check-square" style="color:#008000"></i> [SimpleRNN](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/recurrent/KerasSimpleRnn.java)
 * <i class="fas fa-times" style="color:#FF0000"></i> GRU
-* <i class="far fa-check-square" style="color:#008000"></i> [LSTM](https://github.com/deeplearning4j/deeplearning4j/tree/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/recurrent/KerasLstm.java)
+* <i class="far fa-check-square" style="color:#008000"></i> [LSTM](https://github.com/eclipse/deeplearning4j/tree/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/recurrent/KerasLstm.java)
 * <i class="fas fa-times" style="color:#FF0000"></i> ConvLSTM2D
 
 
-### [Embedding Layers](https://github.com/deeplearning4j/deeplearning4j/tree/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/embeddings)
-* <i class="far fa-check-square" style="color:#008000"></i> [Embedding](https://github.com/deeplearning4j/deeplearning4j/tree/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/embeddings/KerasEmbedding.java)
+### [Embedding Layers](https://github.com/eclipse/deeplearning4j/tree/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/embeddings)
+* <i class="far fa-check-square" style="color:#008000"></i> [Embedding](https://github.com/eclipse/deeplearning4j/tree/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/embeddings/KerasEmbedding.java)
 
-### [Merge Layers](https://github.com/deeplearning4j/deeplearning4j/tree/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/core/KerasMerge.java)
+### [Merge Layers](https://github.com/eclipse/deeplearning4j/tree/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/core/KerasMerge.java)
 * <i class="far fa-check-square" style="color:#008000"></i> Add / add
 * <i class="far fa-check-square" style="color:#008000"></i> Multiply / multiply
 * <i class="far fa-check-square" style="color:#008000"></i> Subtract / subtract
@@ -105,25 +105,25 @@ Mapping keras to DL4J layers is done in the [layers](https://github.com/deeplear
 * <i class="fas fa-times" style="color:#FF0000"></i> Dot / dot
 
 
-### [Advanced Activation Layers](https://github.com/deeplearning4j/deeplearning4j/tree/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/advanced/activations)
-* <i class="far fa-check-square" style="color:#008000"></i> [LeakyReLU](https://github.com/deeplearning4j/deeplearning4j/tree/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/advanced/activations/KerasLeakyReLU.java)
-* <i class="far fa-check-square" style="color:#008000"></i> [PReLU](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/advanced/activations/KerasPReLU.java)
+### [Advanced Activation Layers](https://github.com/eclipse/deeplearning4j/tree/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/advanced/activations)
+* <i class="far fa-check-square" style="color:#008000"></i> [LeakyReLU](https://github.com/eclipse/deeplearning4j/tree/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/advanced/activations/KerasLeakyReLU.java)
+* <i class="far fa-check-square" style="color:#008000"></i> [PReLU](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/advanced/activations/KerasPReLU.java)
 * <i class="far fa-check-square" style="color:#008000"></i> ELU
-* <i class="far fa-check-square" style="color:#008000"></i> [ThresholdedReLU](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/advanced/activations/KerasThresholdedReLU.java)
+* <i class="far fa-check-square" style="color:#008000"></i> [ThresholdedReLU](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/advanced/activations/KerasThresholdedReLU.java)
 
-### [Normalization Layers](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/normalization)
-* <i class="far fa-check-square" style="color:#008000"></i> [BatchNormalization](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/normalization/KerasBatchNormalization.java)
+### [Normalization Layers](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/normalization)
+* <i class="far fa-check-square" style="color:#008000"></i> [BatchNormalization](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/normalization/KerasBatchNormalization.java)
 
 ### Noise Layers
-* <i class="far fa-check-square" style="color:#008000"></i> [GaussianNoise](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/noise/KerasGaussianNoise.java)
-* <i class="far fa-check-square" style="color:#008000"></i> [GaussianDropout](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/noise/KerasGaussianDropout.java)
-* <i class="far fa-check-square" style="color:#008000"></i> [AlphaDropout](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/noise/KerasAlphaDropout.java)
+* <i class="far fa-check-square" style="color:#008000"></i> [GaussianNoise](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/noise/KerasGaussianNoise.java)
+* <i class="far fa-check-square" style="color:#008000"></i> [GaussianDropout](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/noise/KerasGaussianDropout.java)
+* <i class="far fa-check-square" style="color:#008000"></i> [AlphaDropout](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/noise/KerasAlphaDropout.java)
 
 ### Layer Wrappers
 * <i class="fas fa-times" style="color:#FF0000"></i> TimeDistributed
-* <i class="far fa-check-square" style="color:#008000"></i> [Bidirectional](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/wrappers/KerasBidirectional.java)
+* <i class="far fa-check-square" style="color:#008000"></i> [Bidirectional](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/wrappers/KerasBidirectional.java)
 
-## <a name="losses">[Losses](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/utils/KerasLossUtils.java)</a>
+## <a name="losses">[Losses](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/utils/KerasLossUtils.java)</a>
 * <i class="far fa-check-square" style="color:#008000"></i> mean_squared_error
 * <i class="far fa-check-square" style="color:#008000"></i> mean_absolute_error
 * <i class="far fa-check-square" style="color:#008000"></i> mean_absolute_percentage_error
@@ -139,7 +139,7 @@ Mapping keras to DL4J layers is done in the [layers](https://github.com/deeplear
 * <i class="far fa-check-square" style="color:#008000"></i> poisson
 * <i class="far fa-check-square" style="color:#008000"></i> cosine_proximity
 
-## <a name="activations">[Activations](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/utils/KerasActivationUtils.java)</a>
+## <a name="activations">[Activations](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/utils/KerasActivationUtils.java)</a>
 * <i class="far fa-check-square" style="color:#008000"></i> softmax
 * <i class="far fa-check-square" style="color:#008000"></i> elu
 * <i class="far fa-check-square" style="color:#008000"></i> selu
@@ -151,7 +151,7 @@ Mapping keras to DL4J layers is done in the [layers](https://github.com/deeplear
 * <i class="far fa-check-square" style="color:#008000"></i> hard_sigmoid
 * <i class="far fa-check-square" style="color:#008000"></i> linear
 
-## <a name="initializers">[Initializers](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/utils/KerasInitilizationUtils.java)</a>
+## <a name="initializers">[Initializers](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/utils/KerasInitilizationUtils.java)</a>
 * <i class="far fa-check-square" style="color:#008000"></i> Zeros
 * <i class="far fa-check-square" style="color:#008000"></i> Ones
 * <i class="far fa-check-square" style="color:#008000"></i> Constant
@@ -168,18 +168,18 @@ Mapping keras to DL4J layers is done in the [layers](https://github.com/deeplear
 * <i class="far fa-check-square" style="color:#008000"></i> he_normal
 * <i class="far fa-check-square" style="color:#008000"></i> he_uniform
 
-## <a name="regularizers">[Regularizers](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/utils/KerasRegularizerUtils.java)</a>
+## <a name="regularizers">[Regularizers](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/utils/KerasRegularizerUtils.java)</a>
 * <i class="far fa-check-square" style="color:#008000"></i> l1
 * <i class="far fa-check-square" style="color:#008000"></i> l2
 * <i class="far fa-check-square" style="color:#008000"></i> l1_l2
 
-## <a name="constraints">[Constraints](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/utils/KerasConstraintUtils.java)</a>
+## <a name="constraints">[Constraints](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/utils/KerasConstraintUtils.java)</a>
 * <i class="far fa-check-square" style="color:#008000"></i> max_norm
 * <i class="far fa-check-square" style="color:#008000"></i> non_neg
 * <i class="far fa-check-square" style="color:#008000"></i> unit_norm
 * <i class="far fa-check-square" style="color:#008000"></i> min_max_norm
 
-## <a name="optimizers">[Optimizers](https://github.com/deeplearning4j/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/utils/KerasOptimizerUtils.java)</a>
+## <a name="optimizers">[Optimizers](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/utils/KerasOptimizerUtils.java)</a>
 * <i class="far fa-check-square" style="color:#008000"></i> SGD
 * <i class="far fa-check-square" style="color:#008000"></i> RMSprop
 * <i class="far fa-check-square" style="color:#008000"></i> Adagrad
diff --git a/docs/nd4j/templates/overview.md b/docs/nd4j/templates/overview.md
index c5724e4a9..12e61c476 100644
--- a/docs/nd4j/templates/overview.md
+++ b/docs/nd4j/templates/overview.md
@@ -58,7 +58,7 @@ Some concepts you should be familiar with:
 
 In terms of indexing there are a few things to know. First, rows are dimension 0, and columns are dimension 1: thus `INDArray.size(0)` is the number of rows, and `INDArray.size(1)` is the number of columns. Like normal arrays in most programming languages, indexing is zero-based: thus rows have indexes `0` to `INDArray.size(0)-1`, and so on for the other dimensions.
 
-Throughout this document, we'll use the term `NDArray` to refer to the general concept of an n-dimensional array; the term `INDArray` refers specifically to the [Java interface](https://github.com/deeplearning4j/nd4j/blob/master/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ndarray/INDArray.java) that ND4J defines. In practice, these two terms can be used interchangeably.
+Throughout this document, we'll use the term `NDArray` to refer to the general concept of an n-dimensional array; the term `INDArray` refers specifically to the [Java interface](https://github.com/eclipse/deeplearning4j/tree/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ndarray/INDArray.java) that ND4J defines. In practice, these two terms can be used interchangeably.
 
 ### <a name="inmemory">NDArrays: How Are They Stored in Memory?</a>
 
@@ -470,7 +470,7 @@ Note that with the `x.add(y)` operation, the original array `x` is not modified.
 
 ### <a name="opsscalar">Scalar Ops</a>
 
-[Scalar ops](https://github.com/deeplearning4j/nd4j/tree/master/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/scalar) are element-wise operations that also take a scalar (i.e., a number). Examples of scalar ops are  add, max, multiply, set and divide operations (see the previous link for a full list).
+[Scalar ops](https://github.com/eclipse/deeplearning4j/tree/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/scalar) are element-wise operations that also take a scalar (i.e., a number). Examples of scalar ops are  add, max, multiply, set and divide operations (see the previous link for a full list).
 
 A number of the methods such as `INDArray.addi(Number)` and `INDArray.divi(Number)` actually execute scalar ops behind the scenes, so when available, it is more convenient to use these methods.
 
@@ -491,7 +491,7 @@ To execute an element-wise tanh operation directly (on the full NDArray) you can
 `INDArray tanh = Nd4j.getExecutioner().execAndReturn(new Tanh(myArr))`
 As with scalar ops mentioned above, transform operations using the above method are *in-place* operations: that is, the NDArray myArr is modified, and the returned array `tanh` is actually the same object as the input `myArr`. Again, you can use `myArr.dup()` if you want a copy.
 
-The [Transforms class](https://github.com/deeplearning4j/nd4j/blob/master/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/ops/transforms/Transforms.java) also defines some convenience methods, such as: `INDArray tanh = Transforms.tanh(INDArray in,boolean copy);` This is equivalent to the method using `Nd4j.getExecutioner()` above.
+The [Transforms class](https://github.com/eclipse/deeplearning4j/tree/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/ops/transforms/Transforms.java) also defines some convenience methods, such as: `INDArray tanh = Transforms.tanh(INDArray in,boolean copy);` This is equivalent to the method using `Nd4j.getExecutioner()` above.
 
 ### <a name="opsaccum">Accumulation (Reduction) Ops</a>
 
@@ -524,7 +524,7 @@ Accumulations along dimensions also generalize to NDArrays with 3 or more dimens
 
 ### <a name="opsindexaccum">Index Accumulation Ops</a>
 
-[Index accumulation ops](https://github.com/deeplearning4j/nd4j/tree/master/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/indexaccum) are very similar to accumulation ops. The difference is that they return an integer index, instead of a double values.
+[Index accumulation ops](https://github.com/eclipse/deeplearning4j/tree/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/indexaccum) are very similar to accumulation ops. The difference is that they return an integer index, instead of a double values.
 
 Examples of index accumulation ops are IMax (argmax), IMin (argmin) and IAMax (argmax of absolute values).
 
@@ -561,7 +561,7 @@ As with other ops, there are inplace and copy versions. There are also column co
 
 [This section: Forthcoming.]
 
-[Link: Boolean Indexing Unit Tests](https://github.com/deeplearning4j/nd4j/blob/master/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/linalg/indexing/BooleanIndexingTest.java)
+[Link: Boolean Indexing Unit Tests](https://github.com/eclipse/deeplearning4j/tree/master/nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/linalg/indexing/BooleanIndexingTest.java)
 
 
 ## <a name="workspaces">Workspaces</a>
@@ -571,7 +571,7 @@ Workspaces are a feature of ND4J used to improve performance, by means of more e
 For more details on workspaces, see the following links:
 
 * <a href="https://deeplearning4j.org/workspaces">Deeplearning4j Guide to Workspaces</a>
-* <a href="https://github.com/deeplearning4j/dl4j-examples/blob/master/nd4j-examples/src/main/java/org/nd4j/examples/Nd4jEx15_Workspaces.java">Workspaces Examples</a>
+* <a href="https://github.com/eclipse/deeplearning4j-examples/blob/master/nd4j-examples/src/main/java/org/nd4j/examples/Nd4jEx15_Workspaces.java">Workspaces Examples</a>
 
 ### <a name="workspaces-panic">Workspaces: Scope Panic</a>
 
@@ -722,7 +722,7 @@ arrRead = Nd4j.readTxt("tmp.txt");
 arrRead =Nd4j.readNumpy("tmp.csv", ", ");
 ```
 
-The [nd4j-serde](https://github.com/deeplearning4j/nd4j/tree/master/nd4j-serde) directory provides packages for Aeron, base64, camel-routes, gsom, jackson and kryo.  
+The [nd4j-serde](https://github.com/eclipse/deeplearning4j/tree/master/nd4j/nd4j-serde) directory provides packages for Aeron, base64, camel-routes, gsom, jackson and kryo.  
 
 
 ## <a name="quickref">Quick Reference: A Summary Overview of ND4J Methods</a>
@@ -804,7 +804,7 @@ Note: all of these methods return
 
 **Element-Wise Transforms (Tanh, Sigmoid, Sin, Log etc)**:
 
-* Using [Transforms](https://github.com/deeplearning4j/nd4j/blob/master/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/ops/transforms/Transforms.java): `Transforms.sin(INDArray)`, `Transforms.log(INDArray)`, `Transforms.sigmoid(INDArray)` etc
+* Using [Transforms](https://github.com/eclipse/deeplearning4j/tree/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/ops/transforms/Transforms.java): `Transforms.sin(INDArray)`, `Transforms.log(INDArray)`, `Transforms.sigmoid(INDArray)` etc
 * Directly (method 1): `Nd4j.getExecutioner().execAndReturn(new Tanh(INDArray))`
 * Directly (method 2) `Nd4j.getExecutioner().execAndReturn(Nd4j.getOpFactory().createTransform("tanh",INDArray))`
 
diff --git a/docs/nd4j/templates/syntax.md b/docs/nd4j/templates/syntax.md
index 41ba5d080..474700338 100644
--- a/docs/nd4j/templates/syntax.md
+++ b/docs/nd4j/templates/syntax.md
@@ -9,7 +9,7 @@ weight: 10
 
 For the complete nd4j-api index, please consult the [Javadoc](../doc).
 
-There are three types of operations used in ND4J: scalars, transforms and accumulations. We’ll use the word op synonymously with operation. You can see the lists of those three kinds of [ND4J ops under the directories here]( https://github.com/deeplearning4j/nd4j/blob/master/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl
+There are three types of operations used in ND4J: scalars, transforms and accumulations. We’ll use the word op synonymously with operation. You can see the lists of those three kinds of [ND4J ops under the directories here]( https://github.com/eclipse/deeplearning4j/tree/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl
 ). Each Java file in each list is an op.
 
 Most of the ops just take [enums](https://docs.oracle.com/javase/tutorial/java/javaOO/enum.html), or a list of discrete values that you can autocomplete. Activation functions are the exception, because they take strings such as `"relu"` or `"tanh"`.
@@ -49,7 +49,7 @@ As you can see, there are three possible argument types with ND4J ops: inputs, o
 |Transforms.tanh(myArray)|Hyperbolic tangent: a sigmoidal function. This applies elementwise tanh inplace.|
 |Nd4j.getExecutioner().exec(Nd4j.getOpFactory() .createTransform("tanh", myArray))|equivalent to the above|
 
-For other transforms, [please see this page](https://github.com/deeplearning4j/nd4j/blob/master/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/ops/transforms/Transforms.java).
+For other transforms, [please see this page](https://github.com/eclipse/deeplearning4j/tree/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/ops/transforms/Transforms.java).
 
 Here are two examples of performing `z = tanh(x)`, in which the original array `x` is unmodified.
 ```java
diff --git a/docs/samediff/templates/adding-ops.md b/docs/samediff/templates/adding-ops.md
index b5a74e5b5..89806aed4 100644
--- a/docs/samediff/templates/adding-ops.md
+++ b/docs/samediff/templates/adding-ops.md
@@ -10,7 +10,7 @@ weight: 2
 
 ### A quick SameDiff overview
 
-To get started with SameDiff, familiarize yourself with the `autodiff` module of the ND4J API located [here on GitHub.](https://github.com/deeplearning4j/nd4j/tree/master/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff)
+To get started with SameDiff, familiarize yourself with the `autodiff` module of the ND4J API located [here on GitHub.](https://github.com/eclipse/deeplearning4j/tree/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff)
 
 For better or worse, SameDiff code is organized in just a few key places. For basic usage and testing of SameDiff the following modules are key. We'll discuss some of them in more detail in just a bit.
 
@@ -22,7 +22,7 @@ For better or worse, SameDiff code is organized in just a few key places. For ba
 
 ### Differential functions in the `functions` module
 
-See the `functions` module on [GitHub.](https://github.com/deeplearning4j/nd4j/tree/master/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/functions)
+See the `functions` module on [GitHub.](https://github.com/eclipse/deeplearning4j/tree/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/functions)
 
 The central abstraction of the `functions` module is `DifferentialFunction`, which underlies pretty much everything in SameDiff. Mathematically, what we're doing in SameDiff is build a directed acyclic graph whose nodes are differential functions, for which we can compute gradients. In that regard, `DifferentialFunction` makes up a SameDiff graph on a fundamental level.
 
@@ -82,7 +82,7 @@ public List<SDVariable> doDiff(List<SDVariable> grad) {
 
 ### Building and executing graphs in `samediff`
 
-See the `samediff` module on [GitHub.](https://github.com/deeplearning4j/nd4j/tree/master/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/samediff)
+See the `samediff` module on [GitHub.](https://github.com/eclipse/deeplearning4j/tree/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/samediff)
 
 Not surprisingly, this is where the magic happens. This module has the core structures that SameDiff operates with. First, let's have a look at the variables that make up SameDiff operations.
 
@@ -107,7 +107,7 @@ allows you to call `c = a.add(b)` on two SameDiff variables, the result of which
 
 The `SameDiff` class is the main workhorse of the module and brings together most of the concepts discussed so far. A little unfortunately, the inverse is also true and `SameDiff` instances are part of all other SameDiff module abstractions in some way or the other (which is why you've seen it many times already). Generally speaking, `SameDiff` is the main entry point for automatic differentiation and you use it to define a symbolic graph that carries operations on `SDVariable`s. Once built, a SameDiff graph can be run in a few ways, for instance `exec()` and `execAndEndResult()`.
 
-Convince yourself that invoking `SameDiff()` sets up a [million things!]( https://github.com/deeplearning4j/nd4j/blob/master/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/samediff/SameDiff.java#L817-L846) Essentially, `SameDiff` will collect and give you access (in terms of both getters and setters) to
+Convince yourself that invoking `SameDiff()` sets up a [million things!]( https://github.com/eclipse/deeplearning4j/tree/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/samediff/SameDiff.java#L817-L846) Essentially, `SameDiff` will collect and give you access (in terms of both getters and setters) to
 
 - All differential functions for the graph, with all their properties, which can be accessed in various ways (e.g. name or id).
 - All inputs and output information for said functions.
@@ -142,54 +142,54 @@ SDVariable inMul2 = in.mul(2.0);
 sd.exec();
 ```
 
-This example is taken from [SameDiffTests](https://github.com/deeplearning4j/nd4j/blob/4c00b19ad4972399264233b6f0b0f5a22493235b/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/autodiff/samediff/SameDiffTests.java), one of the main test sources, in which you also find a few complete end-to-end examples.
+This example is taken from [SameDiffTests](https://github.com/eclipse/deeplearning4j/tree/master/nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/autodiff/samediff/SameDiffTests.java), one of the main test sources, in which you also find a few complete end-to-end examples.
 
-The second place you find tests is in [gradcheck](https://github.com/deeplearning4j/nd4j/tree/4c00b19ad4972399264233b6f0b0f5a22493235b/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/autodiff/gradcheck). Whenever you add a new operation to SameDiff, add tests for the forward pass and gradient checks as well.
+The second place you find tests is in [gradcheck](https://github.com/eclipse/deeplearning4j/tree/master/nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/autodiff/gradcheck). Whenever you add a new operation to SameDiff, add tests for the forward pass and gradient checks as well.
 
-The third set of relevant tests is stored in [imports](https://github.com/deeplearning4j/nd4j/tree/20e3d53dbcd56a14dd1b7572dd52d5e200e9a4ba/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/imports) and contains test for importing TensorFlow and ONNX graphs. On a side note, the resources for these import tests are generated in our [TFOpsTests](https://github.com/deeplearning4j/TFOpTests) project.
+The third set of relevant tests is stored in [imports](https://github.com/eclipse/deeplearning4j/tree/master/nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/imports) and contains test for importing TensorFlow and ONNX graphs. On a side note, the resources for these import tests are generated in our [TFOpsTests](https://github.com/deeplearning4j/TFOpTests) project.
 
 ### Creating and exposing new SameDiff ops
 
-We've seen how ND4J operations get picked up by `DifferentialFunctionFactory` and `SameDiff` to expose them to SameDiff at various levels. As for actually implementing these ops, you need to know a few things. In libnd4j you find two classes of operations, which are described [here](https://github.com/deeplearning4j/libnd4j/blob/5dea2d228c61cdec7535d1c0c6aa093a15fef9fa/AddingNewOps.md) in detail. We'll show how to implement both op types.
+We've seen how ND4J operations get picked up by `DifferentialFunctionFactory` and `SameDiff` to expose them to SameDiff at various levels. As for actually implementing these ops, you need to know a few things. In libnd4j you find two classes of operations, which are described [here](https://github.com/eclipse/deeplearning4j/blob/master/libnd4j/AddingNewOps.md) in detail. We'll show how to implement both op types.
 
-All operations go [here](https://github.com/deeplearning4j/nd4j/tree/master/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl), and most of the time it's obvious where exactly to put the ops. Special attention goes to `layers`, which is reserved for deep learning layer implementations (like [`Conv2D`](https://github.com/deeplearning4j/nd4j/blob/master/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/layers/convolution/Conv2D.java)). These higher-level ops are based on the concept of [Modules](https://github.com/deeplearning4j/nd4j/blob/master/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/BaseModule.java), similar to modules in pytorch or layers in TensorFlow. These layer op implementation also provide a source of more involved op implementations.
+All operations go [here](https://github.com/eclipse/deeplearning4j/tree/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl), and most of the time it's obvious where exactly to put the ops. Special attention goes to `layers`, which is reserved for deep learning layer implementations (like [`Conv2D`](https://github.com/eclipse/deeplearning4j/tree/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/layers/convolution/Conv2D.java)). These higher-level ops are based on the concept of [Modules](https://github.com/eclipse/deeplearning4j/tree/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/BaseModule.java), similar to modules in pytorch or layers in TensorFlow. These layer op implementation also provide a source of more involved op implementations.
 
 #### Implementing legacy operations
 
-Legacy (or XYZ) operations are the old breed of ND4J operations with a characteristic "xyz" signature. Here's how to implement cosine in ND4J by wrapping the `cos` legacy op from libn4j: [Cosine implementation](https://github.com/deeplearning4j/nd4j/blob/master/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/transforms/Cos.java#L38-L72). When it comes to SameDiff, the good thing about legacy ops is that they're already available in ND4J, but need to be augmented by SameDiff specific functionality to pass the muster. Since the cosine function does not have any properties, this implementation is straightforward. The parts that make this op SameDiff compliant are:
+Legacy (or XYZ) operations are the old breed of ND4J operations with a characteristic "xyz" signature. Here's how to implement cosine in ND4J by wrapping the `cos` legacy op from libn4j: [Cosine implementation](https://github.com/eclipse/deeplearning4j/tree/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/transforms/Cos.java#L38-L72). When it comes to SameDiff, the good thing about legacy ops is that they're already available in ND4J, but need to be augmented by SameDiff specific functionality to pass the muster. Since the cosine function does not have any properties, this implementation is straightforward. The parts that make this op SameDiff compliant are:
 
-- You specify SameDiff constructors [here](https://github.com/deeplearning4j/nd4j/blob/master/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/transforms/Cos.java#L38-L51)
-- You implement `doDiff` [here] (https://github.com/deeplearning4j/nd4j/blob/master/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/transforms/Cos.java#L38-L51)
-- You specify a SameDiff `opName`, a TensorFlow `tensorflowName` and an ONNX `onnxName` [here](https://github.com/deeplearning4j/nd4j/blob/master/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/transforms/Cos.java#L74-L93).
+- You specify SameDiff constructors [here](https://github.com/eclipse/deeplearning4j/tree/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/transforms/Cos.java#L38-L51)
+- You implement `doDiff` [here] (https://github.com/eclipse/deeplearning4j/tree/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/transforms/Cos.java#L38-L51)
+- You specify a SameDiff `opName`, a TensorFlow `tensorflowName` and an ONNX `onnxName` [here](https://github.com/eclipse/deeplearning4j/tree/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/transforms/Cos.java#L74-L93).
 
-If you look closely, this is only part of the truth, since `Cos` extends `BaseTransformOp`, which implements other SameDiff functionality. (Note that `BaseTransformOp` is a [`BaseOp`](https://github.com/deeplearning4j/nd4j/blob/master/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/BaseOp.java), which extends `DifferentialFunction` from earlier.) For instance, `calculateOutputShape` is [implemented there](https://github.com/deeplearning4j/nd4j/blob/master/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/BaseTransformOp.java#L195-L207). If you want to implement a new transform, you can simply inherit from `BaseTransformOp`, too. For other op types like reductions etc. there are op base classes available as well, meaning you only need to address the three bullet points above.
+If you look closely, this is only part of the truth, since `Cos` extends `BaseTransformOp`, which implements other SameDiff functionality. (Note that `BaseTransformOp` is a [`BaseOp`](https://github.com/eclipse/deeplearning4j/tree/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/BaseOp.java), which extends `DifferentialFunction` from earlier.) For instance, `calculateOutputShape` is [implemented there](https://github.com/eclipse/deeplearning4j/tree/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/BaseTransformOp.java#L195-L207). If you want to implement a new transform, you can simply inherit from `BaseTransformOp`, too. For other op types like reductions etc. there are op base classes available as well, meaning you only need to address the three bullet points above.
 
 In the rare case you need to write a legacy op from scratch, you'll have to find the respective op number from libn4j, which can be found in `legacy_ops.h`.
 
 #### Implementing Dynamic Custom Operations
 
-[`DynamicCustomOp`](https://github.com/deeplearning4j/nd4j/blob/master/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/DynamicCustomOp.java) is the new kind of operation from libnd4j and all recent additions are implemented as such. This operation type in ND4J directly extends `DifferentialFunction`.
+[`DynamicCustomOp`](https://github.com/eclipse/deeplearning4j/tree/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/DynamicCustomOp.java) is the new kind of operation from libnd4j and all recent additions are implemented as such. This operation type in ND4J directly extends `DifferentialFunction`.
 
-[Here's](https://github.com/deeplearning4j/nd4j/blob/master/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/transforms/BatchToSpace.java) an example of the `BatchToSpace` operation, which inherits from `DynamicCustomOp`:
+[Here's](https://github.com/eclipse/deeplearning4j/tree/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/transforms/BatchToSpace.java) an example of the `BatchToSpace` operation, which inherits from `DynamicCustomOp`:
 
-- BatchToSpace is [initialized](https://github.com/deeplearning4j/nd4j/blob/master/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/transforms/BatchToSpace.java#L49-L67) with two properties, `blocks` and `crops`. Note how `blocks` and `crops`, which are both of integer type, get added to _integer arguments_ for the operation by calling `addIArgument`. For float arguments and other _types_, use `addTArgument` instead.
-- The operation gets its own name and [names for import](https://github.com/deeplearning4j/nd4j/blob/master/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/transforms/BatchToSpace.java#L69-L82),
-- and `doDiff` is [implemented](https://github.com/deeplearning4j/nd4j/blob/master/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/transforms/BatchToSpace.java#L84-L89).
+- BatchToSpace is [initialized](https://github.com/eclipse/deeplearning4j/tree/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/transforms/BatchToSpace.java#L49-L67) with two properties, `blocks` and `crops`. Note how `blocks` and `crops`, which are both of integer type, get added to _integer arguments_ for the operation by calling `addIArgument`. For float arguments and other _types_, use `addTArgument` instead.
+- The operation gets its own name and [names for import](https://github.com/eclipse/deeplearning4j/tree/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/transforms/BatchToSpace.java#L69-L82),
+- and `doDiff` is [implemented](https://github.com/eclipse/deeplearning4j/tree/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/transforms/BatchToSpace.java#L84-L89).
 
-The BatchToSpace operation is then integrated into `DifferentialFunctionFactory` [here](https://github.com/deeplearning4j/nd4j/blob/master/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/functions/DifferentialFunctionFactory.java#L840-L844), exposed to `SameDiff` [here](https://github.com/deeplearning4j/nd4j/blob/master/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/samediff/SameDiff.java#L2105-L2107) and tested [here](https://github.com/deeplearning4j/nd4j/blob/4c00b19ad4972399264233b6f0b0f5a22493235b/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/autodiff/gradcheck/GradCheckTransforms.java#L151-L191).
+The BatchToSpace operation is then integrated into `DifferentialFunctionFactory` [here](https://github.com/eclipse/deeplearning4j/tree/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/functions/DifferentialFunctionFactory.java#L840-L844), exposed to `SameDiff` [here](https://github.com/eclipse/deeplearning4j/tree/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/samediff/SameDiff.java#L2105-L2107) and tested [here](https://github.com/eclipse/deeplearning4j/tree/master/nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/autodiff/gradcheck/GradCheckTransforms.java#L151-L191).
 
-The only thing BatchToSpace is currently missing is _property mapping_. We call the properties for this operation `blocks` and `crops`, but in ONNX or TensorFlow they might be called and stored quite differently. To look up the differences for mappings this correctly, see [`ops.proto`](https://github.com/deeplearning4j/nd4j/blob/master/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/resources/ops.proto) for TensorFlow and [`onnxops.json`](https://github.com/deeplearning4j/nd4j/blob/master/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/resources/onnxops.json) for ONNX.
+The only thing BatchToSpace is currently missing is _property mapping_. We call the properties for this operation `blocks` and `crops`, but in ONNX or TensorFlow they might be called and stored quite differently. To look up the differences for mappings this correctly, see [`ops.proto`](https://github.com/eclipse/deeplearning4j/tree/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/resources/ops.proto) for TensorFlow and [`onnxops.json`](https://github.com/eclipse/deeplearning4j/tree/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/resources/onnxops.json) for ONNX.
 
 
-Let's look at another operation that does property mapping right, namely [`DynamicPartition`](https://github.com/deeplearning4j/nd4j/blob/master/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/transforms/DynamicPartition.java). This op has precisely one property, called `numPartitions` in SameDiff. To map and use this property, you do the following:
+Let's look at another operation that does property mapping right, namely [`DynamicPartition`](https://github.com/eclipse/deeplearning4j/tree/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/transforms/DynamicPartition.java). This op has precisely one property, called `numPartitions` in SameDiff. To map and use this property, you do the following:
 
-- Implement a little helper method called [`addArgs`](https://github.com/deeplearning4j/nd4j/blob/master/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/transforms/DynamicPartition.java#L59-L61) that is used in the constructor of the op and in an import helper one-liner that we're discussing next. It's not necessary, but encouraged to do this and call it `addArgs` consistently, for clarity.
-- Override [`initFromTensorFlow` method](https://github.com/deeplearning4j/nd4j/blob/master/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/transforms/DynamicPartition.java#L63-L67) that maps properties for us using a `TFGraphMapper` instance and adding arguments with `addArgs`. Note that since ONNX does not support dynamic partitioning at the time of this writing (hence no `onnxName`) there's also no `initFromOnnx` method, which works pretty much the same way as `initFromTensorFlow`.
-- For the TensorFlow import to work, we also need to [override `mappingsForFunction`](https://github.com/deeplearning4j/nd4j/blob/master/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/transforms/DynamicPartition.java#L70-L83). This example of a mapping is very simple, all it does is map TensorFlow's property name `num_partititions` to our name `numPartitions`.
+- Implement a little helper method called [`addArgs`](https://github.com/eclipse/deeplearning4j/tree/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/transforms/DynamicPartition.java#L59-L61) that is used in the constructor of the op and in an import helper one-liner that we're discussing next. It's not necessary, but encouraged to do this and call it `addArgs` consistently, for clarity.
+- Override [`initFromTensorFlow` method](https://github.com/eclipse/deeplearning4j/tree/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/transforms/DynamicPartition.java#L63-L67) that maps properties for us using a `TFGraphMapper` instance and adding arguments with `addArgs`. Note that since ONNX does not support dynamic partitioning at the time of this writing (hence no `onnxName`) there's also no `initFromOnnx` method, which works pretty much the same way as `initFromTensorFlow`.
+- For the TensorFlow import to work, we also need to [override `mappingsForFunction`](https://github.com/eclipse/deeplearning4j/tree/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/transforms/DynamicPartition.java#L70-L83). This example of a mapping is very simple, all it does is map TensorFlow's property name `num_partititions` to our name `numPartitions`.
 
 Note that while `DynamicPartition` has proper property mapping, it currently does not have a working `doDiff` implementation.
 
-As a last example, we show one that has a little more interesting property mapping setup, namely [`Dilation2D`](https://github.com/deeplearning4j/nd4j/blob/master/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/transforms/Dilation2D.java). Not only has this op far more properties to map, as you can see in [`mappingsForFunction`](https://github.com/deeplearning4j/nd4j/blob/master/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/transforms/Dilation2D.java#L59-L104), the properties also come with _property values_, as defined in [`attributeAdaptersForFunction`](https://github.com/deeplearning4j/nd4j/blob/master/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/transforms/Dilation2D.java#L106-L132). We've chosen to show this op because it is one that has property mapping, but is neither exposed to `DifferentialFunctionFactory` not `SameDiff`.
+As a last example, we show one that has a little more interesting property mapping setup, namely [`Dilation2D`](https://github.com/eclipse/deeplearning4j/tree/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/transforms/Dilation2D.java). Not only has this op far more properties to map, as you can see in [`mappingsForFunction`](https://github.com/eclipse/deeplearning4j/tree/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/transforms/Dilation2D.java#L59-L104), the properties also come with _property values_, as defined in [`attributeAdaptersForFunction`](https://github.com/eclipse/deeplearning4j/tree/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/transforms/Dilation2D.java#L106-L132). We've chosen to show this op because it is one that has property mapping, but is neither exposed to `DifferentialFunctionFactory` not `SameDiff`.
 
 Hence, the three `DynamicCustomOp` examples shown each come with their own defects and represent examples of the work that has to be done for SameDiff. To summarize, to add a new SameDiff op you need to:
 
diff --git a/gym-java-client/.github/CONTRIBUTING.md b/gym-java-client/.github/CONTRIBUTING.md
deleted file mode 100644
index d6cdef83e..000000000
--- a/gym-java-client/.github/CONTRIBUTING.md
+++ /dev/null
@@ -1,15 +0,0 @@
-## Contribute
-
-1. Check for open issues, or open a new issue to start a discussion around a feature idea or a bug.
-2. If you feel uncomfortable or uncertain about an issue or your changes, feel free to contact us on Gitter using the link above.
-3. Fork [the repository](https://github.com/deeplearning4j/gym-java-client.git) on GitHub to start making your changes to the **master** branch (or branch off of it).
-4. Write a test, which shows that the bug was fixed or that the feature works as expected.
-5. Note the repository follows
-   the [Google Java style](https://google.github.io/styleguide/javaguide.html)
-   with two modifications: 120-char column wrap and 4-spaces indentation. You
-   can format your code to this format by typing `mvn formatter:format` in the
-   subproject you work on, by using the `contrib/formatter.xml` at the root of
-   the repository to configure the Eclipse formatter, or by [using the INtellij
-   plugin](https://github.com/HPI-Information-Systems/Metanome/wiki/Installing-the-google-styleguide-settings-in-intellij-and-eclipse).
-
-6. Send a pull request, and bug us on Gitter until it gets merged and published.
diff --git a/gym-java-client/.github/ISSUE_TEMPLATE.md b/gym-java-client/.github/ISSUE_TEMPLATE.md
deleted file mode 100644
index 9dd585d22..000000000
--- a/gym-java-client/.github/ISSUE_TEMPLATE.md
+++ /dev/null
@@ -1,19 +0,0 @@
-#### Issue Description
-
-Please describe your issue, along with:
-- expected behavior
-- encountered behavior
-
-#### Version Information
-
-Please indicate relevant versions, including, if relevant:
-
-* Deeplearning4j version
-* platform information (OS, etc)
-* CUDA version, if used
-* NVIDIA driver version, if in use
-
-#### Contributing
-
-If you'd like to help us fix the issue by contributing some code, but would
-like guidance or help in doing so, please mention it!
diff --git a/gym-java-client/.github/PULL_REQUEST_TEMPLATE.md b/gym-java-client/.github/PULL_REQUEST_TEMPLATE.md
deleted file mode 100644
index 1f92bacd6..000000000
--- a/gym-java-client/.github/PULL_REQUEST_TEMPLATE.md
+++ /dev/null
@@ -1,10 +0,0 @@
-## What changes were proposed in this pull request?
-
-(Please fill in changes proposed in this fix)
-
-## How was this patch tested?
-
-(Please explain how this patch was tested. E.g. unit tests, integration tests, manual tests)
-
-Please review
-https://github.com/deeplearning4j/deeplearning4j/blob/master/CONTRIBUTING.md before opening a pull request.
diff --git a/jumpy/README.md b/jumpy/README.md
index 132381f86..b18179024 100644
--- a/jumpy/README.md
+++ b/jumpy/README.md
@@ -2,7 +2,7 @@ Jumpy: Python interface for [nd4j](https://nd4j.org)
 ===========================================
 
 [![Join the chat at https://gitter.im/deeplearning4j/deeplearning4j](https://badges.gitter.im/Join%20Chat.svg)](https://gitter.im/deeplearning4j/deeplearning4j?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge)
-[![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://github.com/deeplearning4j/deeplearning4j/blob/master/jumpy/LICENSE)
+[![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://github.com/eclipse/deeplearning4j/blob/master/jumpy/LICENSE)
 [![PyPI version](https://badge.fury.io/py/jumpy.svg)](https://badge.fury.io/py/jumpy)
 
 Jumpy allows you to use ND4J from Python _without any network communication_. Many other Python libraries bridging Java
diff --git a/libnd4j/RaspberryPi.md b/libnd4j/RaspberryPi.md
index 184f43f98..6aee42014 100644
--- a/libnd4j/RaspberryPi.md
+++ b/libnd4j/RaspberryPi.md
@@ -16,7 +16,7 @@ Please follow following instructions to build nd4j for raspberry PI:
 
     ```
     $ cd $HOME
-    $ git clone https://github.com/deeplearning4j/deeplearning4j.git
+    $ git clone https://github.com/eclipse/deeplearning4j.git
     ```
 
 3. build libnd4j:
diff --git a/libnd4j/UnderstandingGraph.md b/libnd4j/UnderstandingGraph.md
index 065e661c0..7e2231c08 100644
--- a/libnd4j/UnderstandingGraph.md
+++ b/libnd4j/UnderstandingGraph.md
@@ -13,7 +13,7 @@ delete graph;
 ```
 
 ### FlatBuffers schemas
-You can find scheme files [here](https://github.com/deeplearning4j/libnd4j/tree/master/include/graph/scheme).
+You can find scheme files [here](https://github.com/eclipse/deeplearning4j/tree/master/libnd4j/include/graph/scheme).
 
 At this moment libnd4j repo contains compiled definitions for C++, Python, Java, and JSON, but FlatBuffers can be compiled for PHP, C#, JavaScript, TypeScript and Go as well. Please refer to `flatc` instructions to do that.
 
diff --git a/libnd4j/blas/NDArray.h b/libnd4j/blas/NDArray.h
index 3a57fc92b..21eedc665 100644
--- a/libnd4j/blas/NDArray.h
+++ b/libnd4j/blas/NDArray.h
@@ -1770,7 +1770,7 @@ NDArray NDArray::operator()(const Nd4jLong i) const {
     } else {
         Nd4jLong idx[MAX_RANK];
         shape::ind2subC(rankOf(), shapeOf(), i, idx);
-        auto xOffset = shape::getOffset(0, shapeOf(), stridesOf(), idx, rankOf());
+        auto xOffset = shape::getOffset(getShapeInfo(), idx);
 
         auto cast = reinterpret_cast<int8_t *>(_buffer) + (xOffset * this->sizeOfT());
         NDArray result(cast, nd4j::ShapeBuilders::createScalarShapeInfo(this->dataType(), this->getWorkspace()));
@@ -1801,7 +1801,7 @@ NDArray& NDArray::operator()(const Nd4jLong i) {
     } else {
         Nd4jLong idx[MAX_RANK];
         shape::ind2subC(rankOf(), shapeOf(), i, idx);
-        auto xOffset = shape::getOffset(0, shapeOf(), stridesOf(), idx, rankOf());
+        auto xOffset = shape::getOffset(getShapeInfo(), idx);
 
         auto cast = reinterpret_cast<int8_t *>(_buffer) + (xOffset * this->sizeOfT());
         NDArray result(cast, nd4j::ShapeBuilders::createScalarShapeInfo(this->dataType(), this->getWorkspace()));
@@ -1818,7 +1818,7 @@ NDArray NDArray::operator()(const Nd4jLong i, const Nd4jLong j) const {
        throw std::invalid_argument("NDArray::operator(i,j): one of input indexes is out of array length or rank!=2 !");
 
     Nd4jLong coords[2] = {i, j};
-    auto xOffset = shape::getOffset(0, shapeOf(), stridesOf(), coords, rankOf());
+    auto xOffset = shape::getOffset(getShapeInfo(), coords);
 
     // TODO: do we really want a view here?
     auto cast = reinterpret_cast<int8_t *>(_buffer) + (xOffset * this->sizeOfT());
@@ -1834,7 +1834,7 @@ NDArray& NDArray::operator()(const Nd4jLong  i, const Nd4jLong j) {
        throw std::invalid_argument("NDArray::operator(i,j): one of input indexes is out of array length or rank!=2 !");
 
     Nd4jLong coords[2] = {i, j};
-    auto xOffset = shape::getOffset(0, shapeOf(), stridesOf(), coords, rankOf());
+    auto xOffset = shape::getOffset(getShapeInfo(), coords);
 
     auto cast = reinterpret_cast<int8_t *>(_buffer) + (xOffset * this->sizeOfT());
     NDArray result(cast, nd4j::ShapeBuilders::createScalarShapeInfo(this->dataType(), this->getWorkspace()));
@@ -1853,7 +1853,7 @@ NDArray NDArray::operator()(const Nd4jLong i, const Nd4jLong j, const Nd4jLong k
        throw std::invalid_argument("NDArray::operator(i,j,k): one of input indexes is out of array length or rank!=3 !");
 
     Nd4jLong coords[3] = {i, j, k};
-    auto xOffset = shape::getOffset(0, shapeOf(), stridesOf(), coords, rankOf());
+    auto xOffset = shape::getOffset(getShapeInfo(), coords);
 
     auto cast = reinterpret_cast<int8_t *>(_buffer) + (xOffset * this->sizeOfT());
     NDArray result(cast, nd4j::ShapeBuilders::createScalarShapeInfo(this->dataType(), this->getWorkspace()));
@@ -1870,7 +1870,7 @@ NDArray& NDArray::operator()(const Nd4jLong i, const Nd4jLong j, const Nd4jLong
        throw std::invalid_argument("NDArray::operator(i,j,k): one of input indexes is out of array length or rank!=3 !");
 
     Nd4jLong coords[3] = {i, j, k};
-    auto xOffset = shape::getOffset(0, shapeOf(), stridesOf(), coords, rankOf());
+    auto xOffset = shape::getOffset(getShapeInfo(), coords);
 
     auto cast = reinterpret_cast<int8_t *>(_buffer) + (xOffset * this->sizeOfT());
     NDArray result(cast, nd4j::ShapeBuilders::createScalarShapeInfo(this->dataType(), this->getWorkspace()));
@@ -1886,7 +1886,7 @@ NDArray NDArray::operator()(const Nd4jLong t, const Nd4jLong u, const Nd4jLong v
        throw std::invalid_argument("NDArray::operator(t,u,v,w): one of input indexes is out of array length or rank!=4 !");
 
     Nd4jLong coords[4] = {t, u, v, w};
-    auto xOffset = shape::getOffset(0, shapeOf(), stridesOf(), coords, rankOf());
+    auto xOffset = shape::getOffset(getShapeInfo(), coords);
 
     auto cast = reinterpret_cast<int8_t *>(_buffer) + (xOffset * this->sizeOfT());
     NDArray result(cast, nd4j::ShapeBuilders::createScalarShapeInfo(this->dataType(), this->getWorkspace()));
@@ -1900,7 +1900,7 @@ NDArray& NDArray::operator()(const Nd4jLong t, const Nd4jLong u, const Nd4jLong
        throw std::invalid_argument("NDArray::operator(t,u,v,w): one of input indexes is out of array length or rank!=4 !");
 
     Nd4jLong coords[4] = {t, u, v, w};
-    auto xOffset = shape::getOffset(0, shapeOf(), stridesOf(), coords, rankOf());
+    auto xOffset = shape::getOffset(getShapeInfo(), coords);
 
     // FIXME
     auto cast = reinterpret_cast<int8_t *>(_buffer) + (xOffset * this->sizeOfT());
@@ -1916,7 +1916,7 @@ NDArray NDArray::operator()(const Nd4jLong* idx) const {
         if (idx[i] >= sizeAt(i))
             throw std::invalid_argument("NDArray::operator(const Nd4jLong* idx): input index is out of dimension length !");
 
-    auto xOffset = shape::getOffset(0, shapeOf(), stridesOf(), idx, rankOf());
+    auto xOffset = shape::getOffset(getShapeInfo(), idx);
 
     auto cast = reinterpret_cast<int8_t *>(_buffer) + (xOffset * this->sizeOfT());
     NDArray result(cast, nd4j::ShapeBuilders::createScalarShapeInfo(this->dataType(), this->getWorkspace()));
@@ -1931,7 +1931,7 @@ NDArray& NDArray::operator()(const Nd4jLong* idx) {
         if (idx[i] >= sizeAt(i))
             throw std::invalid_argument("NDArray::operator(const Nd4jLong* idx): input index is out of dimension length !");
 
-    auto xOffset = shape::getOffset(0, shapeOf(), stridesOf(), idx, rankOf());
+    auto xOffset = shape::getOffset(getShapeInfo(), idx);
 
     auto cast = reinterpret_cast<int8_t *>(_buffer) + (xOffset * this->sizeOfT());
     NDArray result(cast, nd4j::ShapeBuilders::createScalarShapeInfo(this->dataType(), this->getWorkspace()));
@@ -2067,7 +2067,7 @@ T& NDArray::t(const Nd4jLong i, const Nd4jLong j) {
         syncToHost();
 
     Nd4jLong coords[2] = {i, j};
-    auto offset = shape::getOffset(0, shapeOf(), stridesOf(), coords, rankOf());
+    auto offset = shape::getOffset(getShapeInfo(), coords);
     tickWriteHost();
     return *(reinterpret_cast<T*>(bufferWithOffset(offset)));
 }
@@ -2084,7 +2084,7 @@ T& NDArray::t(const Nd4jLong i, const Nd4jLong j, const Nd4jLong k) {
         syncToHost();
 
     Nd4jLong coords[3] = {i, j, k};
-    auto offset = shape::getOffset(0, shapeOf(), stridesOf(), coords, rankOf());
+    auto offset = shape::getOffset(getShapeInfo(), coords);
     tickWriteHost();
     return *(reinterpret_cast<T*>(bufferWithOffset(offset)));
 }
@@ -2118,7 +2118,7 @@ T NDArray::t(const Nd4jLong i, const Nd4jLong j) const {
         syncToHost();
 
     Nd4jLong coords[2] = {i, j};
-    auto offset = shape::getOffset(0, shapeOf(), stridesOf(), coords, rankOf());
+    auto offset = shape::getOffset(getShapeInfo(), coords);
     tickReadHost();
     return *(reinterpret_cast<T*>(bufferWithOffset(offset)));
 }
@@ -2135,7 +2135,7 @@ T NDArray::t(const Nd4jLong i, const Nd4jLong j) const {
             syncToHost();
 
         Nd4jLong coords[3] = {i, j, k};
-        auto offset = shape::getOffset(0, shapeOf(), stridesOf(), coords, rankOf());
+        auto offset = shape::getOffset(getShapeInfo(), coords);
         tickReadHost();
         return *(reinterpret_cast<T*>(bufferWithOffset(offset)));
     }
diff --git a/libnd4j/blas/NDArray.hpp b/libnd4j/blas/NDArray.hpp
index 82427f9b9..0f0621a80 100644
--- a/libnd4j/blas/NDArray.hpp
+++ b/libnd4j/blas/NDArray.hpp
@@ -808,7 +808,7 @@ void NDArray::templatedSet(void *buffer, const Nd4jLong *indices, const void *va
     auto t = reinterpret_cast<T *>(buffer);
     const auto y = *(reinterpret_cast<const Y *>(value));
 
-    auto xOffset = shape::getOffset(0, shapeOf(), stridesOf(), indices, rankOf());
+    auto xOffset = shape::getOffset(getShapeInfo(), indices);
     t[xOffset] = static_cast<T>(y);
 }
 BUILD_DOUBLE_TEMPLATE(template void NDArray::templatedSet, (void *buffer, const Nd4jLong *indices, const void *value), LIBND4J_TYPES, LIBND4J_TYPES);
@@ -2462,14 +2462,13 @@ double NDArray::getTrace() const {
 
     int  rank    = rankOf();
     auto shape   = shapeOf();
-    auto strides = stridesOf();
     int  minDim  = 100000000;
 
     Nd4jLong indices[MAX_RANK];
     for(int j = 0; j < rank; ++j)
         indices[j] = 1;
 
-    auto offset = shape::getOffset(0, shape, strides, indices, rank);
+    auto offset = shape::getOffset(getShapeInfo(), indices);
 
     for(int i = 0; i < rank; ++i)
         if(minDim > shape[i])
@@ -3472,7 +3471,7 @@ T NDArray::e(const Nd4jLong i, const Nd4jLong j) const {
         throw std::invalid_argument("NDArray::e(i,j): one of input indexes is out of array length or rank!=2 !");
 
     const Nd4jLong coords[2] = {i, j};
-    const auto xOffset = shape::getOffset(0, shapeOf(), stridesOf(), coords, rankOf());
+    const auto xOffset = shape::getOffset(getShapeInfo(), coords);
 
     NDArray::preparePrimaryUse({}, {this});
     NDArray::registerPrimaryUse({}, {this});
@@ -3492,7 +3491,7 @@ T NDArray::e(const Nd4jLong i, const Nd4jLong j, const Nd4jLong k) const {
         throw std::invalid_argument("NDArray::e(i,j,k): one of input indexes is out of array length or rank!=3 !");
 
     const Nd4jLong coords[3] = {i, j, k};
-    const auto xOffset = shape::getOffset(0, shapeOf(), stridesOf(), coords, rankOf());
+    const auto xOffset = shape::getOffset(getShapeInfo(), coords);
 
     NDArray::preparePrimaryUse({}, {this});
     NDArray::registerPrimaryUse({}, {this});
@@ -3512,7 +3511,7 @@ T NDArray::e(const Nd4jLong i, const Nd4jLong j, const Nd4jLong k, const Nd4jLon
         throw std::invalid_argument("NDArray::e(i,j,k,l): one of input indexes is out of array length or rank!=4 !");
 
     const Nd4jLong coords[4] = {i, j, k, l};
-    const auto xOffset = shape::getOffset(0, shapeOf(), stridesOf(), coords, rankOf());
+    const auto xOffset = shape::getOffset(getShapeInfo(), coords);
 
     NDArray::preparePrimaryUse({}, {this});
     NDArray::registerPrimaryUse({}, {this});
@@ -4095,7 +4094,7 @@ void NDArray::p(const Nd4jLong i, const Nd4jLong j, const T value) {
 
     void *p = reinterpret_cast<void *>(const_cast<T *>(&value));
     Nd4jLong coords[2] = {i, j};
-    auto xOffset = shape::getOffset(0, shapeOf(), stridesOf(), coords, rankOf());
+    auto xOffset = shape::getOffset(getShapeInfo(), coords);
 
     NDArray::preparePrimaryUse({this}, {}, true);
     BUILD_SINGLE_PARTIAL_SELECTOR(dataType(), templatedSet<, T>(this->getBuffer(), xOffset, p), LIBND4J_TYPES);
@@ -4127,7 +4126,7 @@ void NDArray::p(const Nd4jLong i, const Nd4jLong j, const Nd4jLong k, const T va
 
     void *p = reinterpret_cast<void *>(const_cast<T *>(&value));
     Nd4jLong coords[3] = {i, j, k};
-    auto xOffset = shape::getOffset(0, shapeOf(), stridesOf(), coords, rankOf());
+    auto xOffset = shape::getOffset(getShapeInfo(), coords);
     BUILD_SINGLE_PARTIAL_SELECTOR(dataType(), templatedSet<, T>(this->getBuffer(), xOffset, p), LIBND4J_TYPES);
     NDArray::registerPrimaryUse({this}, {});
 }
@@ -4154,7 +4153,7 @@ void NDArray::p(const Nd4jLong i, const Nd4jLong j, const Nd4jLong k, const Nd4j
 
     void *p = reinterpret_cast<void *>(const_cast<T *>(&value));
     Nd4jLong coords[4] = {i, j, k, l};
-    auto xOffset = shape::getOffset(0, shapeOf(), stridesOf(), coords, rankOf());
+    auto xOffset = shape::getOffset(getShapeInfo(), coords);
 
     NDArray::preparePrimaryUse({this}, {}, true);
     BUILD_SINGLE_PARTIAL_SELECTOR(dataType(), templatedSet<, T>(this->getBuffer(), xOffset, p), LIBND4J_TYPES);
@@ -4409,7 +4408,7 @@ Nd4jLong NDArray::getOffset(const Nd4jLong i) const {
     if (i >= lengthOf())
         throw std::invalid_argument("NDArray::getOffset: input index is out of array length !");
 
-    return shape::getIndexOffset(i, _shapeInfo, lengthOf());
+    return shape::getIndexOffset(i, _shapeInfo);
 }
 
 NDArray NDArray::like() {
@@ -4455,7 +4454,7 @@ NDArray* NDArray::diagonal(const char type) const {
             indices[i] = 1;
         }
 
-        auto step = shape::getOffset(0, shapeOf(), stridesOf(), indices, rank);
+        auto step = shape::getOffset(getShapeInfo(), indices);
 
         if(type == 'c') {
             outShapeInfo[1] = diagSize;
diff --git a/libnd4j/blas/cpu/NDArray.cpp b/libnd4j/blas/cpu/NDArray.cpp
index 24ef100d3..03c7c53e1 100644
--- a/libnd4j/blas/cpu/NDArray.cpp
+++ b/libnd4j/blas/cpu/NDArray.cpp
@@ -103,8 +103,8 @@ void NDArray::fillAsTriangular(const float val, int lower, int upper, const char
     PRAGMA_OMP_PARALLEL_FOR_ARGS(OMP_IF(zLen > Environment::getInstance()->elementwiseThreshold()) firstprivate(coords))
     for (Nd4jLong i = 0; i < zLen; ++i) {
 
-        shape::index2coords(zRank, target->shapeOf(), i, zLen, coords.data());
-        const auto zOffset = shape::getOffset(0, target->shapeOf(), target->stridesOf(), coords.data(), zRank);
+        shape::index2coords(i, target->getShapeInfo(), coords.data());
+        const auto zOffset = shape::getOffset(target->getShapeInfo(), coords.data());
 
         // if( (row + upper < col) || (row + lower > col) )
         if((coords[zRank - 2] + upper < coords[zRank - 1]) || (coords[zRank - 2] + lower > coords[zRank - 1]))
@@ -112,7 +112,7 @@ void NDArray::fillAsTriangular(const float val, int lower, int upper, const char
         else if(this != target) {      // when this and target are different arrays
             if(xRank != zRank)
                 coords[0] = coords[1];
-            const auto xOffset = areSameOffsets ? zOffset : shape::getOffset(0, shapeOf(), stridesOf(), coords.data(), xRank);
+            const auto xOffset = areSameOffsets ? zOffset : shape::getOffset(getShapeInfo(), coords.data());
             z[zOffset] = x[xOffset];
         }
     }
@@ -128,13 +128,12 @@ void NDArray::setIdentity() {
 
     int  rank    = rankOf();
     auto shape   = shapeOf();
-    auto strides = stridesOf();
     int  minDim  = MAX_INT;
     Nd4jLong indices[MAX_RANK];
     for(int j = 0; j < rank; ++j)
         indices[j] = 1;
 
-    Nd4jLong offset = shape::getOffset(0, shape, strides, indices, rank);
+    Nd4jLong offset = shape::getOffset(getShapeInfo(), indices);
 
     for(int i = 0; i < rank; ++i)
         if(minDim > shape[i])
@@ -380,9 +379,9 @@ static void repeat_(const NDArray& input, NDArray& output, const std::vector<int
     PRAGMA_OMP_PARALLEL_FOR_ARGS(schedule(guided) firstprivate(coords))
     for (Nd4jLong i = 0; i < zLen; ++i) {
 
-        shape::index2coords(rank, output.shapeOf(), i, zLen, coords.data());
+        shape::index2coords(i, output.getShapeInfo(), coords.data());
 
-        const auto zOffset = shape::getOffset(0, output.shapeOf(), output.stridesOf(), coords.data(), rank);
+        const auto zOffset = shape::getOffset(output.getShapeInfo(), coords.data());
 
         if(repSize > 1) {
             for (uint j = 0; j < repSize; ++j) {
@@ -396,7 +395,7 @@ static void repeat_(const NDArray& input, NDArray& output, const std::vector<int
         else
             coords[axis] /= repeats[0];
 
-        z[zOffset] = x[shape::getOffset(0, input.shapeOf(), input.stridesOf(), coords.data(), rank)];
+        z[zOffset] = x[shape::getOffset(input.getShapeInfo(), coords.data())];
     }
 }
 
diff --git a/libnd4j/blas/cpu/NativeOps.cpp b/libnd4j/blas/cpu/NativeOps.cpp
index 2b5733523..243bd96f7 100644
--- a/libnd4j/blas/cpu/NativeOps.cpp
+++ b/libnd4j/blas/cpu/NativeOps.cpp
@@ -1389,8 +1389,8 @@ void pullRowsGeneric(void *vx,
         }
         else {
             for (int i = 0; i < tadLength; i++) {
-                auto xOffset = xTadOffsetForBlock + shape::getIndexOffset(i, tadShapeInfo, tadLength);
-                auto zOffset = zTadOffsetForBlock + shape::getIndexOffset(i, zTadShapeInfo, tadLength);
+                auto xOffset = xTadOffsetForBlock + shape::getIndexOffset(i, tadShapeInfo);
+                auto zOffset = zTadOffsetForBlock + shape::getIndexOffset(i, zTadShapeInfo);
                 hZ[zOffset] = hX[xOffset];
             }
         }
@@ -1454,7 +1454,7 @@ void tearGeneric(void *vx,
         else {
 
             for (Nd4jLong j = 0; j < tadLength; j++)
-                hZ[shape::getIndexOffset(j, hZShapeInfo, tadLength)] = s[shape::getIndexOffset(j, tadShapeInfo, tadLength)];
+                hZ[shape::getIndexOffset(j, hZShapeInfo)] = s[shape::getIndexOffset(j, tadShapeInfo)];
         }
     }
 }
@@ -1601,7 +1601,7 @@ void shuffleGeneric(void **hX, Nd4jLong **hXShapeInfo, void **dz, Nd4jLong **hZS
                     }
                 } else {
                     for (Nd4jLong i = 0; i < tadLength; i++) {
-                        auto offset = shape::getIndexOffset(i, tadOnlyShapeInfo[f], tadLength);
+                        auto offset = shape::getIndexOffset(i, tadOnlyShapeInfo[f]);
                         nd4j::math::nd4j_swap<T>(hX[offset + oldOffset], hX[offset + newOffset]);
                     }
                 }
diff --git a/libnd4j/blas/cuda/NDArray.cu b/libnd4j/blas/cuda/NDArray.cu
index f6a05c44b..1d95fd3c2 100644
--- a/libnd4j/blas/cuda/NDArray.cu
+++ b/libnd4j/blas/cuda/NDArray.cu
@@ -106,8 +106,8 @@ __global__ static void fillAsTriangularCuda(const void* vx, const Nd4jLong* xSha
 
     for (Nd4jLong i = tid; i < zLen; i += totalThreads) {
 
-        shape::index2coords(zRank, shape::shapeOf(const_cast<Nd4jLong*>(zShapeInfo)), i, zLen, coords);
-        const auto zOffset = shape::getOffset(0, shape::shapeOf(const_cast<Nd4jLong*>(zShapeInfo)), shape::stride(const_cast<Nd4jLong*>(zShapeInfo)), coords, zRank);
+        shape::index2coords(i, zShapeInfo, coords);
+        const auto zOffset = shape::getOffset(zShapeInfo, coords);
 
         // if( (row + upper < col) || (row + lower > col) )
         if((coords[zRank - 2] + upper < coords[zRank - 1]) || (coords[zRank - 2] + lower > coords[zRank - 1]))
@@ -115,7 +115,7 @@ __global__ static void fillAsTriangularCuda(const void* vx, const Nd4jLong* xSha
         else if(vx != vz) {      // when x and z are different arrays
             if(xRank != zRank)
                 coords[0] = coords[1];
-            const auto xOffset = areSameOffsets ? zOffset : shape::getOffset(0, shape::shapeOf(const_cast<Nd4jLong*>(xShapeInfo)), shape::stride(const_cast<Nd4jLong*>(xShapeInfo)), coords, xRank);
+            const auto xOffset = areSameOffsets ? zOffset : shape::getOffset(xShapeInfo, coords);
             z[zOffset] = x[xOffset];
         }
     }
@@ -177,8 +177,8 @@ __global__ static void identityMatrixCuda(void* vx, const Nd4jLong* xShapeInfo,
 
     for (Nd4jLong i = tid; i < len; i += totalThreads) {
 
-        shape::index2coords(rank, shape::shapeOf(const_cast<Nd4jLong*>(xShapeInfo)), i, len, coords);
-        const auto offset = shape::getOffset(0, shape::shapeOf(const_cast<Nd4jLong*>(xShapeInfo)), shape::stride(const_cast<Nd4jLong*>(xShapeInfo)), coords, rank);
+        shape::index2coords(i, xShapeInfo, coords);
+        const auto offset = shape::getOffset(xShapeInfo, coords);
 
         if(coords[rank - 2] == coords[rank - 1]) // row == col -> on diagonal
             x[offset] = val;
@@ -424,9 +424,9 @@ __global__ static void repeatCuda(const void* vx, const Nd4jLong* xShapeInfo,
 
     for (Nd4jLong i = tid; i < zLen; i += totalThreads) {
 
-        shape::index2coords(rank, zShapeInfo + 1, i, zLen, coords);
+        shape::index2coords(i, zShapeInfo, coords);
 
-        const auto zOffset = shape::getOffset(0, zShapeInfo + 1, zShapeInfo + rank + 1, coords, rank);
+        const auto zOffset = shape::getOffset(zShapeInfo, coords);
 
         if(repSize > 1) {
             for (uint j = 0; j < repSize; ++j) {
@@ -440,7 +440,7 @@ __global__ static void repeatCuda(const void* vx, const Nd4jLong* xShapeInfo,
         else
             coords[axis] /= repeats[0];
 
-        z[zOffset] = x[shape::getOffset(0, xShapeInfo + 1, xShapeInfo + rank + 1, coords, rank)];
+        z[zOffset] = x[shape::getOffset(xShapeInfo, coords)];
     }
 }
 
diff --git a/libnd4j/blas/cuda/NDArrayLambda.hpp b/libnd4j/blas/cuda/NDArrayLambda.hpp
index bf9848981..c27476bfb 100644
--- a/libnd4j/blas/cuda/NDArrayLambda.hpp
+++ b/libnd4j/blas/cuda/NDArrayLambda.hpp
@@ -23,8 +23,8 @@
 #include <cuda.h>
 #include <cuda_runtime.h>
 
-static Nd4jLong __device__ __noinline__ __getIndexOffset(Nd4jLong index, Nd4jLong *shapeInfo, Nd4jLong length) {
-    return shape::getIndexOffset(index, shapeInfo, length);
+static Nd4jLong __device__ __noinline__ __getIndexOffset(Nd4jLong index, Nd4jLong *shapeInfo) {
+    return shape::getIndexOffset(index, shapeInfo);
 }
 
 static Nd4jLong __device__ __noinline__ __length(Nd4jLong *shapeInfo) {
@@ -103,8 +103,8 @@ static _CUDA_G void lambdaKernel(void* vx, Nd4jLong *xShapeInfo, void *vz, Nd4jL
             z[e * zEws] = lambda(x[e * xEws]);
     } else {
         for (uint e = tid; e < zLength; e += blockDim.x * gridDim.x) {
-            auto xOffset = __getIndexOffset(e, xShapeInfo, zLength);
-            auto zOffset = __getIndexOffset(e, zShapeInfo, zLength);
+            auto xOffset = __getIndexOffset(e, xShapeInfo);
+            auto zOffset = __getIndexOffset(e, zShapeInfo);
 
             z[zOffset] = lambda(x[xOffset]);
         }
@@ -132,8 +132,8 @@ static _CUDA_G void lambdaIndexedKernel(void* vx, Nd4jLong *xShapeInfo, void *vz
             z[e * zEws] = lambda(e, x[e * xEws]);
     } else {
         for (uint e = tid; e < zLength; e += blockDim.x * gridDim.x) {
-            auto xOffset = __getIndexOffset(e, xShapeInfo, zLength);
-            auto zOffset = __getIndexOffset(e, zShapeInfo, zLength);
+            auto xOffset = __getIndexOffset(e, xShapeInfo);
+            auto zOffset = __getIndexOffset(e, zShapeInfo);
 
             z[zOffset] = lambda(e, x[xOffset]);
         }
@@ -164,9 +164,9 @@ static _CUDA_G void lambdaIndexedPairwiseKernel(void* vx, Nd4jLong *xShapeInfo,
             z[e * zEws] = lambda(e, x[e * xEws], y[e * yEws]);
     } else {
         for (uint e = tid; e < zLength; e += blockDim.x * gridDim.x) {
-            auto xOffset = __getIndexOffset(e, xShapeInfo, zLength);
-            auto yOffset = __getIndexOffset(e, yShapeInfo, zLength);
-            auto zOffset = __getIndexOffset(e, zShapeInfo, zLength);
+            auto xOffset = __getIndexOffset(e, xShapeInfo);
+            auto yOffset = __getIndexOffset(e, yShapeInfo);
+            auto zOffset = __getIndexOffset(e, zShapeInfo);
 
             z[zOffset] = lambda(e, x[xOffset], y[yOffset]);
         }
@@ -197,9 +197,9 @@ static _CUDA_G void lambdaPairwiseKernel(void* vx, Nd4jLong *xShapeInfo, void* v
             z[e * zEws] = lambda(x[e * xEws], y[e * yEws]);
     } else {
         for (uint e = tid; e < zLength; e += blockDim.x * gridDim.x) {
-            auto xOffset = __getIndexOffset(e, xShapeInfo, zLength);
-            auto yOffset = __getIndexOffset(e, yShapeInfo, zLength);
-            auto zOffset = __getIndexOffset(e, zShapeInfo, zLength);
+            auto xOffset = __getIndexOffset(e, xShapeInfo);
+            auto yOffset = __getIndexOffset(e, yShapeInfo);
+            auto zOffset = __getIndexOffset(e, zShapeInfo);
 
             z[zOffset] = lambda(x[xOffset], y[yOffset]);
         }
@@ -233,10 +233,10 @@ static _CUDA_G void lambdaTriplewiseKernel(void* vw, Nd4jLong *wShapeInfo, void*
             z[e * zEws] = lambda(w[e * wEws], x[e * xEws], y[e * yEws]);
     } else {
         for (uint e = tid; e < zLength; e += blockDim.x * gridDim.x) {
-            auto wOffset = __getIndexOffset(e, wShapeInfo, zLength);
-            auto xOffset = __getIndexOffset(e, xShapeInfo, zLength);
-            auto yOffset = __getIndexOffset(e, yShapeInfo, zLength);
-            auto zOffset = __getIndexOffset(e, zShapeInfo, zLength);
+            auto wOffset = __getIndexOffset(e, wShapeInfo);
+            auto xOffset = __getIndexOffset(e, xShapeInfo);
+            auto yOffset = __getIndexOffset(e, yShapeInfo);
+            auto zOffset = __getIndexOffset(e, zShapeInfo);
 
             z[zOffset] = lambda(w[wOffset], x[xOffset], y[yOffset]);
         }
diff --git a/libnd4j/blas/cuda/NativeOps.cu b/libnd4j/blas/cuda/NativeOps.cu
index ee1bed052..dc9d37b03 100755
--- a/libnd4j/blas/cuda/NativeOps.cu
+++ b/libnd4j/blas/cuda/NativeOps.cu
@@ -3228,8 +3228,8 @@ __global__ static void scatterUpdateCuda(const int opCode, const int numOfSubArr
 
         for (Nd4jLong i = threadIdx.x; i < arrLenX; i += blockDim.x) {
 
-            const auto xOffset = shape::getIndexOffset(i, xShapeInfo, arrLenX);
-            const auto yOffset = shape::getIndexOffset(i, yShapeInfo, arrLenY);
+            const auto xOffset = shape::getIndexOffset(i, xShapeInfo);
+            const auto yOffset = shape::getIndexOffset(i, yShapeInfo);
 
             switch (opCode) {
                 case 0:
diff --git a/libnd4j/include/helpers/Loops.h b/libnd4j/include/helpers/Loops.h
index d04d3315d..392ed3edf 100644
--- a/libnd4j/include/helpers/Loops.h
+++ b/libnd4j/include/helpers/Loops.h
@@ -246,9 +246,9 @@ void Loops::loopXYZ(const X* x, const Nd4jLong* xShapeInfo,
                 auto lenPerThread = static_cast<uint>(threadsInfo.getItersPerThread(threadNum));
                 PRAGMA_OMP_SIMD
                 for (uint i = 0; i < lenPerThread; i++) {
-                    auto xOffset = shape::indexOffset(i + threadOffset, xShapeInfo, xShapeInfoCast, len, canCastX);
-                    auto yOffset = shape::indexOffset(i + threadOffset, yShapeInfo, yShapeInfoCast, len, canCastY);
-                    auto zOffset = shape::indexOffset(i + threadOffset, zShapeInfo, zShapeInfoCast, len, canCastZ);
+                    auto xOffset = shape::indexOffset(i + threadOffset, xShapeInfo, xShapeInfoCast, canCastX);
+                    auto yOffset = shape::indexOffset(i + threadOffset, yShapeInfo, yShapeInfoCast, canCastY);
+                    auto zOffset = shape::indexOffset(i + threadOffset, zShapeInfo, zShapeInfoCast, canCastZ);
                     z[zOffset] = op(x[xOffset], y[yOffset], extraParams);
                 }
             }
@@ -452,7 +452,7 @@ void Loops::loopXYZ(const X* x, const Nd4jLong* xShapeInfo,
                     for (uint j = 0; j < tadLen; j++)
                         start = OpType::update(start, OpType::op(tad[j * tadEws], extraParams), extraParams);
 
-                    auto zOffset = shape::indexOffset(i, zShapeInfo, castZShapeInfo, zLen, canCastZ);
+                    auto zOffset = shape::indexOffset(i, zShapeInfo, castZShapeInfo, canCastZ);
                     z[zOffset] = OpType::postProcess(start, tadLen, extraParams);
                 }
             }
@@ -469,7 +469,7 @@ void Loops::loopXYZ(const X* x, const Nd4jLong* xShapeInfo,
                     auto start = OpType::startingValue(tad);
 
                     for (uint j = 0; j < tadLen; j++) {
-                        auto tadOffset = shape::indexOffset(j, tadShapeInfo, castTadShapeInfo, tadLen, canCastTad);
+                        auto tadOffset = shape::indexOffset(j, tadShapeInfo, castTadShapeInfo, canCastTad);
                         start = OpType::update(start, OpType::op(tad[tadOffset], extraParams), extraParams);
                     }
 
@@ -491,11 +491,11 @@ void Loops::loopXYZ(const X* x, const Nd4jLong* xShapeInfo,
             //         auto start = OpType::startingValue(tad);
 
             //         for (uint j = 0; j < tadLen; j++) {
-            //             auto tadOffset = shape::indexOffset(j, tadShapeInfo, castTadShapeInfo, tadLen, canCastTad);
+            //             auto tadOffset = shape::indexOffset(j, tadShapeInfo, castTadShapeInfo, canCastTad);
             //             start = OpType::update(start, OpType::op(tad[tadOffset], extraParams), extraParams);
             //         }
 
-            //         auto zOffset = shape::indexOffset(i, zShapeInfo, castZShapeInfo, zLen, canCastZ);
+            //         auto zOffset = shape::indexOffset(i, zShapeInfo, castZShapeInfo, canCastZ);
             //         z[zOffset] = OpType::postProcess(start, tadLen, extraParams);
             //     }
             // }
@@ -517,7 +517,7 @@ void Loops::loopXYZ(const X* x, const Nd4jLong* xShapeInfo,
                     for (uint j = 0; j < tadLen; j++)
                         start = OpType::update(start, OpType::op(tad[innertadOffsets[j]], extraParams), extraParams);
 
-                    auto zOffset = shape::indexOffset(i, zShapeInfo, castZShapeInfo, zLen, canCastZ);
+                    auto zOffset = shape::indexOffset(i, zShapeInfo, castZShapeInfo, canCastZ);
                     z[zOffset] = OpType::postProcess(start, tadLen, extraParams);
                 }
 
@@ -658,13 +658,13 @@ void Loops::loopXYZ(const X* x, const Nd4jLong* xShapeInfo,
 
                         PRAGMA_OMP_SIMD
                         for (uint i = 0; i < lenPerThread; i++) {
-                            const auto xOffset = shape::indexOffset(i + threadOffset, xShapeInfo, castXShapeInfo, len, canCastX);
+                            const auto xOffset = shape::indexOffset(i + threadOffset, xShapeInfo, castXShapeInfo, canCastX);
                             zi[i * zEws] = OpType::op(x[xOffset], extraParams);
                         }
                     } else {
                         PRAGMA_OMP_SIMD
                         for (uint i = 0; i < lenPerThread; i++) {
-                            const auto xOffset = shape::indexOffset(i + threadOffset, xShapeInfo, castXShapeInfo, len, canCastX);
+                            const auto xOffset = shape::indexOffset(i + threadOffset, xShapeInfo, castXShapeInfo, canCastX);
                             zi[i] = OpType::op(x[xOffset], extraParams);
                         }
                     }
@@ -782,8 +782,8 @@ void Loops::loopXYZ(const X* x, const Nd4jLong* xShapeInfo,
 
                     PRAGMA_OMP_SIMD
                     for (uint i = 0; i < lenPerThread; i++) {
-                        auto xOffset = shape::indexOffset(i + threadOffset, xShapeInfo, xShapeInfoCast, len, canCastX);
-                        auto zOffset = shape::indexOffset(i + threadOffset, zShapeInfo, zShapeInfoCast, len, canCastZ);
+                        auto xOffset = shape::indexOffset(i + threadOffset, xShapeInfo, xShapeInfoCast, canCastX);
+                        auto zOffset = shape::indexOffset(i + threadOffset, zShapeInfo, zShapeInfoCast, canCastZ);
                         z[zOffset] = OpType::op(x[xOffset], extraParams);
                     }
                 }
@@ -1123,7 +1123,7 @@ void Loops::loopXYZ(const X* x, const Nd4jLong* xShapeInfo,
                         auto start      = OpType::startingValue(xTad);
 
                         for (uint j = 0; j < tadLen; ++j) {
-                            const auto tadOffset = shape::indexOffset(j, xTadShapeInfo, castXTadShapeInfo, tadLen, canCastXTad);
+                            const auto tadOffset = shape::indexOffset(j, xTadShapeInfo, castXTadShapeInfo, canCastXTad);
                             start = OpType::update(start, OpType::op(xTad[tadOffset], yTad[tadOffset], extraParams), extraParams);
                         }
 
@@ -1147,8 +1147,8 @@ void Loops::loopXYZ(const X* x, const Nd4jLong* xShapeInfo,
                         auto start      = OpType::startingValue(xTad);
 
                         for (uint j = 0; j < tadLen; ++j) {
-                            const auto xTadOffset = shape::indexOffset(j, xTadShapeInfo, castXTadShapeInfo, tadLen, canCastXTad);
-                            const auto yTadOffset = shape::indexOffset(j, yTadShapeInfo, castYTadShapeInfo, tadLen, canCastYTad);
+                            const auto xTadOffset = shape::indexOffset(j, xTadShapeInfo, castXTadShapeInfo, canCastXTad);
+                            const auto yTadOffset = shape::indexOffset(j, yTadShapeInfo, castYTadShapeInfo, canCastYTad);
                             start = OpType::update(start, OpType::op(xTad[xTadOffset], yTad[yTadOffset], extraParams), extraParams);
                         }
 
@@ -1423,7 +1423,7 @@ void Loops::loopXYZ(const X* x, const Nd4jLong* xShapeInfo,
                                   auto start = startVal;
 
                             for (uint j = 0; j < tadLen; ++j) {
-                                const auto tadOffset = shape::indexOffset(j, xTadShapeInfo, castXTadShapeInfo, tadLen, canCastXTad);
+                                const auto tadOffset = shape::indexOffset(j, xTadShapeInfo, castXTadShapeInfo, canCastXTad);
                                 start = OpType::update(start, OpType::op(xTad[tadOffset], yTad[tadOffset], extraParams), extraParams);
                             }
                             z[zInd * zEws] = OpType::postProcess(start, tadLen, extraParams);
@@ -1449,8 +1449,8 @@ void Loops::loopXYZ(const X* x, const Nd4jLong* xShapeInfo,
                                   auto start = startVal;
 
                             for (uint j = 0; j < tadLen; ++j) {
-                                const auto xTadOffset = shape::indexOffset(j, xTadShapeInfo, castXTadShapeInfo, tadLen, canCastXTad);
-                                const auto yTadOffset = shape::indexOffset(j, yTadShapeInfo, castYTadShapeInfo, tadLen, canCastYTad);
+                                const auto xTadOffset = shape::indexOffset(j, xTadShapeInfo, castXTadShapeInfo, canCastXTad);
+                                const auto yTadOffset = shape::indexOffset(j, yTadShapeInfo, castYTadShapeInfo, canCastYTad);
                                 start = OpType::update(start, OpType::op(xTad[xTadOffset], yTad[yTadOffset], extraParams), extraParams);
                             }
 
diff --git a/libnd4j/include/helpers/ShapeUtils.h b/libnd4j/include/helpers/ShapeUtils.h
index 1d991e36a..ba0f956a5 100644
--- a/libnd4j/include/helpers/ShapeUtils.h
+++ b/libnd4j/include/helpers/ShapeUtils.h
@@ -15,7 +15,7 @@
  ******************************************************************************/
 
 //
-// @author iuriish@yahoo.com
+// @author Yurii Shyrma (iuriish@yahoo.com)
 //
 
 #ifndef LIBND4J_SHAPEUTILS_H
diff --git a/libnd4j/include/helpers/TAD.h b/libnd4j/include/helpers/TAD.h
index c49f1047d..9888bb1fd 100644
--- a/libnd4j/include/helpers/TAD.h
+++ b/libnd4j/include/helpers/TAD.h
@@ -526,7 +526,7 @@ namespace shape {
         /* int *sub = new int[leftOverIndexLen];
          shape::ind2subOrder(tadShape,index,len,sub);
         */
-        shape::index2coords(leftOverIndexLen,tadShape, index,len, sub);
+        shape::index2coords(index, leftOverIndexLen,tadShape, sub);
 
 
         for(int i = 0; i < leftOverIndexLen; i++) {
@@ -609,7 +609,7 @@ namespace shape {
         if(dimensionLength > 1) {
             Nd4jLong *tad2Sub = this->tad2Sub(index, ptrManager);
 
-            Nd4jLong ret = shape::getOffset(0,shape::shapeOf(shapeInfo),shape::stride(shapeInfo),tad2Sub,shape::rank(shapeInfo));
+            Nd4jLong ret = shape::getOffset(shapeInfo, tad2Sub);
 
             if(ret < 0) {
                 if (ptrManager == nullptr)
@@ -625,7 +625,7 @@ namespace shape {
         else {
             Nd4jLong *tad2Sub = this->tad2Sub(index, ptrManager);
 
-            Nd4jLong ret = shape::getOffset(0,shape::shapeOf(shapeInfo),shape::stride(shapeInfo),tad2Sub,shape::rank(shapeInfo));
+            Nd4jLong ret = shape::getOffset(shapeInfo, tad2Sub);
 
             if (ptrManager == nullptr)
                 delete[] tad2Sub;
@@ -703,7 +703,7 @@ namespace shape {
         /* int *sub = new int[leftOverIndexLen];
          shape::ind2subOrder(tadShape,index,len,sub);
         */
-        shape::index2coords(leftOverIndexLen,tadShape,index,len, sub);
+        shape::index2coords(index, leftOverIndexLen,tadShape, sub);
 
         for(int i = 0; i < leftOverIndexLen; i++) {
             ret[leftOverIndexes[i]] = sub[i];
@@ -732,7 +732,7 @@ namespace shape {
         //    return shape::createScalarShapeInfo();
 
         //ensure tad shapes get setup right for vectors
-        if(dimensionLength > 1 && shape::isVector(shapeInfo)) 
+        if(dimensionLength > 1 && shape::isVector(shapeInfo))
             return shape::copyOf(shape::shapeInfoLength(shape::rank(shapeInfo)),shapeInfo);
 
         // case when tad coincides with whole array
diff --git a/libnd4j/include/helpers/benchmark/ParametersBatch.h b/libnd4j/include/helpers/benchmark/ParametersBatch.h
index 5a45099c3..4a7119937 100644
--- a/libnd4j/include/helpers/benchmark/ParametersBatch.h
+++ b/libnd4j/include/helpers/benchmark/ParametersBatch.h
@@ -64,7 +64,7 @@ namespace nd4j {
 
 
             for (int i = 0; i < totalIterations; i++) {
-                shape::index2coords(xRank, xShape, i, totalIterations, xCoords);
+                shape::index2coords(i, xRank, xShape, xCoords);
 
                 Parameters params;
                 for (int j = 0; j < xRank; j++) {
diff --git a/libnd4j/include/helpers/cpu/loops/IndexReductionLoops.cpp b/libnd4j/include/helpers/cpu/loops/IndexReductionLoops.cpp
index 0a096b65f..22ff3e6b1 100644
--- a/libnd4j/include/helpers/cpu/loops/IndexReductionLoops.cpp
+++ b/libnd4j/include/helpers/cpu/loops/IndexReductionLoops.cpp
@@ -226,7 +226,7 @@ void nd4j::IndexReductionLoops<X,Z>::loopIndexReduce(X* x, Nd4jLong* xShapeInfo,
                     indexValue = OpType::update(indexValue, comp, extraParams);
                 }
 
-                auto zOffset = shape::indexOffset(i, zShapeInfo, castZShapeInfo, zLen, canCastZ);
+                auto zOffset = shape::indexOffset(i, zShapeInfo, castZShapeInfo, canCastZ);
                 z[zOffset] = (Z) indexValue.index;
             }
         }
@@ -243,7 +243,7 @@ void nd4j::IndexReductionLoops<X,Z>::loopIndexReduce(X* x, Nd4jLong* xShapeInfo,
                 auto indexValue = OpType::startingIndexValue(tad);
 
                 for (uint j = 0; j < tadLen; j++) {
-                    auto tadOffset = shape::indexOffset(j, tadShapeInfo, castTadShapeInfo, tadLen, canCastTad);
+                    auto tadOffset = shape::indexOffset(j, tadShapeInfo, castTadShapeInfo, canCastTad);
                     functions::indexreduce::IndexValue<X> comp(tad[tadOffset], j);
                     indexValue = OpType::update(indexValue, comp, extraParams);
                 }
@@ -266,12 +266,12 @@ void nd4j::IndexReductionLoops<X,Z>::loopIndexReduce(X* x, Nd4jLong* xShapeInfo,
                 auto indexValue = OpType::startingIndexValue(tad);
 
                 for (uint j = 0; j < tadLen; j++) {
-                    auto tadOffset = shape::indexOffset(j, tadShapeInfo, castTadShapeInfo, tadLen, canCastTad);
+                    auto tadOffset = shape::indexOffset(j, tadShapeInfo, castTadShapeInfo, canCastTad);
                     functions::indexreduce::IndexValue<X> comp(tad[tadOffset], j);
                     indexValue = OpType::update(indexValue, comp, extraParams);
                 }
 
-                auto zOffset = shape::indexOffset(i, zShapeInfo, castZShapeInfo, zLen, canCastZ);
+                auto zOffset = shape::indexOffset(i, zShapeInfo, castZShapeInfo, canCastZ);
                 z[zOffset] = (Z) indexValue.index;
             }
         }
diff --git a/libnd4j/include/helpers/impl/ShapeUtils.cpp b/libnd4j/include/helpers/impl/ShapeUtils.cpp
index 6d93351c3..91ee09123 100644
--- a/libnd4j/include/helpers/impl/ShapeUtils.cpp
+++ b/libnd4j/include/helpers/impl/ShapeUtils.cpp
@@ -15,7 +15,7 @@
  ******************************************************************************/
 
 //
-// @author Yurii Shyrma
+// @author Yurii Shyrma (iuriish@yahoo.com)
 //
 
 #include <algorithm>
@@ -931,7 +931,7 @@ void ShapeUtils::evalIdxRangesForSubArr(const Nd4jLong subArrIdx,  const Nd4jLon
     for(int i = 0; i < subArrRank; ++i)
         shapeOfSubArr[i] = shapeInfo[dimsToExclude[i] + 1];
 
-    shape::index2coords(subArrRank, shapeOfSubArr.data(), subArrIdx, indexes.data());
+    shape::index2coords(subArrIdx, subArrRank, shapeOfSubArr.data(), indexes.data());
 
     memset(idxRanges, 0, 2 * rank * sizeof(Nd4jLong));
 
diff --git a/libnd4j/include/helpers/shape.h b/libnd4j/include/helpers/shape.h
index 538bd657d..b8cbf1b37 100644
--- a/libnd4j/include/helpers/shape.h
+++ b/libnd4j/include/helpers/shape.h
@@ -887,7 +887,7 @@ namespace shape {
 * @param indices the indices to iterate over
 * @return the double at the specified index
 */
-    ND4J_EXPORT _CUDA_HD Nd4jLong getOffset(Nd4jLong baseOffset, const Nd4jLong *shape, const Nd4jLong *stride,  const Nd4jLong *indices, const int rank);
+
     ND4J_EXPORT _CUDA_HD Nd4jLong getOffset(const Nd4jLong *shapeInfo, const Nd4jLong *indices, Nd4jLong baseOffset = 0);
     ND4J_EXPORT Nd4jLong getOffset(const Nd4jLong *shapeInfo, const std::vector<uint>& indices);
 
@@ -897,20 +897,19 @@ namespace shape {
 
     /**
     * Convert a linear index to the corresponding coordinates
-    * for example if shape is {2, 4}, then index 5 corresponds to following coordinates
-    * -> [1, 1] in case of c order
-    * -> [1, 2] in case of f order
+    * for example if shape is {2, 4}, then index 5 corresponds to coordinates [1, 1]
     */
-    ND4J_EXPORT _CUDA_HD void index2coords(const int rank, const Nd4jLong *shape, Nd4jLong index, Nd4jLong arrLen, Nd4jLong *coords, const char order = 'c');
-    ND4J_EXPORT _CUDA_HD void index2coords(const int rank, const Nd4jLong *shape, Nd4jLong index, Nd4jLong *coords, const char order = 'c');
+    ND4J_EXPORT _CUDA_HD void index2coords(Nd4jLong index, const Nd4jLong *shapeInfo,  Nd4jLong *coords);
+    ND4J_EXPORT _CUDA_HD void index2coords(Nd4jLong index, const int rank, const Nd4jLong *shape, Nd4jLong *coords);
+
+
 
     /**
     * Convert coordinates to the corresponding linear index (sequence number in other words)
-    * for example if shape is {2, 4}, then:
-    * in case of c order and coordinates [1, 1] index 5 is returned
-    * in case of f order and coordinates [1, 2] index 5 is returned
+    * for example if shape is {2, 4} and coordinates [1, 1] then index 5 is returned
     */
-    ND4J_EXPORT _CUDA_HD Nd4jLong coords2index(const int rank, const Nd4jLong *shape, const Nd4jLong *coords, const char order = 'c');
+    ND4J_EXPORT _CUDA_HD Nd4jLong coords2index(const Nd4jLong *shapeInfo, const Nd4jLong *coords);
+    ND4J_EXPORT _CUDA_HD Nd4jLong coords2index(const int rank, const Nd4jLong *shape, const Nd4jLong *coords);
 
    /**
    * increment n-dimensional array by one iteration by changing coord appropriately
@@ -921,24 +920,10 @@ namespace shape {
    */
 
    /* calculates an array buffer offset for given "index" using following formula: offset = coord_0*stride_0 + coord_1*stride_1 + ... + coord_{rank-1}*stride_{rank-1}
-    * arrLen - array length
    */
-    ND4J_EXPORT _CUDA_HD uint getIndexOffset(uint index, const uint *shapeInfo, uint arrLen);
-    ND4J_EXPORT _CUDA_HD Nd4jLong getIndexOffset(Nd4jLong index, const Nd4jLong *shapeInfo, Nd4jLong arrLen);
-    ND4J_EXPORT _CUDA_HD Nd4jLong getIndexOrderOffset(Nd4jLong index, const Nd4jLong *shapeInfo, Nd4jLong arrLen, const char order);
-    ND4J_EXPORT _CUDA_HD Nd4jLong indexOffset(Nd4jLong index, const Nd4jLong* lShapeInfo, const uint* uShapeInfo, Nd4jLong arrLen, const bool useUnsigned);
-
-    /**
-   * Compute the real linear indices for the given shape and stride
-   */
-    ND4J_EXPORT _CUDA_HD Nd4jLong *computeIndices(int rank,  Nd4jLong *shape,  Nd4jLong *stride);
-
-    /**
-   * Compute the real linear indices for the
-     * given shape buffer. Shape,stride and rank are derived
-     * from the buffer
-   */
-    ND4J_EXPORT _CUDA_HD Nd4jLong *computeIndices( Nd4jLong *shapeBuffer);
+    ND4J_EXPORT _CUDA_HD uint getIndexOffset(uint index, const uint *shapeInfo);
+    ND4J_EXPORT _CUDA_HD Nd4jLong getIndexOffset(Nd4jLong index, const Nd4jLong *shapeInfo);
+    ND4J_EXPORT _CUDA_HD Nd4jLong indexOffset(Nd4jLong index, const Nd4jLong* lShapeInfo, const uint* uShapeInfo, const bool useUnsigned);
 
     ND4J_EXPORT _CUDA_HD void printShapeInfo(Nd4jLong *shapeInfo);
 
@@ -1749,57 +1734,34 @@ __device__ INLINEDEF Nd4jLong *cuMalloc(Nd4jLong *buffer, long size) {
         return output;
     }
 
-/**
- * Compute the real linear indices for the given shape and stride
- */
-    INLINEDEF _CUDA_HD Nd4jLong *computeIndices(int rank, Nd4jLong *shape,  Nd4jLong *stride) {
-        Nd4jLong length = shape::prodLong(shape,rank);
+//////////////////////////////////////////////////////////////////////
+INLINEDEF _CUDA_HD Nd4jLong coords2index(const Nd4jLong *shapeInfo, const Nd4jLong *indices) {
 
-        traceNew(13);
+    Nd4jLong index, shift = 1;;
 
-        Nd4jLong *ret = new Nd4jLong[length];
-        for(int i = 0; i < length; i++) {
-            Nd4jLong *idx = new Nd4jLong[rank];
-            shape::index2coords(rank, shape, i, idx, 'f');
-            ret[i] = shape::getOffset(0, shape, stride, idx, rank);
-            delete[] idx;
-        }
-
-        return ret;
-    }
-
-/**
-* Compute the real linear indices for the given shape and stride
-*/
-    INLINEDEF _CUDA_HD Nd4jLong *computeIndices(Nd4jLong *shapeBuffer) {
-        return computeIndices(shape::rank(shapeBuffer),shape::shapeOf(shapeBuffer),shape::stride(shapeBuffer));
+    index = indices[shapeInfo[0] - 1];
+    for(uint i = shapeInfo[0]; i > 1; --i) {
+        shift *= shapeInfo[i];
+        index += shift * indices[i - 2];
     }
 
+    return index;
+}
 
 //////////////////////////////////////////////////////////////////////
-    INLINEDEF _CUDA_HD Nd4jLong coords2index(const int rank, const Nd4jLong *shape, const Nd4jLong *indices, const char order) {
+INLINEDEF _CUDA_HD Nd4jLong coords2index(const int rank, const Nd4jLong *shape, const Nd4jLong *indices) {
 
-        Nd4jLong index, shift = 1;;
+    Nd4jLong index, shift = 1;;
 
-        if(order == 'c') {
-
-            index = indices[rank - 1];
-            for(int i = rank - 2; i >= 0; --i) {
-                shift *= shape[i + 1];
-                index += shift * indices[i];
-            }
-        }
-        else {
-            index = indices[0];
-            for(int i = 1; i < rank; ++i) {
-                shift *= shape[i - 1];
-                index += shift * indices[i];
-            }
-        }
-
-        return index;
+    index = indices[rank - 1];
+    for(uint i = rank - 1; i >= 1; --i) {
+        shift *= shape[i];
+        index += shift * indices[i - 1];
     }
 
+    return index;
+}
+
 template <typename T>
  INLINEDEF _CUDA_HD void fill(T* buffer, T value, Nd4jLong length) {
 
@@ -1809,85 +1771,110 @@ template <typename T>
  }
 
 
-//////////////////////////////////////////////////////////////////////
-    INLINEDEF _CUDA_HD Nd4jLong getIndexOffset(Nd4jLong index, const Nd4jLong *shapeInfo, Nd4jLong arrLen) {
+// //////////////////////////////////////////////////////////////////////
+//     INLINEDEF _CUDA_HD Nd4jLong getIndexOffset(Nd4jLong index, const Nd4jLong *shapeInfo, Nd4jLong arrLen) {
 
-        const Nd4jLong ews = shapeInfo[shapeInfo[0] + shapeInfo[0] + 2];
+//         const Nd4jLong ews = shapeInfo[shapeInfo[0] + shapeInfo[0] + 2];
 
-        if(ews > 0 && order(shapeInfo) == 'c')
-           if (ews == 1)
-               return index;
-           else
-               return ews * index;
+//         if(ews > 0 && order(shapeInfo) == 'c')
+//            if (ews == 1)
+//                return index;
+//            else
+//                return ews * index;
 
-        Nd4jLong offset = 0;
-        Nd4jLong rank = shapeInfo[0];
-        for(int i = 1; i <= shapeInfo[0]; ++i) {
-            arrLen /= shapeInfo[i];
-            if(arrLen > 0 && shapeInfo[i] > 1) {
-                offset += (index / arrLen) * shapeInfo[i + rank];
-                index %= arrLen;
-            }
-        }
-        return offset;
-    }
+//         Nd4jLong offset = 0;
+//         Nd4jLong rank = shapeInfo[0];
+//         for(int i = 1; i <= shapeInfo[0]; ++i) {
+//             arrLen /= shapeInfo[i];
+//             if(arrLen > 0 && shapeInfo[i] > 1) {
+//                 offset += (index / arrLen) * shapeInfo[i + rank];
+//                 index %= arrLen;
+//             }
+//         }
+//         return offset;
+//     }
 
-    INLINEDEF _CUDA_HD uint getIndexOffset(uint index, const uint *shapeInfo, uint arrLen) {
+//     INLINEDEF _CUDA_HD uint getIndexOffset(uint index, const uint *shapeInfo, uint arrLen) {
 
-        const uint rank = shapeInfo[0];
-        const uint ews = shapeInfo[rank + rank + 2];
+//         const uint rank = shapeInfo[0];
+//         const uint ews = shapeInfo[rank + rank + 2];
 
-        if(ews > 0 && shapeInfo[rank + rank + 3] == 99)
-           if (ews == 1)
-               return index;
-           else
-               return ews * index;
+//         if(ews > 0 && shapeInfo[rank + rank + 3] == 99)
+//            if (ews == 1)
+//                return index;
+//            else
+//                return ews * index;
 
-        uint offset = 0;
+//         uint offset = 0;
 
-        for(uint i = 1; i <= rank; ++i) {
-            arrLen /= shapeInfo[i];
-            if(arrLen > 0 && shapeInfo[i] > 1) {
-                offset += (index / arrLen) * shapeInfo[i + rank];
-                index %= arrLen;
-            }
-        }
-        return offset;
-    }
-
-    INLINEDEF _CUDA_HD Nd4jLong indexOffset(Nd4jLong index, const Nd4jLong* lShapeInfo, const uint* uShapeInfo, Nd4jLong arrLen, const bool useUnsigned) {
-
-        if(useUnsigned)
-            return getIndexOffset(static_cast<uint>(index), uShapeInfo, static_cast<uint>(arrLen));
-
-        return getIndexOffset(index, lShapeInfo, arrLen);
-    }
+//         for(uint i = 1; i <= rank; ++i) {
+//             arrLen /= shapeInfo[i];
+//             if(arrLen > 0 && shapeInfo[i] > 1) {
+//                 offset += (index / arrLen) * shapeInfo[i + rank];
+//                 index %= arrLen;
+//             }
+//         }
+//         return offset;
+//     }
 
 //////////////////////////////////////////////////////////////////////
-    INLINEDEF _CUDA_HD Nd4jLong getIndexOrderOffset(Nd4jLong index, const Nd4jLong *shapeInfo, Nd4jLong arrLen, const char order) {
+INLINEDEF _CUDA_HD Nd4jLong getIndexOffset(Nd4jLong index, const Nd4jLong *shapeInfo) {
 
-        Nd4jLong offset = 0;
-        if(order == 'c') {
-            for(int i = 1; i <= *shapeInfo; ++i) {
-                arrLen /= shapeInfo[i];
-                if(arrLen > 0 && shapeInfo[i] > 1) {
-                    offset += (index / arrLen) * shapeInfo[i + *shapeInfo];
-                    index %= arrLen;
-                }
-            }
-        }
-        else {
-            for(int i = *shapeInfo; i >= 1 ; --i) {
-                arrLen /= shapeInfo[i];
-                if(arrLen > 0 && shapeInfo[i] > 1) {
-                    offset += (index / arrLen) * shapeInfo[i + *shapeInfo];
-                    index %= arrLen;
-                }
-            }
-        }
-        return offset;
+    if (shapeInfo[2 * shapeInfo[0] + 3] == 99) {
+
+        const Nd4jLong ews = shapeInfo[2 * shapeInfo[0] + 2];
+        if (ews == 1)
+            return index;
+        else if(ews > 1)
+            return ews * index;
     }
 
+    Nd4jLong offset = 0;
+
+    for(uint i = shapeInfo[0]; i > 1; --i) {
+        offset += (index % shapeInfo[i]) * shapeInfo[i + shapeInfo[0]];
+        index /= shapeInfo[i];
+    }
+
+    offset += index * shapeInfo[1 + shapeInfo[0]];  // last iteration
+
+    return offset;
+}
+
+//////////////////////////////////////////////////////////////////////
+INLINEDEF _CUDA_HD uint getIndexOffset(uint index, const uint *shapeInfo) {
+
+    if (shapeInfo[2 * shapeInfo[0] + 3] == 99) {
+
+        const Nd4jLong ews = shapeInfo[2 * shapeInfo[0] + 2];
+        if (ews == 1)
+            return index;
+        else if(ews > 1)
+            return ews * index;
+    }
+
+    uint offset = 0;
+
+    for(uint i = shapeInfo[0]; i > 1; --i) {
+        offset += (index % shapeInfo[i]) * shapeInfo[i + shapeInfo[0]];
+        index /= shapeInfo[i];
+    }
+
+    offset += index * shapeInfo[1 + shapeInfo[0]];  // last iteration
+
+    return offset;
+}
+
+
+//////////////////////////////////////////////////////////////////////
+INLINEDEF _CUDA_HD Nd4jLong indexOffset(Nd4jLong index, const Nd4jLong* lShapeInfo, const uint* uShapeInfo, const bool useUnsigned) {
+
+    if(useUnsigned)
+        return getIndexOffset(static_cast<uint>(index), uShapeInfo);
+
+    return getIndexOffset(index, lShapeInfo);
+}
+
 /**
  *
  * @param length
@@ -2394,7 +2381,7 @@ template <typename T>
         auto indices = new Nd4jLong[rank];
         memset((void *) indices,0,rank * sizeof(Nd4jLong));
         indices[0] = sliceIdx;
-        Nd4jLong offset = shape::getOffset(0,newShape,newStride,indices,rank);
+        Nd4jLong offset = shape::getOffset(newShapeBuffer, indices);
         newShapeBuffer[shape::shapeInfoLength(newRank) - 3] = offset;
 
         // set current order and ews
@@ -3201,30 +3188,30 @@ INLINEDEF _CUDA_HD bool haveSameShapeAndStrides(const Nd4jLong *shapeInfo1, cons
 * @param indices the indices to iterate over
 * @return the double at the specified index
 */
-    INLINEDEF _CUDA_HD Nd4jLong getOffset(Nd4jLong baseOffset, const Nd4jLong *shape,  const Nd4jLong *stride,  const Nd4jLong *indices, const int rank) {
-        Nd4jLong offset = baseOffset;
-        for(int i = 0; i < rank; i++) {
-            if(shape[i] != 1)
-                offset += indices[i] * stride[i];
-        }
 
-        return offset;
-    }
+//////////////////////////////////////////////////////////////////////////
+INLINEDEF _CUDA_HD Nd4jLong getOffset(const Nd4jLong *shapeInfo, const Nd4jLong *indices, Nd4jLong baseOffset) {
 
-    INLINEDEF _CUDA_HD Nd4jLong getOffset(const Nd4jLong *shapeInfo, const Nd4jLong *indices, Nd4jLong baseOffset) {
-        return shape::getOffset(baseOffset, shape::shapeOf(const_cast<Nd4jLong*>(shapeInfo)), shape::stride(const_cast<Nd4jLong*>(shapeInfo)), indices, shapeInfo[0]);
-    }
+    Nd4jLong offset = baseOffset;
 
-    INLINEDEF Nd4jLong getOffset(const Nd4jLong *shapeInfo, const std::vector<uint>& indices) {
+    for(uint i = 1; i <= shapeInfo[0]; ++i)
+        if(shapeInfo[i] != 1)
+            offset += indices[i - 1] * shapeInfo[shapeInfo[0] + i];
 
-        Nd4jLong offset = 0;
+    return offset;
+}
 
-        for(uint i = 0; i < shapeInfo[0]; ++i)
-            if(shapeInfo[i + 1] != 1)
-                offset += indices[i] * shapeInfo[shapeInfo[0] + i + 1];
+//////////////////////////////////////////////////////////////////////////
+INLINEDEF Nd4jLong getOffset(const Nd4jLong *shapeInfo, const std::vector<uint>& indices) {
 
-        return offset;
-    }
+    Nd4jLong offset = 0;
+
+    for(uint i = 1; i <= shapeInfo[0]; ++i)
+        if(shapeInfo[i] != 1)
+            offset += indices[i - 1] * shapeInfo[shapeInfo[0] + i];
+
+    return offset;
+}
 
 
 
@@ -4209,24 +4196,24 @@ INLINEDEF _CUDA_HD void maxIndToMinInd(Nd4jLong* maxIdxs, Nd4jLong* minIdxs, con
     INLINEDEF _CUDA_HD Nd4jLong subArrayIndex(const Nd4jLong maxIdx, const Nd4jLong* maxShapeInfo, const Nd4jLong* minShapeInfo, const int* dimsToExclude, const int dimsLen) {
 
         Nd4jLong maxIdxs[MAX_RANK];
-        shape::index2coords(shape::rank(maxShapeInfo), const_cast<Nd4jLong *>(maxShapeInfo)+1, const_cast<Nd4jLong&>(maxIdx), maxIdxs, shape::order(maxShapeInfo));
+        shape::index2coords(const_cast<Nd4jLong&>(maxIdx), maxShapeInfo, maxIdxs);
 
         Nd4jLong minIdxs[MAX_RANK];
         maxIndToMinInd(maxIdxs, minIdxs, maxShapeInfo, minShapeInfo, dimsToExclude, dimsLen);
 
-        return coords2index(shape::rank(minShapeInfo), minShapeInfo + 1, minIdxs);
+        return shape::coords2index(minShapeInfo, minIdxs);
     }
 
     //////////////////////////////////////////////////////////////////////
     INLINEDEF _CUDA_HD Nd4jLong subArrayOffset(const Nd4jLong maxIdx, const Nd4jLong* maxShapeInfo, const Nd4jLong* minShapeInfo, const int* dimsToExclude, const int dimsLen) {
 
         Nd4jLong maxIdxs[MAX_RANK];
-        shape::index2coords(shape::rank(maxShapeInfo), const_cast<Nd4jLong *>(maxShapeInfo)+1, const_cast<Nd4jLong&>(maxIdx), maxIdxs, shape::order(maxShapeInfo));
+        shape::index2coords(const_cast<Nd4jLong&>(maxIdx), maxShapeInfo, maxIdxs);
 
         Nd4jLong minIdxs[MAX_RANK];
         maxIndToMinInd(maxIdxs, minIdxs, maxShapeInfo, minShapeInfo, dimsToExclude, dimsLen);
 
-        return getOffset(0, minShapeInfo + 1,  minShapeInfo + shape::rank(minShapeInfo) + 1, minIdxs, shape::rank(minShapeInfo));
+        return getOffset(minShapeInfo, minIdxs);
     }
 
     //////////////////////////////////////////////////////////////////////
@@ -4246,7 +4233,7 @@ INLINEDEF _CUDA_HD void maxIndToMinInd(Nd4jLong* maxIdxs, Nd4jLong* minIdxs, con
         int N, minI, maxI;
 
         // calculate min per-dim-indices which corresponds to absolute minIdx index
-        shape::index2coords(rankMin, minShapeInfo + 1, minIdx, indices, order(minShapeInfo));
+        shape::index2coords(minIdx, minShapeInfo, indices);
 
         // transform storage indices to contain per-dim max indices, purpose - memory saving
         // fill increment array as well
@@ -4277,7 +4264,7 @@ INLINEDEF _CUDA_HD void maxIndToMinInd(Nd4jLong* maxIdxs, Nd4jLong* minIdxs, con
         maxI = rankMax-1;
         N = 0;
         int step;
-        maxOffsets[N++] = shape::getOffset(0, maxShapeInfo + 1,  maxShapeInfo + rankMax + 1, indices, rankMax);
+        maxOffsets[N++] = shape::getOffset(maxShapeInfo, indices);
 
         // nested loops - producing of absolute indices for max array
         while(maxI >= 0) {
@@ -4290,7 +4277,7 @@ INLINEDEF _CUDA_HD void maxIndToMinInd(Nd4jLong* maxIdxs, Nd4jLong* minIdxs, con
                     step = -1;
                 }
                 else {
-                    maxOffsets[N++] = shape::getOffset(0, maxShapeInfo + 1,  maxShapeInfo + rankMax + 1, indices, rankMax);
+                    maxOffsets[N++] = shape::getOffset(maxShapeInfo, indices);
                     step =  rankMax - 1 - maxI;
                 }
             }
@@ -4322,7 +4309,7 @@ INLINEDEF _CUDA_HD void maxIndToMinInd(Nd4jLong* maxIdxs, Nd4jLong* minIdxs, con
         int N, minI, maxI;
 
         // calculate min per-dim-indices which corresponds to absolute minIdx index
-        shape::index2coords(rankMin, minShapeInfo + 1, minIdx, indices, order(minShapeInfo));
+        shape::index2coords(minIdx, minShapeInfo, indices);
 
         // transform storage indices to contain per-dim max indices, purpose - memory saving
         // fill increment array as well
@@ -4353,7 +4340,7 @@ INLINEDEF _CUDA_HD void maxIndToMinInd(Nd4jLong* maxIdxs, Nd4jLong* minIdxs, con
         maxI = rankMax-1;
         N = 0;
         int step;
-        maxIdxs[N++] = coords2index(rankMax, maxShapeInfo + 1, indices);
+        maxIdxs[N++] = shape::coords2index(maxShapeInfo, indices);
 
         // nested loops - producing of absolute indices for max array
         while(maxI >= 0) {
@@ -4366,7 +4353,7 @@ INLINEDEF _CUDA_HD void maxIndToMinInd(Nd4jLong* maxIdxs, Nd4jLong* minIdxs, con
                     step = -1;
                 }
                 else {
-                    maxIdxs[N++] = coords2index(rankMax, maxShapeInfo + 1, indices);
+                    maxIdxs[N++] = shape::coords2index(maxShapeInfo, indices);
                     step =  rankMax - 1 - maxI;
                 }
             }
@@ -4699,37 +4686,23 @@ INLINEDEF _CUDA_HD void calcSubArrShapeAndOffsets(const Nd4jLong* wholeShapeInfo
 }
 
 //////////////////////////////////////////////////////////////////////
-INLINEDEF void _CUDA_HD index2coords(const int rank, const Nd4jLong *shape, Nd4jLong index, Nd4jLong *coords, const char order) {
-    Nd4jLong arrLen = shape::prodLong(shape, rank);
-    shape::index2coords(rank, shape, index, arrLen, coords, order);
+INLINEDEF void _CUDA_HD index2coords(Nd4jLong index, const Nd4jLong *shapeInfo, Nd4jLong *coords) {
+
+    for(uint i = shapeInfo[0]; i > 1; --i) {
+        coords[i - 1] = index % shapeInfo[i];
+        index /= shapeInfo[i];
+    }
+    coords[0] = index;      // last iteration
 }
 
-INLINEDEF void _CUDA_HD index2coords(const int rank, const Nd4jLong *shape, Nd4jLong index, Nd4jLong arrLen, Nd4jLong *coords, const char order) {
+//////////////////////////////////////////////////////////////////////
+INLINEDEF void _CUDA_HD index2coords(Nd4jLong index, const int rank, const Nd4jLong *shape, Nd4jLong *coords) {
 
-    if(order == 'c') {
-
-        for(int i = 0; i < rank; i++) {
-            arrLen /= shape[i];
-            if(arrLen > 0 && shape[i] > 1) {
-                coords[i] = index / arrLen;
-                index %= arrLen;
-            }
-            else
-                coords[i] = 0;
-        }
-    }
-    else {
-
-        for(int i = rank - 1; i >= 0; i--) {
-            arrLen /= shape[i];
-            if(arrLen > 0 && shape[i] > 1) {
-                coords[i] = index / arrLen;
-                index %= arrLen;
-            }
-            else
-                coords[i] = 0;
-        }
+    for(uint i = rank - 1; i > 0; --i) {
+        coords[i] = index % shape[i];
+        index /= shape[i];
     }
+    coords[0] = index;      // last iteration
 }
 
 //////////////////////////////////////////////////////////////////////
diff --git a/libnd4j/include/loops/cpu/broadcasting.hpp b/libnd4j/include/loops/cpu/broadcasting.hpp
index dce9ca54b..3bd619827 100644
--- a/libnd4j/include/loops/cpu/broadcasting.hpp
+++ b/libnd4j/include/loops/cpu/broadcasting.hpp
@@ -170,13 +170,13 @@ namespace functions {
 
                     PRAGMA_OMP_PARALLEL_FOR_THREADS(threads)
                     for (int i = 0; i < tads; i++) {
-                        
+
                         auto oX = x + tadOffsets[i];
                         auto oZ = z + zTadOffset[i];
 
                         PRAGMA_OMP_SIMD
                         for (unsigned int f = 0; f < tadLength; f++) {
-                            auto offset = shape::indexOffset(f, xTadShapeShapeInfo, tadShapeShapeInfoCast, tadLength, canCastX);
+                            auto offset = shape::indexOffset(f, xTadShapeShapeInfo, tadShapeShapeInfoCast, canCastX);
                             oZ[offset] = OpType::op(oX[offset], y[offset]);
                         }
                     }
@@ -190,14 +190,14 @@ namespace functions {
 
                     PRAGMA_OMP_PARALLEL_FOR_THREADS(threads)
                     for (int i = 0; i < tads; i++) {
-                    
+
                         auto oZ = z + zTadOffset[i];
                         auto oX = x + tadOffsets[i];
 
                         PRAGMA_OMP_SIMD
                         for (int f = 0; f < tadLength; f++) {
-                            auto offset  = shape::indexOffset(f, xTadShapeShapeInfo, tadShapeShapeInfoCast, tadLength, canCastX);
-                            auto zOffset = shape::indexOffset(f, zTadShapeInfo, tadShapeInfoZCast, lenZ, canCastZ);
+                            auto offset  = shape::indexOffset(f, xTadShapeShapeInfo, tadShapeShapeInfoCast, canCastX);
+                            auto zOffset = shape::indexOffset(f, zTadShapeInfo, tadShapeInfoZCast, canCastZ);
                             oZ[zOffset] = OpType::op(oX[offset], y[offset]);
                         }
                     }
@@ -211,14 +211,14 @@ namespace functions {
 
                     PRAGMA_OMP_PARALLEL_FOR_THREADS(threads)
                     for (int i = 0; i < tads; i++) {
-                    
+
                         auto oZ = z + zTadOffset[i];
                         auto oX = x + tadOffsets[i];
 
                         PRAGMA_OMP_SIMD
                         for (int f = 0; f < tadLength; f++) {
-                            auto offset  = shape::indexOffset(f, xTadShapeShapeInfo, tadShapeShapeInfoCast, tadLength, canCastX);
-                            auto yOffset = shape::indexOffset(f, yShapeInfo, yShapeInfoCast, lenY, canCastY);
+                            auto offset  = shape::indexOffset(f, xTadShapeShapeInfo, tadShapeShapeInfoCast, canCastX);
+                            auto yOffset = shape::indexOffset(f, yShapeInfo, yShapeInfoCast, canCastY);
                             oZ[offset] = OpType::op(oX[offset], y[yOffset]);
                         }
                     }
@@ -232,14 +232,14 @@ namespace functions {
 
                     PRAGMA_OMP_PARALLEL_FOR_THREADS(threads)
                     for (int i = 0; i < tads; i++) {
-                    
+
                         auto oZ = z + zTadOffset[i];
                         auto oX = x + tadOffsets[i];
 
                         PRAGMA_OMP_SIMD
                         for (int f = 0; f < tadLength; f++) {
-                            auto xOffset  = shape::indexOffset(f, xTadShapeShapeInfo, tadShapeShapeInfoCast, tadLength, canCastX);
-                            auto offset = shape::indexOffset(f, yShapeInfo, yShapeInfoCast, lenY, canCastY);
+                            auto xOffset  = shape::indexOffset(f, xTadShapeShapeInfo, tadShapeShapeInfoCast, canCastX);
+                            auto offset = shape::indexOffset(f, yShapeInfo, yShapeInfoCast, canCastY);
                             oZ[offset] = OpType::op(oX[xOffset], y[offset]);
                         }
                     }
@@ -255,15 +255,15 @@ namespace functions {
 
                     PRAGMA_OMP_PARALLEL_FOR_THREADS(threads)
                     for (int i = 0; i < tads; i++) {
-                    
+
                         auto oZ = z + zTadOffset[i];
                         auto oX = x + tadOffsets[i];
 
                         PRAGMA_OMP_SIMD
                         for (int f = 0; f < tadLength; f++) {
-                            auto xOffset  = shape::indexOffset(f, xTadShapeShapeInfo, tadShapeShapeInfoCast, tadLength, canCastX);
-                            auto yOffset = shape::indexOffset(f, yShapeInfo, yShapeInfoCast, lenY, canCastY);
-                            auto zOffset  = shape::indexOffset(f, zTadShapeInfo, tadShapeInfoZCast, lenZ, canCastZ);
+                            auto xOffset  = shape::indexOffset(f, xTadShapeShapeInfo, tadShapeShapeInfoCast, canCastX);
+                            auto yOffset = shape::indexOffset(f, yShapeInfo, yShapeInfoCast, canCastY);
+                            auto zOffset  = shape::indexOffset(f, zTadShapeInfo, tadShapeInfoZCast, canCastZ);
                             oZ[zOffset] = OpType::op(oX[xOffset], y[yOffset]);
                         }
                     }
@@ -362,7 +362,7 @@ namespace functions {
 
                     PRAGMA_OMP_SIMD
                     for (unsigned int f = 0; f < tadLength; f++) {
-                        auto offset = shape::indexOffset(f, yTadShapeShapeInfo, tadShapeShapeInfoCast, tadLength, canCastY);
+                        auto offset = shape::indexOffset(f, yTadShapeShapeInfo, tadShapeShapeInfoCast, canCastY);
                         oZ[offset] = OpType::op(x[offset], oY[offset]);
                     }
                 }
@@ -382,8 +382,8 @@ namespace functions {
 
                     PRAGMA_OMP_SIMD
                     for (int f = 0; f < tadLength; f++) {
-                        auto offset  = shape::indexOffset(f, yTadShapeShapeInfo, tadShapeShapeInfoCast, tadLength, canCastY);
-                        auto zOffset = shape::indexOffset(f, zTadShapeInfo, tadShapeInfoZCast, lenZ, canCastZ);
+                        auto offset  = shape::indexOffset(f, yTadShapeShapeInfo, tadShapeShapeInfoCast, canCastY);
+                        auto zOffset = shape::indexOffset(f, zTadShapeInfo, tadShapeInfoZCast, canCastZ);
                         oZ[zOffset] = OpType::op(x[offset], oY[offset]);
                     }
                 }
@@ -403,8 +403,8 @@ namespace functions {
 
                     PRAGMA_OMP_SIMD
                     for (int f = 0; f < tadLength; f++) {
-                        auto offset  = shape::indexOffset(f, yTadShapeShapeInfo, tadShapeShapeInfoCast, tadLength, canCastY);
-                        auto xOffset = shape::indexOffset(f, yShapeInfo, xShapeInfoCast, lenX, canCastX);
+                        auto offset  = shape::indexOffset(f, yTadShapeShapeInfo, tadShapeShapeInfoCast, canCastY);
+                        auto xOffset = shape::indexOffset(f, yShapeInfo, xShapeInfoCast, canCastX);
                         oZ[offset] = OpType::op(x[xOffset], oY[offset]);
                     }
                 }
@@ -424,8 +424,8 @@ namespace functions {
 
                     PRAGMA_OMP_SIMD
                     for (int f = 0; f < tadLength; f++) {
-                        auto yOffset  = shape::indexOffset(f, yTadShapeShapeInfo, tadShapeShapeInfoCast, tadLength, canCastY);
-                        auto offset = shape::indexOffset(f, xShapeInfo, xShapeInfoCast, lenX, canCastX);
+                        auto yOffset  = shape::indexOffset(f, yTadShapeShapeInfo, tadShapeShapeInfoCast, canCastY);
+                        auto offset = shape::indexOffset(f, xShapeInfo, xShapeInfoCast, canCastX);
                         oZ[offset] = OpType::op(x[offset], oY[yOffset]);
                     }
                 }
@@ -447,9 +447,9 @@ namespace functions {
 
                     PRAGMA_OMP_SIMD
                     for (int f = 0; f < tadLength; f++) {
-                        auto xOffset = shape::indexOffset(f, xShapeInfo, xShapeInfoCast, lenX, canCastX);
-                        auto yOffset  = shape::indexOffset(f, yTadShapeShapeInfo, tadShapeShapeInfoCast, tadLength, canCastY);
-                        auto zOffset  = shape::indexOffset(f, zTadShapeInfo, tadShapeInfoZCast, lenZ, canCastZ);
+                        auto xOffset = shape::indexOffset(f, xShapeInfo, xShapeInfoCast, canCastX);
+                        auto yOffset  = shape::indexOffset(f, yTadShapeShapeInfo, tadShapeShapeInfoCast, canCastY);
+                        auto zOffset  = shape::indexOffset(f, zTadShapeInfo, tadShapeInfoZCast, canCastZ);
                         oZ[zOffset] = OpType::op(x[xOffset], oY[yOffset]);
                     }
                 }
diff --git a/libnd4j/include/loops/cpu/broadcasting_bool.cpp b/libnd4j/include/loops/cpu/broadcasting_bool.cpp
index 54950951c..bca423e3e 100644
--- a/libnd4j/include/loops/cpu/broadcasting_bool.cpp
+++ b/libnd4j/include/loops/cpu/broadcasting_bool.cpp
@@ -126,7 +126,7 @@ namespace functions {
                 if (zTadShapeInfo == nullptr) {
                     zTadShapeInfo = xTadShapeShapeInfo;
                     zTadOffset = tadOffsets;
-                }                                
+                }
 
                 auto lenZ = shape::length(zTadShapeInfo);
                 auto lenY = shape::length(yShapeInfo);
@@ -140,7 +140,7 @@ namespace functions {
                 auto zEws = shape::elementWiseStride(zTadShapeInfo);
 
                 const nd4j::LoopKind::Kind kindOfLoop = nd4j::LoopKind::deduceKindOfLoopXYZ(xTadShapeShapeInfo, yShapeInfo, zTadShapeInfo);
-                
+
                 if (kindOfLoop == nd4j::LoopKind::EWS1) {
                     PRAGMA_OMP_PARALLEL_FOR_THREADS(threads)
                     for (int i = 0; i < tads; i++) {
@@ -170,15 +170,15 @@ namespace functions {
 
                     PRAGMA_OMP_PARALLEL_FOR_THREADS(threads)
                     for (int i = 0; i < tads; i++) {
-                    
+
                         auto oZ = z + zTadOffset[i];
                         auto oX = x + tadOffsets[i];
-                                        
+
                         // TODO: cover this codebranch with tests
                         // all this stuff already happens within thread
                         PRAGMA_OMP_SIMD
                         for (int f = 0; f < tadLength; f++) {
-                            auto offset = shape::indexOffset(f, xTadShapeShapeInfo, tadShapeShapeInfoCast, tadLength, canCastX);
+                            auto offset = shape::indexOffset(f, xTadShapeShapeInfo, tadShapeShapeInfoCast, canCastX);
                             oZ[offset] = OpType::op(oX[offset], y[offset]);
                         }
                     }
@@ -192,14 +192,14 @@ namespace functions {
 
                     PRAGMA_OMP_PARALLEL_FOR_THREADS(threads)
                     for (int i = 0; i < tads; i++) {
-                    
+
                         auto oZ = z + zTadOffset[i];
                         auto oX = x + tadOffsets[i];
 
                         PRAGMA_OMP_SIMD
                         for (int f = 0; f < tadLength; f++) {
-                            auto offset  = shape::indexOffset(f, xTadShapeShapeInfo, tadShapeShapeInfoCast, tadLength, canCastX);
-                            auto zOffset = shape::indexOffset(f, zTadShapeInfo, tadShapeInfoZCast, lenZ, canCastZ);
+                            auto offset  = shape::indexOffset(f, xTadShapeShapeInfo, tadShapeShapeInfoCast, canCastX);
+                            auto zOffset = shape::indexOffset(f, zTadShapeInfo, tadShapeInfoZCast, canCastZ);
                             oZ[zOffset] = OpType::op(oX[offset], y[offset]);
                         }
                     }
@@ -213,14 +213,14 @@ namespace functions {
 
                     PRAGMA_OMP_PARALLEL_FOR_THREADS(threads)
                     for (int i = 0; i < tads; i++) {
-                    
+
                         auto oZ = z + zTadOffset[i];
                         auto oX = x + tadOffsets[i];
 
                         PRAGMA_OMP_SIMD
                         for (int f = 0; f < tadLength; f++) {
-                            auto offset  = shape::indexOffset(f, xTadShapeShapeInfo, tadShapeShapeInfoCast, tadLength, canCastX);
-                            auto yOffset = shape::indexOffset(f, yShapeInfo, yShapeInfoCast, lenY, canCastY);
+                            auto offset  = shape::indexOffset(f, xTadShapeShapeInfo, tadShapeShapeInfoCast, canCastX);
+                            auto yOffset = shape::indexOffset(f, yShapeInfo, yShapeInfoCast, canCastY);
                             oZ[offset] = OpType::op(oX[offset], y[yOffset]);
                         }
                     }
@@ -234,14 +234,14 @@ namespace functions {
 
                     PRAGMA_OMP_PARALLEL_FOR_THREADS(threads)
                     for (int i = 0; i < tads; i++) {
-                    
+
                         auto oZ = z + zTadOffset[i];
                         auto oX = x + tadOffsets[i];
 
                         PRAGMA_OMP_SIMD
                         for (int f = 0; f < tadLength; f++) {
-                            auto xOffset  = shape::indexOffset(f, xTadShapeShapeInfo, tadShapeShapeInfoCast, tadLength, canCastX);
-                            auto offset = shape::indexOffset(f, yShapeInfo, yShapeInfoCast, lenY, canCastY);
+                            auto xOffset  = shape::indexOffset(f, xTadShapeShapeInfo, tadShapeShapeInfoCast, canCastX);
+                            auto offset = shape::indexOffset(f, yShapeInfo, yShapeInfoCast, canCastY);
                             oZ[offset] = OpType::op(oX[xOffset], y[offset]);
                         }
                     }
@@ -257,15 +257,15 @@ namespace functions {
 
                     PRAGMA_OMP_PARALLEL_FOR_THREADS(threads)
                     for (int i = 0; i < tads; i++) {
-                    
+
                         auto oZ = z + zTadOffset[i];
                         auto oX = x + tadOffsets[i];
 
                         PRAGMA_OMP_SIMD
                         for (int f = 0; f < tadLength; f++) {
-                            auto xOffset  = shape::indexOffset(f, xTadShapeShapeInfo, tadShapeShapeInfoCast, tadLength, canCastX);
-                            auto yOffset = shape::indexOffset(f, yShapeInfo, yShapeInfoCast, lenY, canCastY);
-                            auto zOffset  = shape::indexOffset(f, zTadShapeInfo, tadShapeInfoZCast, lenZ, canCastZ);
+                            auto xOffset  = shape::indexOffset(f, xTadShapeShapeInfo, tadShapeShapeInfoCast, canCastX);
+                            auto yOffset = shape::indexOffset(f, yShapeInfo, yShapeInfoCast, canCastY);
+                            auto zOffset  = shape::indexOffset(f, zTadShapeInfo, tadShapeInfoZCast, canCastZ);
                             oZ[zOffset] = OpType::op(oX[xOffset], y[yOffset]);
                         }
                     }
@@ -365,7 +365,7 @@ namespace functions {
                         // all this stuff already happens within thread
                         PRAGMA_OMP_SIMD
                         for (int f = 0; f < tadLength; f++) {
-                            auto offset = shape::indexOffset(f, yTadShapeShapeInfo, tadShapeShapeInfoCast, tadLength, canCastY);
+                            auto offset = shape::indexOffset(f, yTadShapeShapeInfo, tadShapeShapeInfoCast, canCastY);
                             oZ[offset] = OpType::op(x[offset], oY[offset]);
                         }
                     }
@@ -385,8 +385,8 @@ namespace functions {
 
                         PRAGMA_OMP_SIMD
                         for (int f = 0; f < tadLength; f++) {
-                            auto offset  = shape::indexOffset(f, yTadShapeShapeInfo, tadShapeShapeInfoCast, tadLength, canCastY);
-                            auto zOffset = shape::indexOffset(f, zTadShapeInfo, tadShapeInfoZCast, lenZ, canCastZ);
+                            auto offset  = shape::indexOffset(f, yTadShapeShapeInfo, tadShapeShapeInfoCast, canCastY);
+                            auto zOffset = shape::indexOffset(f, zTadShapeInfo, tadShapeInfoZCast, canCastZ);
                             oZ[zOffset] = OpType::op(x[offset], oY[offset]);
                         }
                     }
@@ -406,8 +406,8 @@ namespace functions {
 
                         PRAGMA_OMP_SIMD
                         for (int f = 0; f < tadLength; f++) {
-                            auto offset  = shape::indexOffset(f, yTadShapeShapeInfo, tadShapeShapeInfoCast, tadLength, canCastY);
-                            auto xOffset = shape::indexOffset(f, xShapeInfo, xShapeInfoCast, lenX, canCastX);
+                            auto offset  = shape::indexOffset(f, yTadShapeShapeInfo, tadShapeShapeInfoCast, canCastY);
+                            auto xOffset = shape::indexOffset(f, xShapeInfo, xShapeInfoCast, canCastX);
                             oZ[offset] = OpType::op(x[xOffset], oY[offset]);
                         }
                     }
@@ -427,8 +427,8 @@ namespace functions {
 
                         PRAGMA_OMP_SIMD
                         for (int f = 0; f < tadLength; f++) {
-                            auto yOffset = shape::indexOffset(f, yTadShapeShapeInfo, tadShapeShapeInfoCast, tadLength, canCastY);
-                            auto offset  = shape::indexOffset(f, xShapeInfo, xShapeInfoCast, lenX, canCastX);
+                            auto yOffset = shape::indexOffset(f, yTadShapeShapeInfo, tadShapeShapeInfoCast, canCastY);
+                            auto offset  = shape::indexOffset(f, xShapeInfo, xShapeInfoCast, canCastX);
                             oZ[offset] = OpType::op(x[offset], oY[yOffset]);
                         }
                     }
@@ -450,9 +450,9 @@ namespace functions {
 
                         PRAGMA_OMP_SIMD
                         for (int f = 0; f < tadLength; f++) {
-                            auto xOffset = shape::indexOffset(f, xShapeInfo, xShapeInfoCast, lenX, canCastX);
-                            auto yOffset = shape::indexOffset(f, yTadShapeShapeInfo, tadShapeShapeInfoCast, tadLength, canCastY);
-                            auto zOffset = shape::indexOffset(f, zTadShapeInfo, tadShapeInfoZCast, lenZ, canCastZ);
+                            auto xOffset = shape::indexOffset(f, xShapeInfo, xShapeInfoCast, canCastX);
+                            auto yOffset = shape::indexOffset(f, yTadShapeShapeInfo, tadShapeShapeInfoCast, canCastY);
+                            auto zOffset = shape::indexOffset(f, zTadShapeInfo, tadShapeInfoZCast, canCastZ);
                             oZ[zOffset] = OpType::op(x[xOffset], oY[yOffset]);
                         }
                     }
diff --git a/libnd4j/include/loops/cpu/broadcasting_int.cpp b/libnd4j/include/loops/cpu/broadcasting_int.cpp
index c092da50b..375d7577a 100644
--- a/libnd4j/include/loops/cpu/broadcasting_int.cpp
+++ b/libnd4j/include/loops/cpu/broadcasting_int.cpp
@@ -126,7 +126,7 @@ namespace functions {
                 if (zTadShapeInfo == nullptr) {
                     zTadShapeInfo = xTadShapeShapeInfo;
                     zTadOffset = tadOffsets;
-                }                                
+                }
 
                 auto lenZ = shape::length(zTadShapeInfo);
                 auto lenY = shape::length(yShapeInfo);
@@ -140,7 +140,7 @@ namespace functions {
                 auto zEws = shape::elementWiseStride(zTadShapeInfo);
 
                 const nd4j::LoopKind::Kind kindOfLoop = nd4j::LoopKind::deduceKindOfLoopXYZ(xTadShapeShapeInfo, yShapeInfo, zTadShapeInfo);
-                
+
                 if (kindOfLoop == nd4j::LoopKind::EWS1) {
                     PRAGMA_OMP_PARALLEL_FOR_THREADS(threads)
                     for (int i = 0; i < tads; i++) {
@@ -170,15 +170,15 @@ namespace functions {
 
                     PRAGMA_OMP_PARALLEL_FOR_THREADS(threads)
                     for (int i = 0; i < tads; i++) {
-                    
+
                         auto oZ = z + zTadOffset[i];
                         auto oX = x + tadOffsets[i];
-                                        
+
                         // TODO: cover this codebranch with tests
                         // all this stuff already happens within thread
                         PRAGMA_OMP_SIMD
                         for (int f = 0; f < tadLength; f++) {
-                            auto offset = shape::indexOffset(f, xTadShapeShapeInfo, tadShapeShapeInfoCast, tadLength, canCastX);
+                            auto offset = shape::indexOffset(f, xTadShapeShapeInfo, tadShapeShapeInfoCast, canCastX);
                             oZ[offset] = OpType::op(oX[offset], y[offset]);
                         }
                     }
@@ -192,14 +192,14 @@ namespace functions {
 
                     PRAGMA_OMP_PARALLEL_FOR_THREADS(threads)
                     for (int i = 0; i < tads; i++) {
-                    
+
                         auto oZ = z + zTadOffset[i];
                         auto oX = x + tadOffsets[i];
 
                         PRAGMA_OMP_SIMD
                         for (int f = 0; f < tadLength; f++) {
-                            auto offset  = shape::indexOffset(f, xTadShapeShapeInfo, tadShapeShapeInfoCast, tadLength, canCastX);
-                            auto zOffset = shape::indexOffset(f, zTadShapeInfo, tadShapeInfoZCast, lenZ, canCastZ);
+                            auto offset  = shape::indexOffset(f, xTadShapeShapeInfo, tadShapeShapeInfoCast, canCastX);
+                            auto zOffset = shape::indexOffset(f, zTadShapeInfo, tadShapeInfoZCast, canCastZ);
                             oZ[zOffset] = OpType::op(oX[offset], y[offset]);
                         }
                     }
@@ -213,14 +213,14 @@ namespace functions {
 
                     PRAGMA_OMP_PARALLEL_FOR_THREADS(threads)
                     for (int i = 0; i < tads; i++) {
-                    
+
                         auto oZ = z + zTadOffset[i];
                         auto oX = x + tadOffsets[i];
 
                         PRAGMA_OMP_SIMD
                         for (int f = 0; f < tadLength; f++) {
-                            auto offset  = shape::indexOffset(f, xTadShapeShapeInfo, tadShapeShapeInfoCast, tadLength, canCastX);
-                            auto yOffset = shape::indexOffset(f, yShapeInfo, yShapeInfoCast, lenY, canCastY);
+                            auto offset  = shape::indexOffset(f, xTadShapeShapeInfo, tadShapeShapeInfoCast, canCastX);
+                            auto yOffset = shape::indexOffset(f, yShapeInfo, yShapeInfoCast, canCastY);
                             oZ[offset] = OpType::op(oX[offset], y[yOffset]);
                         }
                     }
@@ -234,14 +234,14 @@ namespace functions {
 
                     PRAGMA_OMP_PARALLEL_FOR_THREADS(threads)
                     for (int i = 0; i < tads; i++) {
-                    
+
                         auto oZ = z + zTadOffset[i];
                         auto oX = x + tadOffsets[i];
 
                         PRAGMA_OMP_SIMD
                         for (int f = 0; f < tadLength; f++) {
-                            auto xOffset  = shape::indexOffset(f, xTadShapeShapeInfo, tadShapeShapeInfoCast, tadLength, canCastX);
-                            auto offset = shape::indexOffset(f, yShapeInfo, yShapeInfoCast, lenY, canCastY);
+                            auto xOffset  = shape::indexOffset(f, xTadShapeShapeInfo, tadShapeShapeInfoCast, canCastX);
+                            auto offset = shape::indexOffset(f, yShapeInfo, yShapeInfoCast, canCastY);
                             oZ[offset] = OpType::op(oX[xOffset], y[offset]);
                         }
                     }
@@ -257,15 +257,15 @@ namespace functions {
 
                     PRAGMA_OMP_PARALLEL_FOR_THREADS(threads)
                     for (int i = 0; i < tads; i++) {
-                    
+
                         auto oZ = z + zTadOffset[i];
                         auto oX = x + tadOffsets[i];
 
                         PRAGMA_OMP_SIMD
                         for (int f = 0; f < tadLength; f++) {
-                            auto xOffset  = shape::indexOffset(f, xTadShapeShapeInfo, tadShapeShapeInfoCast, tadLength, canCastX);
-                            auto yOffset = shape::indexOffset(f, yShapeInfo, yShapeInfoCast, lenY, canCastY);
-                            auto zOffset  = shape::indexOffset(f, zTadShapeInfo, tadShapeInfoZCast, lenZ, canCastZ);
+                            auto xOffset  = shape::indexOffset(f, xTadShapeShapeInfo, tadShapeShapeInfoCast, canCastX);
+                            auto yOffset = shape::indexOffset(f, yShapeInfo, yShapeInfoCast, canCastY);
+                            auto zOffset  = shape::indexOffset(f, zTadShapeInfo, tadShapeInfoZCast, canCastZ);
                             oZ[zOffset] = OpType::op(oX[xOffset], y[yOffset]);
                         }
                     }
@@ -365,7 +365,7 @@ namespace functions {
                         // all this stuff already happens within thread
                         PRAGMA_OMP_SIMD
                         for (int f = 0; f < tadLength; f++) {
-                            auto offset = shape::indexOffset(f, yTadShapeShapeInfo, tadShapeShapeInfoCast, tadLength, canCastY);
+                            auto offset = shape::indexOffset(f, yTadShapeShapeInfo, tadShapeShapeInfoCast, canCastY);
                             oZ[offset] = OpType::op(x[offset], oY[offset]);
                         }
                     }
@@ -385,8 +385,8 @@ namespace functions {
 
                         PRAGMA_OMP_SIMD
                         for (int f = 0; f < tadLength; f++) {
-                            auto offset  = shape::indexOffset(f, yTadShapeShapeInfo, tadShapeShapeInfoCast, tadLength, canCastY);
-                            auto zOffset = shape::indexOffset(f, zTadShapeInfo, tadShapeInfoZCast, lenZ, canCastZ);
+                            auto offset  = shape::indexOffset(f, yTadShapeShapeInfo, tadShapeShapeInfoCast, canCastY);
+                            auto zOffset = shape::indexOffset(f, zTadShapeInfo, tadShapeInfoZCast, canCastZ);
                             oZ[zOffset] = OpType::op(x[offset], oY[offset]);
                         }
                     }
@@ -406,8 +406,8 @@ namespace functions {
 
                         PRAGMA_OMP_SIMD
                         for (int f = 0; f < tadLength; f++) {
-                            auto offset  = shape::indexOffset(f, yTadShapeShapeInfo, tadShapeShapeInfoCast, tadLength, canCastY);
-                            auto xOffset = shape::indexOffset(f, xShapeInfo, xShapeInfoCast, lenX, canCastX);
+                            auto offset  = shape::indexOffset(f, yTadShapeShapeInfo, tadShapeShapeInfoCast, canCastY);
+                            auto xOffset = shape::indexOffset(f, xShapeInfo, xShapeInfoCast, canCastX);
                             oZ[offset] = OpType::op(x[xOffset], oY[offset]);
                         }
                     }
@@ -427,8 +427,8 @@ namespace functions {
 
                         PRAGMA_OMP_SIMD
                         for (int f = 0; f < tadLength; f++) {
-                            auto yOffset = shape::indexOffset(f, yTadShapeShapeInfo, tadShapeShapeInfoCast, tadLength, canCastY);
-                            auto offset  = shape::indexOffset(f, xShapeInfo, xShapeInfoCast, lenX, canCastX);
+                            auto yOffset = shape::indexOffset(f, yTadShapeShapeInfo, tadShapeShapeInfoCast, canCastY);
+                            auto offset  = shape::indexOffset(f, xShapeInfo, xShapeInfoCast, canCastX);
                             oZ[offset] = OpType::op(x[offset], oY[yOffset]);
                         }
                     }
@@ -450,9 +450,9 @@ namespace functions {
 
                         PRAGMA_OMP_SIMD
                         for (int f = 0; f < tadLength; f++) {
-                            auto xOffset = shape::indexOffset(f, xShapeInfo, xShapeInfoCast, lenX, canCastX);
-                            auto yOffset = shape::indexOffset(f, yTadShapeShapeInfo, tadShapeShapeInfoCast, tadLength, canCastY);
-                            auto zOffset = shape::indexOffset(f, zTadShapeInfo, tadShapeInfoZCast, lenZ, canCastZ);
+                            auto xOffset = shape::indexOffset(f, xShapeInfo, xShapeInfoCast, canCastX);
+                            auto yOffset = shape::indexOffset(f, yTadShapeShapeInfo, tadShapeShapeInfoCast, canCastY);
+                            auto zOffset = shape::indexOffset(f, zTadShapeInfo, tadShapeInfoZCast, canCastZ);
                             oZ[zOffset] = OpType::op(x[xOffset], oY[yOffset]);
                         }
                     }
diff --git a/libnd4j/include/loops/cpu/indexreduce.cpp b/libnd4j/include/loops/cpu/indexreduce.cpp
index 5a7beee24..23286ecd9 100644
--- a/libnd4j/include/loops/cpu/indexreduce.cpp
+++ b/libnd4j/include/loops/cpu/indexreduce.cpp
@@ -92,7 +92,7 @@ Nd4jLong IndexReduce<X, Y>::execScalar(void *vx, Nd4jLong *xShapeInfo, void *vex
             auto ulen = info.getItersPerThread(threadNum);
 
             for (Nd4jLong i = 0; i < ulen; i++) {
-                auto offset = shape::indexOffset(threadOffset + i, xShapeInfo, xShapeInfoCast, len, canCastX);
+                auto offset = shape::indexOffset(threadOffset + i, xShapeInfo, xShapeInfoCast, canCastX);
                 IndexValue<X> curr(x[offset], threadOffset + i);
                 local = OpType::update(local, curr, extraParams);
             }
diff --git a/libnd4j/include/loops/cpu/pairwise.hpp b/libnd4j/include/loops/cpu/pairwise.hpp
index 6b0c8cb49..9dfa129aa 100644
--- a/libnd4j/include/loops/cpu/pairwise.hpp
+++ b/libnd4j/include/loops/cpu/pairwise.hpp
@@ -137,7 +137,7 @@ namespace functions {
                 void *vz,
                 Nd4jLong* zShapeInfo,
                 void *vextraParams) {
-            
+
             auto x = reinterpret_cast<X *>(vx);
             auto y = reinterpret_cast<Y *>(vy);
             auto z = reinterpret_cast<Z *>(vz);
@@ -152,13 +152,13 @@ namespace functions {
 
             if (shape::isScalar(yShapeInfo)) {
 
-                uint xShapeInfoCast[MAX_RANK];                    
+                uint xShapeInfoCast[MAX_RANK];
                 const bool canCastX = nd4j::DataTypeUtils::castShapeInfo(xShapeInfo, xShapeInfoCast);
-                                    
+
                 if(shape::haveSameShapeAndStrides(xShapeInfo, zShapeInfo)) {
 
                     PRAGMA_OMP_PARALLEL_THREADS(info._numThreads)
-                    {                
+                    {
                         auto threadNum = omp_get_thread_num();
                         auto threadOffset = info.getThreadOffset(threadNum);
 
@@ -166,25 +166,25 @@ namespace functions {
 
                         PRAGMA_OMP_SIMD
                         for(unsigned int i = 0; i < ulen; i++)  {
-                            auto offset = shape::indexOffset(i + threadOffset, xShapeInfo, xShapeInfoCast, n, canCastX);
+                            auto offset = shape::indexOffset(i + threadOffset, xShapeInfo, xShapeInfoCast, canCastX);
                             z[offset] = OpType::op(x[offset], y[0], extraParams);
                         }
                     }
                 }
                 else {
-                    uint zShapeInfoCast[MAX_RANK];                    
+                    uint zShapeInfoCast[MAX_RANK];
                     const bool canCastZ = nd4j::DataTypeUtils::castShapeInfo(zShapeInfo, zShapeInfoCast);
 
                     PRAGMA_OMP_PARALLEL_THREADS(info._numThreads)
-                    {                
+                    {
                         auto threadNum = omp_get_thread_num();
                         auto threadOffset = info.getThreadOffset(threadNum);
                         auto ulen = static_cast<unsigned int>(info.getItersPerThread(threadNum));
 
                         PRAGMA_OMP_SIMD
                         for(unsigned int i = 0; i < ulen; i++)  {
-                            auto xOffset = shape::indexOffset(i + threadOffset, xShapeInfo, xShapeInfoCast, n, canCastX);
-                            auto zOffset = shape::indexOffset(i + threadOffset, zShapeInfo, zShapeInfoCast, n, canCastZ);
+                            auto xOffset = shape::indexOffset(i + threadOffset, xShapeInfo, xShapeInfoCast, canCastX);
+                            auto zOffset = shape::indexOffset(i + threadOffset, zShapeInfo, zShapeInfoCast, canCastZ);
                             z[zOffset] = OpType::op(x[xOffset], y[0], extraParams);
                         }
                     }
@@ -192,18 +192,18 @@ namespace functions {
                 return;
             }
 
-            
+
 
             const nd4j::LoopKind::Kind kindOfLoop = nd4j::LoopKind::deduceKindOfLoopXYZ(xShapeInfo, yShapeInfo, zShapeInfo);
             const bool sameShapesXY = shape::shapeEquals(xShapeInfo, yShapeInfo);
 
             if ((kindOfLoop == nd4j::LoopKind::EWS1 || kindOfLoop == nd4j::LoopKind::EWSNONZERO) && sameShapesXY) {
                 exec<OpType>(x, xEws, y, yEws, z, zEws, extraParams, n);
-            }            
+            }
             else if ((kindOfLoop == nd4j::LoopKind::EWS1 || kindOfLoop == nd4j::LoopKind::EWSNONZERO) && !sameShapesXY) { //not same shape
                 exec<OpType>(x, xEws, y, yEws, z, zEws, extraParams, shape::length(yShapeInfo));
-            }                
-            else {                
+            }
+            else {
 
                 if(shape::haveSameShapeAndStrides(xShapeInfo, yShapeInfo) && shape::haveSameShapeAndStrides(xShapeInfo, zShapeInfo)) {
 
@@ -211,14 +211,14 @@ namespace functions {
                     bool canCastX = nd4j::DataTypeUtils::castShapeInfo(xShapeInfo, xShapeInfoCast);
 
                     PRAGMA_OMP_PARALLEL_THREADS(info._numThreads)
-                    {                
+                    {
                         auto threadNum = omp_get_thread_num();
                         auto threadOffset = info.getThreadOffset(threadNum);
                         auto ulen = static_cast<unsigned int>(info.getItersPerThread(threadNum));
 
                         PRAGMA_OMP_SIMD
                         for (unsigned int i = 0; i < ulen; i++)  {
-                            auto offset = shape::indexOffset(i + threadOffset, xShapeInfo, xShapeInfoCast, n, canCastX);
+                            auto offset = shape::indexOffset(i + threadOffset, xShapeInfo, xShapeInfoCast, canCastX);
                             z[offset] = OpType::op(x[offset], y[offset], extraParams);
                         }
                     }
@@ -231,15 +231,15 @@ namespace functions {
                     bool canCastZ = nd4j::DataTypeUtils::castShapeInfo(zShapeInfo, zShapeInfoCast);
 
                     PRAGMA_OMP_PARALLEL_THREADS(info._numThreads)
-                    {                
+                    {
                         auto threadNum = omp_get_thread_num();
                         auto threadOffset = info.getThreadOffset(threadNum);
                         auto ulen = static_cast<unsigned int>(info.getItersPerThread(threadNum));
 
                         PRAGMA_OMP_SIMD
                         for (unsigned int i = 0; i < ulen; i++)  {
-                            auto offset  = shape::indexOffset(i + threadOffset, xShapeInfo, xShapeInfoCast, n, canCastX);
-                            auto zOffset = shape::indexOffset(i + threadOffset, zShapeInfo, zShapeInfoCast, n, canCastZ);
+                            auto offset  = shape::indexOffset(i + threadOffset, xShapeInfo, xShapeInfoCast, canCastX);
+                            auto zOffset = shape::indexOffset(i + threadOffset, zShapeInfo, zShapeInfoCast, canCastZ);
                             z[zOffset] = OpType::op(x[offset], y[offset], extraParams);
                         }
                     }
@@ -252,15 +252,15 @@ namespace functions {
                     bool canCastY = nd4j::DataTypeUtils::castShapeInfo(yShapeInfo, yShapeInfoCast);
 
                     PRAGMA_OMP_PARALLEL_THREADS(info._numThreads)
-                    {                
+                    {
                         auto threadNum = omp_get_thread_num();
                         auto threadOffset = info.getThreadOffset(threadNum);
                         auto ulen = static_cast<unsigned int>(info.getItersPerThread(threadNum));
 
                         PRAGMA_OMP_SIMD
                         for (unsigned int i = 0; i < ulen; i++)  {
-                            auto offset  = shape::indexOffset(i + threadOffset, xShapeInfo, xShapeInfoCast, n, canCastX);
-                            auto yOffset = shape::indexOffset(i + threadOffset, yShapeInfo, yShapeInfoCast, n, canCastY);
+                            auto offset  = shape::indexOffset(i + threadOffset, xShapeInfo, xShapeInfoCast, canCastX);
+                            auto yOffset = shape::indexOffset(i + threadOffset, yShapeInfo, yShapeInfoCast, canCastY);
                             z[offset] = OpType::op(x[offset], y[yOffset], extraParams);
                         }
                     }
@@ -273,15 +273,15 @@ namespace functions {
                     bool canCastY = nd4j::DataTypeUtils::castShapeInfo(yShapeInfo, yShapeInfoCast);
 
                     PRAGMA_OMP_PARALLEL_THREADS(info._numThreads)
-                    {                
+                    {
                         auto threadNum = omp_get_thread_num();
                         auto threadOffset = info.getThreadOffset(threadNum);
                         auto ulen = static_cast<unsigned int>(info.getItersPerThread(threadNum));
 
                         PRAGMA_OMP_SIMD
                         for (unsigned int i = 0; i < ulen; i++)  {
-                            auto xOffset = shape::indexOffset(i + threadOffset, xShapeInfo, xShapeInfoCast, n, canCastX);
-                            auto offset  = shape::indexOffset(i + threadOffset, yShapeInfo, yShapeInfoCast, n, canCastY);
+                            auto xOffset = shape::indexOffset(i + threadOffset, xShapeInfo, xShapeInfoCast, canCastX);
+                            auto offset  = shape::indexOffset(i + threadOffset, yShapeInfo, yShapeInfoCast, canCastY);
                             z[offset] = OpType::op(x[xOffset], y[offset], extraParams);
                         }
                     }
@@ -296,16 +296,16 @@ namespace functions {
                     bool canCastZ = nd4j::DataTypeUtils::castShapeInfo(zShapeInfo, zShapeInfoCast);
 
                     PRAGMA_OMP_PARALLEL_THREADS(info._numThreads)
-                    {                
+                    {
                         auto threadNum = omp_get_thread_num();
                         auto threadOffset = info.getThreadOffset(threadNum);
                         auto ulen = static_cast<unsigned int>(info.getItersPerThread(threadNum));
 
                         PRAGMA_OMP_SIMD
                         for (unsigned int i = 0; i < ulen; i++)  {
-                            auto xOffset = shape::indexOffset(i + threadOffset, xShapeInfo, xShapeInfoCast, n, canCastX);
-                            auto yOffset = shape::indexOffset(i + threadOffset, yShapeInfo, yShapeInfoCast, n, canCastY);
-                            auto zOffset = shape::indexOffset(i + threadOffset, zShapeInfo, zShapeInfoCast, n, canCastZ);
+                            auto xOffset = shape::indexOffset(i + threadOffset, xShapeInfo, xShapeInfoCast, canCastX);
+                            auto yOffset = shape::indexOffset(i + threadOffset, yShapeInfo, yShapeInfoCast, canCastY);
+                            auto zOffset = shape::indexOffset(i + threadOffset, zShapeInfo, zShapeInfoCast, canCastZ);
                             z[zOffset] = OpType::op(x[xOffset], y[yOffset], extraParams);
                         }
                     }
diff --git a/libnd4j/include/loops/cpu/pairwise_bool.cpp b/libnd4j/include/loops/cpu/pairwise_bool.cpp
index 30d093bce..8feabb98a 100644
--- a/libnd4j/include/loops/cpu/pairwise_bool.cpp
+++ b/libnd4j/include/loops/cpu/pairwise_bool.cpp
@@ -61,7 +61,7 @@ namespace functions {
                                               Nd4jLong zEws,
                                               void *vextraParams,
                                               const Nd4jLong n) {
-            
+
             auto x = reinterpret_cast<X *>(vx);
             auto y = reinterpret_cast<X *>(vy);
             auto z = reinterpret_cast<Z *>(vz);
@@ -72,9 +72,9 @@ namespace functions {
             if (xEws == 1 && yEws == 1 && zEws == 1) {
 
                 PRAGMA_OMP_PARALLEL_THREADS(info._numThreads)
-                {                
+                {
                     auto threadNum = omp_get_thread_num();
-                    Nd4jLong threadOffset = info.getThreadOffset(threadNum);        
+                    Nd4jLong threadOffset = info.getThreadOffset(threadNum);
                     auto xi = x + threadOffset;
                     auto yi = y + threadOffset;
                     auto zi = z + threadOffset;
@@ -88,9 +88,9 @@ namespace functions {
             else {
 
                 PRAGMA_OMP_PARALLEL_THREADS(info._numThreads)
-                {                
+                {
                     auto threadNum = omp_get_thread_num();
-                    Nd4jLong threadOffset = info.getThreadOffset(threadNum);        
+                    Nd4jLong threadOffset = info.getThreadOffset(threadNum);
                     auto xi = x + xEws*threadOffset;
                     auto yi = y + yEws*threadOffset;
                     auto zi = z + zEws*threadOffset;
@@ -151,33 +151,33 @@ namespace functions {
                 if(shape::haveSameShapeAndStrides(xShapeInfo, zShapeInfo)) {
 
                     PRAGMA_OMP_PARALLEL_THREADS(info._numThreads)
-                    {                
+                    {
                         auto threadNum = omp_get_thread_num();
                         auto threadOffset = info.getThreadOffset(threadNum);
                         auto ulen = static_cast<unsigned int>(info.getItersPerThread(threadNum));
 
                         PRAGMA_OMP_SIMD
                         for(Nd4jLong i = 0; i < ulen; i++)  {
-                            auto offset = shape::indexOffset(i + threadOffset, xShapeInfo, xShapeInfoCast, n, canCastX);
+                            auto offset = shape::indexOffset(i + threadOffset, xShapeInfo, xShapeInfoCast, canCastX);
                             z[offset] = OpType::op(x[offset], y[0], extraParams);
                         }
                     }
                 }
                 else {
-                    
+
                     uint zShapeInfoCast[MAX_RANK];
                     const bool canCastZ = nd4j::DataTypeUtils::castShapeInfo(zShapeInfo, zShapeInfoCast);
 
                     PRAGMA_OMP_PARALLEL_THREADS(info._numThreads)
-                    {                
+                    {
                         auto threadNum = omp_get_thread_num();
                         auto threadOffset = info.getThreadOffset(threadNum);
                         auto ulen = static_cast<unsigned int>(info.getItersPerThread(threadNum));
 
                         PRAGMA_OMP_SIMD
                         for(Nd4jLong i = 0; i < ulen; i++)  {
-                            auto xOffset = shape::indexOffset(i + threadOffset, xShapeInfo, xShapeInfoCast, n, canCastX);
-                            auto zOffset = shape::indexOffset(i + threadOffset, zShapeInfo, zShapeInfoCast, n, canCastZ);
+                            auto xOffset = shape::indexOffset(i + threadOffset, xShapeInfo, xShapeInfoCast, canCastX);
+                            auto zOffset = shape::indexOffset(i + threadOffset, zShapeInfo, zShapeInfoCast, canCastZ);
                             z[zOffset] = OpType::op(x[xOffset], y[0], extraParams);
                         }
                     }
@@ -190,11 +190,11 @@ namespace functions {
 
             if ((kindOfLoop == nd4j::LoopKind::EWS1 || kindOfLoop == nd4j::LoopKind::EWSNONZERO) && sameShapesXY) {
                 exec<OpType>(x, xEws, y, yEws, z, zEws, extraParams, n);
-            }            
+            }
             else if ((kindOfLoop == nd4j::LoopKind::EWS1 || kindOfLoop == nd4j::LoopKind::EWSNONZERO) && !sameShapesXY) { //not same shape
                 exec<OpType>(x, xEws, y, yEws, z, zEws, extraParams, shape::length(yShapeInfo));
             }
-            else {                
+            else {
 
                 if(shape::haveSameShapeAndStrides(xShapeInfo, yShapeInfo) && shape::haveSameShapeAndStrides(xShapeInfo, zShapeInfo)) {
 
@@ -202,83 +202,83 @@ namespace functions {
                     const bool canCastX = nd4j::DataTypeUtils::castShapeInfo(xShapeInfo, xShapeInfoCast);
 
                     PRAGMA_OMP_PARALLEL_THREADS(info._numThreads)
-                    {                
+                    {
                         auto threadNum = omp_get_thread_num();
                         auto threadOffset = info.getThreadOffset(threadNum);
                         auto ulen = static_cast<unsigned int>(info.getItersPerThread(threadNum));
 
                         PRAGMA_OMP_SIMD
                         for (Nd4jLong i = 0; i < ulen; i++)  {
-                            auto offset = shape::indexOffset(i + threadOffset, xShapeInfo, xShapeInfoCast, n, canCastX);
+                            auto offset = shape::indexOffset(i + threadOffset, xShapeInfo, xShapeInfoCast, canCastX);
                             z[offset] = OpType::op(x[offset], y[offset], extraParams);
                         }
                     }
                 }
                 else if(shape::haveSameShapeAndStrides(xShapeInfo, yShapeInfo)) {
-                    
+
                     uint xShapeInfoCast[MAX_RANK];
                     uint zShapeInfoCast[MAX_RANK];
                     const bool canCastX = nd4j::DataTypeUtils::castShapeInfo(xShapeInfo, xShapeInfoCast);
                     const bool canCastZ = nd4j::DataTypeUtils::castShapeInfo(zShapeInfo, zShapeInfoCast);
 
                     PRAGMA_OMP_PARALLEL_THREADS(info._numThreads)
-                    {                
+                    {
                         auto threadNum = omp_get_thread_num();
                         auto threadOffset = info.getThreadOffset(threadNum);
                         auto ulen = static_cast<unsigned int>(info.getItersPerThread(threadNum));
 
                         PRAGMA_OMP_SIMD
                         for (Nd4jLong i = 0; i < ulen; i++)  {
-                            auto offset  = shape::indexOffset(i + threadOffset, xShapeInfo, xShapeInfoCast, n, canCastX);                            
-                            auto zOffset = shape::indexOffset(i + threadOffset, zShapeInfo, zShapeInfoCast, n, canCastZ);
+                            auto offset  = shape::indexOffset(i + threadOffset, xShapeInfo, xShapeInfoCast, canCastX);
+                            auto zOffset = shape::indexOffset(i + threadOffset, zShapeInfo, zShapeInfoCast, canCastZ);
                             z[zOffset] = OpType::op(x[offset], y[offset], extraParams);
                         }
                     }
                 }
                 else if(shape::haveSameShapeAndStrides(xShapeInfo, zShapeInfo)) {
-                    
+
                     uint xShapeInfoCast[MAX_RANK];
                     uint yShapeInfoCast[MAX_RANK];
                     const bool canCastX = nd4j::DataTypeUtils::castShapeInfo(xShapeInfo, xShapeInfoCast);
                     const bool canCastY = nd4j::DataTypeUtils::castShapeInfo(yShapeInfo, yShapeInfoCast);
 
                     PRAGMA_OMP_PARALLEL_THREADS(info._numThreads)
-                    {                
+                    {
                         auto threadNum = omp_get_thread_num();
                         auto threadOffset = info.getThreadOffset(threadNum);
                         auto ulen = static_cast<unsigned int>(info.getItersPerThread(threadNum));
 
                         PRAGMA_OMP_SIMD
                         for (Nd4jLong i = 0; i < ulen; i++)  {
-                            auto offset  = shape::indexOffset(i + threadOffset, xShapeInfo, xShapeInfoCast, n, canCastX);                            
-                            auto yOffset = shape::indexOffset(i + threadOffset, yShapeInfo, yShapeInfoCast, n, canCastY);
+                            auto offset  = shape::indexOffset(i + threadOffset, xShapeInfo, xShapeInfoCast, canCastX);
+                            auto yOffset = shape::indexOffset(i + threadOffset, yShapeInfo, yShapeInfoCast, canCastY);
                             z[offset] = OpType::op(x[offset], y[yOffset], extraParams);
                         }
                     }
                 }
                 else if(shape::haveSameShapeAndStrides(yShapeInfo, zShapeInfo)) {
-                    
+
                     uint xShapeInfoCast[MAX_RANK];
                     uint yShapeInfoCast[MAX_RANK];
                     const bool canCastX = nd4j::DataTypeUtils::castShapeInfo(xShapeInfo, xShapeInfoCast);
                     const bool canCastY = nd4j::DataTypeUtils::castShapeInfo(yShapeInfo, yShapeInfoCast);
 
                     PRAGMA_OMP_PARALLEL_THREADS(info._numThreads)
-                    {                
+                    {
                         auto threadNum = omp_get_thread_num();
                         auto threadOffset = info.getThreadOffset(threadNum);
                         auto ulen = static_cast<unsigned int>(info.getItersPerThread(threadNum));
 
                         PRAGMA_OMP_SIMD
                         for (Nd4jLong i = 0; i < ulen; i++)  {
-                            auto xOffset = shape::indexOffset(i + threadOffset, xShapeInfo, xShapeInfoCast, n, canCastX);                            
-                            auto offset  = shape::indexOffset(i + threadOffset, yShapeInfo, yShapeInfoCast, n, canCastY);
+                            auto xOffset = shape::indexOffset(i + threadOffset, xShapeInfo, xShapeInfoCast, canCastX);
+                            auto offset  = shape::indexOffset(i + threadOffset, yShapeInfo, yShapeInfoCast, canCastY);
                             z[offset] = OpType::op(x[xOffset], y[offset], extraParams);
                         }
                     }
                 }
                 else {
-                    
+
                     uint xShapeInfoCast[MAX_RANK];
                     uint yShapeInfoCast[MAX_RANK];
                     uint zShapeInfoCast[MAX_RANK];
@@ -287,16 +287,16 @@ namespace functions {
                     const bool canCastZ = nd4j::DataTypeUtils::castShapeInfo(zShapeInfo, zShapeInfoCast);
 
                     PRAGMA_OMP_PARALLEL_THREADS(info._numThreads)
-                    {                
+                    {
                         auto threadNum = omp_get_thread_num();
                         auto threadOffset = info.getThreadOffset(threadNum);
                         auto ulen = static_cast<unsigned int>(info.getItersPerThread(threadNum));
 
                         PRAGMA_OMP_SIMD
                         for (Nd4jLong i = 0; i < ulen; i++)  {
-                            auto xOffset = shape::indexOffset(i + threadOffset, xShapeInfo, xShapeInfoCast, n, canCastX);                            
-                            auto yOffset = shape::indexOffset(i + threadOffset, yShapeInfo, yShapeInfoCast, n, canCastY);
-                            auto zOffset = shape::indexOffset(i + threadOffset, zShapeInfo, zShapeInfoCast, n, canCastZ);
+                            auto xOffset = shape::indexOffset(i + threadOffset, xShapeInfo, xShapeInfoCast, canCastX);
+                            auto yOffset = shape::indexOffset(i + threadOffset, yShapeInfo, yShapeInfoCast, canCastY);
+                            auto zOffset = shape::indexOffset(i + threadOffset, zShapeInfo, zShapeInfoCast, canCastZ);
                             z[zOffset] = OpType::op(x[xOffset], y[yOffset], extraParams);
                         }
                     }
diff --git a/libnd4j/include/loops/cpu/pairwise_int.cpp b/libnd4j/include/loops/cpu/pairwise_int.cpp
index b356adcc2..63b9dc8c8 100644
--- a/libnd4j/include/loops/cpu/pairwise_int.cpp
+++ b/libnd4j/include/loops/cpu/pairwise_int.cpp
@@ -61,7 +61,7 @@ namespace functions {
                                               Nd4jLong zEws,
                                               void *vextraParams,
                                               const Nd4jLong n) {
-            
+
             auto x = reinterpret_cast<X *>(vx);
             auto y = reinterpret_cast<X *>(vy);
             auto z = reinterpret_cast<X *>(vz);
@@ -72,9 +72,9 @@ namespace functions {
             if (xEws == 1 && yEws == 1 && zEws == 1) {
 
                 PRAGMA_OMP_PARALLEL_THREADS(info._numThreads)
-                {                
+                {
                     auto threadNum = omp_get_thread_num();
-                    Nd4jLong threadOffset = info.getThreadOffset(threadNum);        
+                    Nd4jLong threadOffset = info.getThreadOffset(threadNum);
                     auto xi = x + threadOffset;
                     auto yi = y + threadOffset;
                     auto zi = z + threadOffset;
@@ -88,9 +88,9 @@ namespace functions {
             else {
 
                 PRAGMA_OMP_PARALLEL_THREADS(info._numThreads)
-                {                
+                {
                     auto threadNum = omp_get_thread_num();
-                    Nd4jLong threadOffset = info.getThreadOffset(threadNum);        
+                    Nd4jLong threadOffset = info.getThreadOffset(threadNum);
                     auto xi = x + xEws*threadOffset;
                     auto yi = y + yEws*threadOffset;
                     auto zi = z + zEws*threadOffset;
@@ -151,33 +151,33 @@ namespace functions {
                 if(shape::haveSameShapeAndStrides(xShapeInfo, zShapeInfo)) {
 
                     PRAGMA_OMP_PARALLEL_THREADS(info._numThreads)
-                    {                
+                    {
                         auto threadNum = omp_get_thread_num();
                         auto threadOffset = info.getThreadOffset(threadNum);
                         auto ulen = static_cast<unsigned int>(info.getItersPerThread(threadNum));
 
                         PRAGMA_OMP_SIMD
                         for(Nd4jLong i = 0; i < ulen; i++)  {
-                            auto offset = shape::indexOffset(i + threadOffset, xShapeInfo, xShapeInfoCast, n, canCastX);
+                            auto offset = shape::indexOffset(i + threadOffset, xShapeInfo, xShapeInfoCast, canCastX);
                             z[offset] = OpType::op(x[offset], y[0], extraParams);
                         }
                     }
                 }
                 else {
-                    
+
                     uint zShapeInfoCast[MAX_RANK];
                     const bool canCastZ = nd4j::DataTypeUtils::castShapeInfo(zShapeInfo, zShapeInfoCast);
 
                     PRAGMA_OMP_PARALLEL_THREADS(info._numThreads)
-                    {                
+                    {
                         auto threadNum = omp_get_thread_num();
                         auto threadOffset = info.getThreadOffset(threadNum);
                         auto ulen = static_cast<unsigned int>(info.getItersPerThread(threadNum));
 
                         PRAGMA_OMP_SIMD
                         for(Nd4jLong i = 0; i < ulen; i++)  {
-                            auto xOffset = shape::indexOffset(i + threadOffset, xShapeInfo, xShapeInfoCast, n, canCastX);
-                            auto zOffset = shape::indexOffset(i + threadOffset, zShapeInfo, zShapeInfoCast, n, canCastZ);
+                            auto xOffset = shape::indexOffset(i + threadOffset, xShapeInfo, xShapeInfoCast, canCastX);
+                            auto zOffset = shape::indexOffset(i + threadOffset, zShapeInfo, zShapeInfoCast, canCastZ);
                             z[zOffset] = OpType::op(x[xOffset], y[0], extraParams);
                         }
                     }
@@ -190,11 +190,11 @@ namespace functions {
 
             if ((kindOfLoop == nd4j::LoopKind::EWS1 || kindOfLoop == nd4j::LoopKind::EWSNONZERO) && sameShapesXY) {
                 exec<OpType>(x, xEws, y, yEws, z, zEws, extraParams, n);
-            }            
+            }
             else if ((kindOfLoop == nd4j::LoopKind::EWS1 || kindOfLoop == nd4j::LoopKind::EWSNONZERO) && !sameShapesXY) { //not same shape
                 exec<OpType>(x, xEws, y, yEws, z, zEws, extraParams, shape::length(yShapeInfo));
             }
-            else {                
+            else {
 
                 if(shape::haveSameShapeAndStrides(xShapeInfo, yShapeInfo) && shape::haveSameShapeAndStrides(xShapeInfo, zShapeInfo)) {
 
@@ -202,83 +202,83 @@ namespace functions {
                     const bool canCastX = nd4j::DataTypeUtils::castShapeInfo(xShapeInfo, xShapeInfoCast);
 
                     PRAGMA_OMP_PARALLEL_THREADS(info._numThreads)
-                    {                
+                    {
                         auto threadNum = omp_get_thread_num();
                         auto threadOffset = info.getThreadOffset(threadNum);
                         auto ulen = static_cast<unsigned int>(info.getItersPerThread(threadNum));
 
                         PRAGMA_OMP_SIMD
                         for (Nd4jLong i = 0; i < ulen; i++)  {
-                            auto offset = shape::indexOffset(i + threadOffset, xShapeInfo, xShapeInfoCast, n, canCastX);
+                            auto offset = shape::indexOffset(i + threadOffset, xShapeInfo, xShapeInfoCast, canCastX);
                             z[offset] = OpType::op(x[offset], y[offset], extraParams);
                         }
                     }
                 }
                 else if(shape::haveSameShapeAndStrides(xShapeInfo, yShapeInfo)) {
-                    
+
                     uint xShapeInfoCast[MAX_RANK];
                     uint zShapeInfoCast[MAX_RANK];
                     const bool canCastX = nd4j::DataTypeUtils::castShapeInfo(xShapeInfo, xShapeInfoCast);
                     const bool canCastZ = nd4j::DataTypeUtils::castShapeInfo(zShapeInfo, zShapeInfoCast);
 
                     PRAGMA_OMP_PARALLEL_THREADS(info._numThreads)
-                    {                
+                    {
                         auto threadNum = omp_get_thread_num();
                         auto threadOffset = info.getThreadOffset(threadNum);
                         auto ulen = static_cast<unsigned int>(info.getItersPerThread(threadNum));
 
                         PRAGMA_OMP_SIMD
                         for (Nd4jLong i = 0; i < ulen; i++)  {
-                            auto offset  = shape::indexOffset(i + threadOffset, xShapeInfo, xShapeInfoCast, n, canCastX);                            
-                            auto zOffset = shape::indexOffset(i + threadOffset, zShapeInfo, zShapeInfoCast, n, canCastZ);
+                            auto offset  = shape::indexOffset(i + threadOffset, xShapeInfo, xShapeInfoCast, canCastX);
+                            auto zOffset = shape::indexOffset(i + threadOffset, zShapeInfo, zShapeInfoCast, canCastZ);
                             z[zOffset] = OpType::op(x[offset], y[offset], extraParams);
                         }
                     }
                 }
                 else if(shape::haveSameShapeAndStrides(xShapeInfo, zShapeInfo)) {
-                    
+
                     uint xShapeInfoCast[MAX_RANK];
                     uint yShapeInfoCast[MAX_RANK];
                     const bool canCastX = nd4j::DataTypeUtils::castShapeInfo(xShapeInfo, xShapeInfoCast);
                     const bool canCastY = nd4j::DataTypeUtils::castShapeInfo(yShapeInfo, yShapeInfoCast);
 
                     PRAGMA_OMP_PARALLEL_THREADS(info._numThreads)
-                    {                
+                    {
                         auto threadNum = omp_get_thread_num();
                         auto threadOffset = info.getThreadOffset(threadNum);
                         auto ulen = static_cast<unsigned int>(info.getItersPerThread(threadNum));
 
                         PRAGMA_OMP_SIMD
                         for (Nd4jLong i = 0; i < ulen; i++)  {
-                            auto offset  = shape::indexOffset(i + threadOffset, xShapeInfo, xShapeInfoCast, n, canCastX);                            
-                            auto yOffset = shape::indexOffset(i + threadOffset, yShapeInfo, yShapeInfoCast, n, canCastY);
+                            auto offset  = shape::indexOffset(i + threadOffset, xShapeInfo, xShapeInfoCast, canCastX);
+                            auto yOffset = shape::indexOffset(i + threadOffset, yShapeInfo, yShapeInfoCast, canCastY);
                             z[offset] = OpType::op(x[offset], y[yOffset], extraParams);
                         }
                     }
                 }
                 else if(shape::haveSameShapeAndStrides(yShapeInfo, zShapeInfo)) {
-                    
+
                     uint xShapeInfoCast[MAX_RANK];
                     uint yShapeInfoCast[MAX_RANK];
                     const bool canCastX = nd4j::DataTypeUtils::castShapeInfo(xShapeInfo, xShapeInfoCast);
                     const bool canCastY = nd4j::DataTypeUtils::castShapeInfo(yShapeInfo, yShapeInfoCast);
 
                     PRAGMA_OMP_PARALLEL_THREADS(info._numThreads)
-                    {                
+                    {
                         auto threadNum = omp_get_thread_num();
                         auto threadOffset = info.getThreadOffset(threadNum);
                         auto ulen = static_cast<unsigned int>(info.getItersPerThread(threadNum));
 
                         PRAGMA_OMP_SIMD
                         for (Nd4jLong i = 0; i < ulen; i++)  {
-                            auto xOffset = shape::indexOffset(i + threadOffset, xShapeInfo, xShapeInfoCast, n, canCastX);                            
-                            auto offset  = shape::indexOffset(i + threadOffset, yShapeInfo, yShapeInfoCast, n, canCastY);
+                            auto xOffset = shape::indexOffset(i + threadOffset, xShapeInfo, xShapeInfoCast, canCastX);
+                            auto offset  = shape::indexOffset(i + threadOffset, yShapeInfo, yShapeInfoCast, canCastY);
                             z[offset] = OpType::op(x[xOffset], y[offset], extraParams);
                         }
                     }
                 }
                 else {
-                    
+
                     uint xShapeInfoCast[MAX_RANK];
                     uint yShapeInfoCast[MAX_RANK];
                     uint zShapeInfoCast[MAX_RANK];
@@ -287,16 +287,16 @@ namespace functions {
                     const bool canCastZ = nd4j::DataTypeUtils::castShapeInfo(zShapeInfo, zShapeInfoCast);
 
                     PRAGMA_OMP_PARALLEL_THREADS(info._numThreads)
-                    {                
+                    {
                         auto threadNum = omp_get_thread_num();
                         auto threadOffset = info.getThreadOffset(threadNum);
                         auto ulen = static_cast<unsigned int>(info.getItersPerThread(threadNum));
 
                         PRAGMA_OMP_SIMD
                         for (Nd4jLong i = 0; i < ulen; i++)  {
-                            auto xOffset = shape::indexOffset(i + threadOffset, xShapeInfo, xShapeInfoCast, n, canCastX);                            
-                            auto yOffset = shape::indexOffset(i + threadOffset, yShapeInfo, yShapeInfoCast, n, canCastY);
-                            auto zOffset = shape::indexOffset(i + threadOffset, zShapeInfo, zShapeInfoCast, n, canCastZ);
+                            auto xOffset = shape::indexOffset(i + threadOffset, xShapeInfo, xShapeInfoCast, canCastX);
+                            auto yOffset = shape::indexOffset(i + threadOffset, yShapeInfo, yShapeInfoCast, canCastY);
+                            auto zOffset = shape::indexOffset(i + threadOffset, zShapeInfo, zShapeInfoCast, canCastZ);
                             z[zOffset] = OpType::op(x[xOffset], y[yOffset], extraParams);
                         }
                     }
diff --git a/libnd4j/include/loops/cpu/random.cpp b/libnd4j/include/loops/cpu/random.cpp
index 30bab1327..5abc1447a 100644
--- a/libnd4j/include/loops/cpu/random.cpp
+++ b/libnd4j/include/loops/cpu/random.cpp
@@ -50,27 +50,27 @@ namespace functions {
                 return;
             }
 
-            auto length = shape::length(zShapeInfo);            
+            auto length = shape::length(zShapeInfo);
 
 //            nd4j::random::RandomBuffer *buffer = reinterpret_cast<nd4j::random::RandomBuffer *> (state);
             nd4j::graph::RandomGenerator* rng = reinterpret_cast<nd4j::graph::RandomGenerator*>(state);
             nd4j::OmpLaunchHelper info(length);
 
-           
+
             if(shape::haveSameShapeAndStrides(xShapeInfo, yShapeInfo) && shape::haveSameShapeAndStrides(xShapeInfo, zShapeInfo)) {
 
                 uint xShapeInfoCast[MAX_RANK];
                 const bool canCastX = nd4j::DataTypeUtils::castShapeInfo(xShapeInfo, xShapeInfoCast);
 
                 PRAGMA_OMP_PARALLEL_THREADS(info._numThreads)
-                {                
+                {
                     auto threadNum = omp_get_thread_num();
                     auto threadOffset = info.getThreadOffset(threadNum);
                     auto ulen = static_cast<unsigned int>(info.getItersPerThread(threadNum));
 
                     PRAGMA_OMP_SIMD
                     for (Nd4jLong i = 0; i < ulen; i++)  {
-                        auto offset = shape::indexOffset(i + threadOffset, xShapeInfo, xShapeInfoCast, length, canCastX);
+                        auto offset = shape::indexOffset(i + threadOffset, xShapeInfo, xShapeInfoCast, canCastX);
                         z[offset] = OpClass::op(x[offset], y[offset], i, length, rng, extraArguments);
                     }
                 }
@@ -79,19 +79,19 @@ namespace functions {
 
                 uint xShapeInfoCast[MAX_RANK];
                 uint zShapeInfoCast[MAX_RANK];
-                const bool canCastX = nd4j::DataTypeUtils::castShapeInfo(xShapeInfo, xShapeInfoCast);                
+                const bool canCastX = nd4j::DataTypeUtils::castShapeInfo(xShapeInfo, xShapeInfoCast);
                 const bool canCastZ = nd4j::DataTypeUtils::castShapeInfo(zShapeInfo, zShapeInfoCast);
 
                 PRAGMA_OMP_PARALLEL_THREADS(info._numThreads)
-                {                
+                {
                     auto threadNum = omp_get_thread_num();
                     auto threadOffset = info.getThreadOffset(threadNum);
                     auto ulen = static_cast<unsigned int>(info.getItersPerThread(threadNum));
 
                     PRAGMA_OMP_SIMD
                     for (Nd4jLong i = 0; i < ulen; i++)  {
-                        auto offset  = shape::indexOffset(i + threadOffset, xShapeInfo, xShapeInfoCast, length, canCastX);
-                        auto zOffset = shape::indexOffset(i + threadOffset, zShapeInfo, zShapeInfoCast, length, canCastZ);
+                        auto offset  = shape::indexOffset(i + threadOffset, xShapeInfo, xShapeInfoCast, canCastX);
+                        auto zOffset = shape::indexOffset(i + threadOffset, zShapeInfo, zShapeInfoCast, canCastZ);
                         z[zOffset] = OpClass::op(x[offset], y[offset], i, length, rng, extraArguments);
                     }
                 }
@@ -100,19 +100,19 @@ namespace functions {
 
                 uint xShapeInfoCast[MAX_RANK];
                 uint yShapeInfoCast[MAX_RANK];
-                const bool canCastX = nd4j::DataTypeUtils::castShapeInfo(xShapeInfo, xShapeInfoCast);                
+                const bool canCastX = nd4j::DataTypeUtils::castShapeInfo(xShapeInfo, xShapeInfoCast);
                 const bool canCastY = nd4j::DataTypeUtils::castShapeInfo(yShapeInfo, yShapeInfoCast);
 
                 PRAGMA_OMP_PARALLEL_THREADS(info._numThreads)
-                {                
+                {
                     auto threadNum = omp_get_thread_num();
                     auto threadOffset = info.getThreadOffset(threadNum);
                     auto ulen = static_cast<unsigned int>(info.getItersPerThread(threadNum));
 
                     PRAGMA_OMP_SIMD
                     for (Nd4jLong i = 0; i < ulen; i++)  {
-                        auto offset  = shape::indexOffset(i + threadOffset, xShapeInfo, xShapeInfoCast, length, canCastX);
-                        auto yOffset = shape::indexOffset(i + threadOffset, yShapeInfo, yShapeInfoCast, length, canCastY);
+                        auto offset  = shape::indexOffset(i + threadOffset, xShapeInfo, xShapeInfoCast, canCastX);
+                        auto yOffset = shape::indexOffset(i + threadOffset, yShapeInfo, yShapeInfoCast, canCastY);
                         z[offset] = OpClass::op(x[offset], y[yOffset], i, length, rng, extraArguments);
                     }
                 }
@@ -121,19 +121,19 @@ namespace functions {
 
                 uint xShapeInfoCast[MAX_RANK];
                 uint yShapeInfoCast[MAX_RANK];
-                const bool canCastX = nd4j::DataTypeUtils::castShapeInfo(xShapeInfo, xShapeInfoCast);                
+                const bool canCastX = nd4j::DataTypeUtils::castShapeInfo(xShapeInfo, xShapeInfoCast);
                 const bool canCastY = nd4j::DataTypeUtils::castShapeInfo(yShapeInfo, yShapeInfoCast);
 
                 PRAGMA_OMP_PARALLEL_THREADS(info._numThreads)
-                {                
+                {
                     auto threadNum = omp_get_thread_num();
                     auto threadOffset = info.getThreadOffset(threadNum);
                     auto ulen = static_cast<unsigned int>(info.getItersPerThread(threadNum));
 
                     PRAGMA_OMP_SIMD
-                    for (Nd4jLong i = 0; i < info.getItersPerThread(threadNum); i++)  {                        
-                        auto xOffset = shape::indexOffset(i + threadOffset, xShapeInfo, xShapeInfoCast, length, canCastX);
-                        auto offset  = shape::indexOffset(i + threadOffset, yShapeInfo, yShapeInfoCast, length, canCastY);
+                    for (Nd4jLong i = 0; i < info.getItersPerThread(threadNum); i++)  {
+                        auto xOffset = shape::indexOffset(i + threadOffset, xShapeInfo, xShapeInfoCast, canCastX);
+                        auto offset  = shape::indexOffset(i + threadOffset, yShapeInfo, yShapeInfoCast, canCastY);
                         z[offset] = OpClass::op(x[xOffset], y[offset], i, length, rng, extraArguments);
                     }
                 }
@@ -143,21 +143,21 @@ namespace functions {
                 uint xShapeInfoCast[MAX_RANK];
                 uint yShapeInfoCast[MAX_RANK];
                 uint zShapeInfoCast[MAX_RANK];
-                const bool canCastX = nd4j::DataTypeUtils::castShapeInfo(xShapeInfo, xShapeInfoCast);                
+                const bool canCastX = nd4j::DataTypeUtils::castShapeInfo(xShapeInfo, xShapeInfoCast);
                 const bool canCastY = nd4j::DataTypeUtils::castShapeInfo(yShapeInfo, yShapeInfoCast);
                 const bool canCastZ = nd4j::DataTypeUtils::castShapeInfo(zShapeInfo, zShapeInfoCast);
 
                 PRAGMA_OMP_PARALLEL_THREADS(info._numThreads)
-                {                
+                {
                     auto threadNum = omp_get_thread_num();
                     auto threadOffset = info.getThreadOffset(threadNum);
                     auto ulen = static_cast<unsigned int>(info.getItersPerThread(threadNum));
 
                     PRAGMA_OMP_SIMD
                     for (Nd4jLong i = 0; i < ulen; i++)  {
-                        auto xOffset = shape::indexOffset(i + threadOffset, xShapeInfo, xShapeInfoCast, length, canCastX);
-                        auto yOffset = shape::indexOffset(i + threadOffset, yShapeInfo, yShapeInfoCast, length, canCastY);
-                        auto zOffset = shape::indexOffset(i + threadOffset, zShapeInfo, zShapeInfoCast, length, canCastZ);
+                        auto xOffset = shape::indexOffset(i + threadOffset, xShapeInfo, xShapeInfoCast, canCastX);
+                        auto yOffset = shape::indexOffset(i + threadOffset, yShapeInfo, yShapeInfoCast, canCastY);
+                        auto zOffset = shape::indexOffset(i + threadOffset, zShapeInfo, zShapeInfoCast, canCastZ);
                         z[zOffset] = OpClass::op(x[xOffset], y[yOffset], i, length, rng, extraArguments);
                     }
                 }
@@ -185,18 +185,18 @@ namespace functions {
 
             nd4j::graph::RandomGenerator* rng = reinterpret_cast<nd4j::graph::RandomGenerator*>(state);
             nd4j::OmpLaunchHelper info(length);
-            
+
             if(shape::haveSameShapeAndStrides(xShapeInfo, zShapeInfo)) {
 
                 PRAGMA_OMP_PARALLEL_THREADS(info._numThreads)
-                {                
+                {
                     auto threadNum = omp_get_thread_num();
                     auto threadOffset = info.getThreadOffset(threadNum);
                     auto ulen = static_cast<unsigned int>(info.getItersPerThread(threadNum));
 
                     PRAGMA_OMP_SIMD
                     for (Nd4jLong i = 0; i < ulen; i++)  {
-                        auto offset = shape::indexOffset(i + threadOffset, xShapeInfo, xShapeInfoCast, length, canCastX);                        
+                        auto offset = shape::indexOffset(i + threadOffset, xShapeInfo, xShapeInfoCast, canCastX);
                         z[offset] = OpClass::op(x[offset], i, length, rng, extraArguments);
                     }
                 }
@@ -207,15 +207,15 @@ namespace functions {
                 const bool canCastZ = nd4j::DataTypeUtils::castShapeInfo(zShapeInfo, zShapeInfoCast);
 
                 PRAGMA_OMP_PARALLEL_THREADS(info._numThreads)
-                {                
+                {
                     auto threadNum = omp_get_thread_num();
                     auto threadOffset = info.getThreadOffset(threadNum);
                     auto ulen = static_cast<unsigned int>(info.getItersPerThread(threadNum));
 
                     PRAGMA_OMP_SIMD
                     for (Nd4jLong i = 0; i < ulen; i++)  {
-                        auto xOffset = shape::indexOffset(i + threadOffset, xShapeInfo, xShapeInfoCast, length, canCastX);
-                        auto zOffset = shape::indexOffset(i + threadOffset, zShapeInfo, zShapeInfoCast, length, canCastZ);
+                        auto xOffset = shape::indexOffset(i + threadOffset, xShapeInfo, xShapeInfoCast, canCastX);
+                        auto zOffset = shape::indexOffset(i + threadOffset, zShapeInfo, zShapeInfoCast, canCastZ);
                         z[zOffset] = OpClass::op(x[xOffset], i, length, rng, extraArguments);
                     }
                 }
@@ -231,7 +231,7 @@ namespace functions {
             auto extraArguments = reinterpret_cast<X *>(vextraArguments);
 
             auto length = shape::length(zShapeInfo);
-          
+
             //nd4j::random::RandomBuffer *buffer = reinterpret_cast<nd4j::random::RandomBuffer *> (state);
             nd4j::graph::RandomGenerator* rng = reinterpret_cast<nd4j::graph::RandomGenerator*>(state);
             nd4j::OmpLaunchHelper info(length);
@@ -240,14 +240,14 @@ namespace functions {
             const bool canCastZ = nd4j::DataTypeUtils::castShapeInfo(zShapeInfo, zShapeInfoCast);
 
             PRAGMA_OMP_PARALLEL_THREADS(info._numThreads)
-            {                
+            {
                 auto threadNum = omp_get_thread_num();
                 auto threadOffset = info.getThreadOffset(threadNum);
                 auto ulen = static_cast<unsigned int>(info.getItersPerThread(threadNum));
 
                 PRAGMA_OMP_SIMD
                 for (Nd4jLong i = 0; i < ulen; i++)  {
-                    auto offset = shape::indexOffset(i + threadOffset, zShapeInfo, zShapeInfoCast, length, canCastZ);
+                    auto offset = shape::indexOffset(i + threadOffset, zShapeInfo, zShapeInfoCast, canCastZ);
                     z[offset] = OpClass::op(i+threadOffset, length, rng, extraArguments);
                 }
             }
diff --git a/libnd4j/include/loops/cpu/reduce/reduce_bool.cpp b/libnd4j/include/loops/cpu/reduce/reduce_bool.cpp
index a7145846e..246d18ac4 100644
--- a/libnd4j/include/loops/cpu/reduce/reduce_bool.cpp
+++ b/libnd4j/include/loops/cpu/reduce/reduce_bool.cpp
@@ -77,7 +77,7 @@ namespace functions {
 
                 PRAGMA_OMP_PARALLEL_FOR_SIMD_THREADS(maxThreads)
                 for(Nd4jLong i = 0; i < length; ++i)
-                    intermediate[omp_get_thread_num()] = OpType::update(intermediate[omp_get_thread_num()], OpType::op(x[shape::indexOffset(i, xShapeInfo, xShapeInfoCast, length, canCastX)], extraParams), extraParams);
+                    intermediate[omp_get_thread_num()] = OpType::update(intermediate[omp_get_thread_num()], OpType::op(x[shape::indexOffset(i, xShapeInfo, xShapeInfoCast, canCastX)], extraParams), extraParams);
 
 
                 for (int e = 0; e < maxThreads; e++)
@@ -112,7 +112,7 @@ namespace functions {
 
                     PRAGMA_OMP_PARALLEL_FOR_SIMD
                     for(Nd4jLong i = 0; i < length; ++i)
-                        intermediate[omp_get_thread_num()] = OpType::update(intermediate[omp_get_thread_num()], OpType::op(x[shape::indexOffset(i, xShapeInfo, xShapeInfoCast, length, canCastX)], extraParams), extraParams);
+                        intermediate[omp_get_thread_num()] = OpType::update(intermediate[omp_get_thread_num()], OpType::op(x[shape::indexOffset(i, xShapeInfo, xShapeInfoCast, canCastX)], extraParams), extraParams);
 
                     for (int e = 0; e < omp_get_max_threads(); e++)
                         start = OpType::update(start, intermediate[e], extraParams);
diff --git a/libnd4j/include/loops/cpu/reduce/reduce_float.cpp b/libnd4j/include/loops/cpu/reduce/reduce_float.cpp
index 8d04b7cdb..a94a19b25 100644
--- a/libnd4j/include/loops/cpu/reduce/reduce_float.cpp
+++ b/libnd4j/include/loops/cpu/reduce/reduce_float.cpp
@@ -81,7 +81,7 @@ namespace functions {
 
                 PRAGMA_OMP_PARALLEL_FOR_SIMD_THREADS(maxThreads)
                 for(Nd4jLong i = 0; i < length; ++i)
-                    intermediate[omp_get_thread_num()] = OpType::update(intermediate[omp_get_thread_num()], OpType::op(x[shape::indexOffset(i, xShapeInfo, xShapeInfoCast, length, canCastX)], extraParams), extraParams);
+                    intermediate[omp_get_thread_num()] = OpType::update(intermediate[omp_get_thread_num()], OpType::op(x[shape::indexOffset(i, xShapeInfo, xShapeInfoCast, canCastX)], extraParams), extraParams);
 
 
                 for (int e = 0; e < maxThreads; e++)
@@ -115,7 +115,7 @@ namespace functions {
 
                     PRAGMA_OMP_PARALLEL_FOR_SIMD
                     for(Nd4jLong i = 0; i < length; ++i)
-                        intermediate[omp_get_thread_num()] = OpType::update(intermediate[omp_get_thread_num()], OpType::op(x[shape::indexOffset(i, xShapeInfo, xShapeInfoCast, length, canCastX)], extraParams), extraParams);
+                        intermediate[omp_get_thread_num()] = OpType::update(intermediate[omp_get_thread_num()], OpType::op(x[shape::indexOffset(i, xShapeInfo, xShapeInfoCast, canCastX)], extraParams), extraParams);
 
                     for (int e = 0; e < omp_get_max_threads(); e++)
                         start = OpType::update(start, intermediate[e], extraParams);
diff --git a/libnd4j/include/loops/cpu/reduce/reduce_long.cpp b/libnd4j/include/loops/cpu/reduce/reduce_long.cpp
index 9069f4198..1a148805e 100644
--- a/libnd4j/include/loops/cpu/reduce/reduce_long.cpp
+++ b/libnd4j/include/loops/cpu/reduce/reduce_long.cpp
@@ -77,7 +77,7 @@ namespace functions {
 
                 PRAGMA_OMP_PARALLEL_FOR_SIMD_THREADS(maxThreads)
                 for(Nd4jLong i = 0; i < length; ++i)
-                    intermediate[omp_get_thread_num()] = OpType::update(intermediate[omp_get_thread_num()], OpType::op(x[shape::indexOffset(i, xShapeInfo, xShapeInfoCast, length, canCastX)], extraParams), extraParams);
+                    intermediate[omp_get_thread_num()] = OpType::update(intermediate[omp_get_thread_num()], OpType::op(x[shape::indexOffset(i, xShapeInfo, xShapeInfoCast, canCastX)], extraParams), extraParams);
 
 
                 for (int e = 0; e < maxThreads; e++)
@@ -113,7 +113,7 @@ namespace functions {
 
                     PRAGMA_OMP_PARALLEL_FOR_SIMD
                     for(Nd4jLong i = 0; i < length; ++i)
-                        intermediate[omp_get_thread_num()] = OpType::update(intermediate[omp_get_thread_num()], OpType::op(x[shape::indexOffset(i, xShapeInfo, xShapeInfoCast, length, canCastX)], extraParams), extraParams);
+                        intermediate[omp_get_thread_num()] = OpType::update(intermediate[omp_get_thread_num()], OpType::op(x[shape::indexOffset(i, xShapeInfo, xShapeInfoCast, canCastX)], extraParams), extraParams);
 
                     for (int e = 0; e < omp_get_max_threads(); e++)
                         start = OpType::update(start, intermediate[e], extraParams);
diff --git a/libnd4j/include/loops/cpu/reduce/reduce_same.cpp b/libnd4j/include/loops/cpu/reduce/reduce_same.cpp
index 676348017..0dfff5e73 100644
--- a/libnd4j/include/loops/cpu/reduce/reduce_same.cpp
+++ b/libnd4j/include/loops/cpu/reduce/reduce_same.cpp
@@ -79,7 +79,7 @@ namespace functions {
 
                 PRAGMA_OMP_PARALLEL_FOR_SIMD_THREADS(maxThreads)
                 for(Nd4jLong i = 0; i < length; ++i)
-                    intermediate[omp_get_thread_num()] = OpType::update(intermediate[omp_get_thread_num()], OpType::op(x[shape::indexOffset(i, xShapeInfo, xShapeInfoCast, length, canCastX)], extraParams), extraParams);
+                    intermediate[omp_get_thread_num()] = OpType::update(intermediate[omp_get_thread_num()], OpType::op(x[shape::indexOffset(i, xShapeInfo, xShapeInfoCast, canCastX)], extraParams), extraParams);
 
 
                 for (int e = 0; e < maxThreads; e++)
@@ -117,7 +117,7 @@ namespace functions {
 
                     PRAGMA_OMP_PARALLEL_FOR_SIMD_THREADS(maxThreads)
                     for(Nd4jLong i = 0; i < length; ++i)
-                        intermediate[omp_get_thread_num()] = OpType::update(intermediate[omp_get_thread_num()], OpType::op(x[shape::indexOffset(i, xShapeInfo, xShapeInfoCast, length, canCastX)], extraParams), extraParams);
+                        intermediate[omp_get_thread_num()] = OpType::update(intermediate[omp_get_thread_num()], OpType::op(x[shape::indexOffset(i, xShapeInfo, xShapeInfoCast, canCastX)], extraParams), extraParams);
 
                     for (int e = 0; e < maxThreads; e++)
                         start = OpType::update(start, intermediate[e], extraParams);
diff --git a/libnd4j/include/loops/cpu/reduce3.cpp b/libnd4j/include/loops/cpu/reduce3.cpp
index eeea227c8..fd09dc0e1 100644
--- a/libnd4j/include/loops/cpu/reduce3.cpp
+++ b/libnd4j/include/loops/cpu/reduce3.cpp
@@ -95,7 +95,7 @@ void Reduce3<X,Z>::execScalar(void *vx, Nd4jLong *xShapeInfo,
         PRAGMA_OMP_PARALLEL_FOR_SIMD_THREADS(t._numThreads)
         for(unsigned int i = 0; i < length; i++) {
             const auto threadNum = omp_get_thread_num();
-            auto offset  = shape::indexOffset(i, xShapeInfo, xShapeInfoCast, length, canCastX);
+            auto offset  = shape::indexOffset(i, xShapeInfo, xShapeInfoCast, canCastX);
             intermediate[threadNum] = OpType::update(intermediate[threadNum], OpType::op(x[offset], y[offset], extraParamsLocal + 3 * threadNum), extraParamsLocal + 3 * threadNum);
         }
     } else {
@@ -105,8 +105,8 @@ void Reduce3<X,Z>::execScalar(void *vx, Nd4jLong *xShapeInfo,
         PRAGMA_OMP_PARALLEL_FOR_SIMD_THREADS(t._numThreads)
         for(unsigned int i = 0; i < length; i++) {
             const auto threadNum = omp_get_thread_num();
-            auto xOffset  = shape::indexOffset(i, xShapeInfo, xShapeInfoCast, length, canCastX);
-            auto yOffset  = shape::indexOffset(i, yShapeInfo, yShapeInfoCast, length, canCastY);
+            auto xOffset  = shape::indexOffset(i, xShapeInfo, xShapeInfoCast, canCastX);
+            auto yOffset  = shape::indexOffset(i, yShapeInfo, yShapeInfoCast, canCastY);
             intermediate[threadNum] = OpType::update(intermediate[threadNum], OpType::op(x[xOffset], y[yOffset], extraParamsLocal + 3 * threadNum), extraParamsLocal + 3 * threadNum);
         }
     }
diff --git a/libnd4j/include/loops/cpu/scalar.hpp b/libnd4j/include/loops/cpu/scalar.hpp
index 8f9fd0990..79e53e4a2 100644
--- a/libnd4j/include/loops/cpu/scalar.hpp
+++ b/libnd4j/include/loops/cpu/scalar.hpp
@@ -33,14 +33,14 @@ namespace scalar    {
 ////////////////////////////////////////////////////////////////////////
 template<typename X, typename Y, typename Z>
 template<typename OpType>
-void ScalarTransform<X, Y, Z>::transform(void *vx, Nd4jLong *xShapeInfo, 
-                                                void *vextraParams, 
-                                                void *vz, Nd4jLong *zShapeInfo, 
-                                                void *vscalars, 
-                                                int *dimension, int dimensionLength, 
+void ScalarTransform<X, Y, Z>::transform(void *vx, Nd4jLong *xShapeInfo,
+                                                void *vextraParams,
+                                                void *vz, Nd4jLong *zShapeInfo,
+                                                void *vscalars,
+                                                int *dimension, int dimensionLength,
                                                 Nd4jLong *xTadShapeInfo, Nd4jLong *xTadOffsets,
                                                 Nd4jLong *zTadShapeInfo, Nd4jLong *zTadOffsets) {
-        
+
     auto x = reinterpret_cast<X *>(vx);
     auto z = reinterpret_cast<Z *>(vz);
     auto scalars = reinterpret_cast<Y *>(vscalars);
@@ -159,37 +159,37 @@ void ScalarTransform<X, Y, Z>::transform(void *vx, Nd4jLong *xShapeInfo,
 
             PRAGMA_OMP_PARALLEL_THREADS_IF(info._numThreads, allowParallelism)
             {
-                auto threadNum = omp_get_thread_num();                    
+                auto threadNum = omp_get_thread_num();
                 auto threadOffset = info.getThreadOffset(threadNum);
                 auto ulen = static_cast<unsigned int>(info.getItersPerThread(threadNum));
 
                 PRAGMA_OMP_SIMD
                 for (unsigned int i = 0; i < ulen; i++) {
-                    auto offset = shape::indexOffset(i + threadOffset, xShapeInfo, xShapeInfoCast, len, canCastX);                    
+                    auto offset = shape::indexOffset(i + threadOffset, xShapeInfo, xShapeInfoCast, canCastX);
                     z[offset] = OpType::op(x[offset], scalar, extraParams);
                 }
             }
         }
         else {
-            
+
             uint zShapeInfoCast[MAX_RANK];
             const bool canCastZ = nd4j::DataTypeUtils::castShapeInfo<uint>(zShapeInfo, zShapeInfoCast);
 
             PRAGMA_OMP_PARALLEL_THREADS_IF(info._numThreads, allowParallelism)
             {
-                auto threadNum = omp_get_thread_num();                    
+                auto threadNum = omp_get_thread_num();
                 auto threadOffset = info.getThreadOffset(threadNum);
                 auto ulen = static_cast<unsigned int>(info.getItersPerThread(threadNum));
 
                 PRAGMA_OMP_SIMD
                 for (unsigned int i = 0; i < ulen; i++) {
-                    auto xOffset = shape::indexOffset(i + threadOffset, xShapeInfo, xShapeInfoCast, len, canCastX);
-                    auto zOffset = shape::indexOffset(i + threadOffset, zShapeInfo, zShapeInfoCast, len, canCastZ);
+                    auto xOffset = shape::indexOffset(i + threadOffset, xShapeInfo, xShapeInfoCast, canCastX);
+                    auto zOffset = shape::indexOffset(i + threadOffset, zShapeInfo, zShapeInfoCast, canCastZ);
                     z[zOffset] = OpType::op(x[xOffset], scalar, extraParams);
                 }
             }
-        }  
-    }                        
+        }
+    }
 }
 
 ////////////////////////////////////////////////////////////////////////
@@ -200,7 +200,7 @@ void ScalarTransform<X, Y, Z>::transform(void *vx, Nd4jLong xEws,
                                         void *vscalar,
                                         void *vextraParams,
                                         const Nd4jLong len, bool allowParallelism) {
-               
+
     auto x = reinterpret_cast<X *>(vx);
     auto z = reinterpret_cast<Z *>(vz);
     auto scalar = reinterpret_cast<Y *>(vscalar)[0];
diff --git a/libnd4j/include/loops/cpu/scalar_bool.cpp b/libnd4j/include/loops/cpu/scalar_bool.cpp
index 1f400119b..b37bdd6ef 100644
--- a/libnd4j/include/loops/cpu/scalar_bool.cpp
+++ b/libnd4j/include/loops/cpu/scalar_bool.cpp
@@ -33,14 +33,14 @@ namespace functions {
 
         template<typename X, typename Z>
         template<typename OpType>
-        void ScalarBoolTransform<X, Z>::transform(void *vx, Nd4jLong *xShapeInfo, 
-                                                void *vextraParams, 
-                                                void *vz,  Nd4jLong *zShapeInfo, 
-                                                void *vscalars, 
-                                                int *dimension, int dimensionLength, 
+        void ScalarBoolTransform<X, Z>::transform(void *vx, Nd4jLong *xShapeInfo,
+                                                void *vextraParams,
+                                                void *vz,  Nd4jLong *zShapeInfo,
+                                                void *vscalars,
+                                                int *dimension, int dimensionLength,
                                                 Nd4jLong *xTadShapeInfo, Nd4jLong *xTadOffsets,
                                                 Nd4jLong *zTadShapeInfo, Nd4jLong *zTadOffsets) {
-            
+
             auto x = reinterpret_cast<X *>(vx);
             auto z = reinterpret_cast<Z *>(vz);
             auto scalars = reinterpret_cast<X *>(vscalars);
@@ -63,7 +63,7 @@ namespace functions {
                 printf("ScalarBoolTransform<X, Z>::transform: super-bad loop visited. Shouldn't ever happen\n");
                 return;
             }
-            
+
             int num_threads = nd4j::math::nd4j_min<int>(numTads, omp_get_max_threads());
 
             if (kindOfLoop == nd4j::LoopKind::EWS1) {
@@ -76,7 +76,7 @@ namespace functions {
                     for (unsigned int f = 0; f < tadLength; f++)
                         oZ[f] = OpType::op(oX[f], scalars[r], extraParams);
                 }
-            } 
+            }
             else { // kindOfLoop != nd4j::LoopKind::EWSNONZERO
                 PRAGMA_OMP_PARALLEL_FOR_THREADS(num_threads)
                 for (unsigned int r = 0; r < numTads; r++) {
@@ -87,7 +87,7 @@ namespace functions {
                     for (unsigned int f = 0; f < tadLength; f++)
                         oZ[f * zTadEws] = OpType::op(oX[f * xTadEws], scalars[r], extraParams);
                 }
-            }          
+            }
         }
 
         template<typename X, typename Y>
@@ -139,7 +139,7 @@ namespace functions {
                                Nd4jLong *zShapeInfo,
                                void *vscalar,
                                void *vextraParams) {
-            
+
             auto x = reinterpret_cast<X *>(vx);
             auto z = reinterpret_cast<Z *>(vz);
             auto scalar = reinterpret_cast<X *>(vscalar)[0];
@@ -162,41 +162,41 @@ namespace functions {
             const bool canCastX = nd4j::DataTypeUtils::castShapeInfo<uint>(xShapeInfo, xShapeInfoCast);
 
             nd4j::OmpLaunchHelper info(len);
-                               
+
             if(shape::haveSameShapeAndStrides(xShapeInfo, zShapeInfo)) {
 
                 PRAGMA_OMP_PARALLEL_THREADS(info._numThreads)
                 {
-                    auto threadNum = omp_get_thread_num();                    
+                    auto threadNum = omp_get_thread_num();
                     auto threadOffset = info.getThreadOffset(threadNum);
                     auto ulen = static_cast<unsigned int>(info.getItersPerThread(threadNum));
 
                     PRAGMA_OMP_SIMD
                     for (unsigned int i = 0; i < ulen; i++) {
-                        auto offset = shape::indexOffset(i + threadOffset, xShapeInfo, xShapeInfoCast, len, canCastX);
+                        auto offset = shape::indexOffset(i + threadOffset, xShapeInfo, xShapeInfoCast, canCastX);
                         z[offset] = OpType::op(x[offset], scalar, extraParams);
                     }
                 }
             }
             else {
-                
+
                 uint zShapeInfoCast[MAX_RANK];
                 const bool canCastZ = nd4j::DataTypeUtils::castShapeInfo<uint>(zShapeInfo, zShapeInfoCast);
 
                 PRAGMA_OMP_PARALLEL_THREADS(info._numThreads)
                 {
-                    auto threadNum = omp_get_thread_num();                    
+                    auto threadNum = omp_get_thread_num();
                     auto threadOffset = info.getThreadOffset(threadNum);
                     auto ulen = static_cast<unsigned int>(info.getItersPerThread(threadNum));
 
                     PRAGMA_OMP_SIMD
                     for (unsigned int i = 0; i < ulen; i++) {
-                        auto xOffset = shape::indexOffset(i + threadOffset, xShapeInfo, xShapeInfoCast, len, canCastX);
-                        auto zOffset = shape::indexOffset(i + threadOffset, zShapeInfo, zShapeInfoCast, len, canCastZ);
+                        auto xOffset = shape::indexOffset(i + threadOffset, xShapeInfo, xShapeInfoCast, canCastX);
+                        auto zOffset = shape::indexOffset(i + threadOffset, zShapeInfo, zShapeInfoCast, canCastZ);
                         z[zOffset] = OpType::op(x[xOffset], scalar, extraParams);
                     }
                 }
-            }          
+            }
         }
 
 
@@ -213,7 +213,7 @@ namespace functions {
                 auto x = reinterpret_cast<X *>(vx);
                 auto z = reinterpret_cast<Z *>(vz);
                 auto scalar = reinterpret_cast<X *>(vscalar)[0];
-                auto extraParams = reinterpret_cast<X *>(vextraParams); 
+                auto extraParams = reinterpret_cast<X *>(vextraParams);
 
                 nd4j::OmpLaunchHelper info(len);
 
@@ -231,7 +231,7 @@ namespace functions {
                         for (unsigned int i = 0; i < ulen; i++)
                             zi[i] = OpType::op(xi[i], scalar, extraParams);
                     }
-                } 
+                }
                 else {
 
                     PRAGMA_OMP_PARALLEL_THREADS(info._numThreads)
diff --git a/libnd4j/include/loops/cpu/scalar_int.cpp b/libnd4j/include/loops/cpu/scalar_int.cpp
index 9920cc836..9e73e2756 100644
--- a/libnd4j/include/loops/cpu/scalar_int.cpp
+++ b/libnd4j/include/loops/cpu/scalar_int.cpp
@@ -34,13 +34,13 @@ namespace functions {
         template<typename X>
         template<typename OpType>
         void ScalarIntTransform<X>::transform(void *vx, Nd4jLong *xShapeInfo,
-                                                void *vextraParams, 
-                                                void *vz,  Nd4jLong *zShapeInfo, 
-                                                void *vscalars, 
-                                                int *dimension, int dimensionLength, 
+                                                void *vextraParams,
+                                                void *vz,  Nd4jLong *zShapeInfo,
+                                                void *vscalars,
+                                                int *dimension, int dimensionLength,
                                                 Nd4jLong *xTadShapeInfo, Nd4jLong *xTadOffsets,
                                                 Nd4jLong *zTadShapeInfo, Nd4jLong *zTadOffsets) {
-            
+
             auto x = reinterpret_cast<X *>(vx);
             auto z = reinterpret_cast<X *>(vz);
             auto scalars = reinterpret_cast<X *>(vscalars);
@@ -63,7 +63,7 @@ namespace functions {
                 printf("ScalarIntTransform<X>::transform: super-bad loop visited. Shouldn't ever happen\n");
                 return;
             }
-            
+
             int num_threads = nd4j::math::nd4j_min<int>(numTads, omp_get_max_threads());
 
             if (kindOfLoop == nd4j::LoopKind::EWS1) {
@@ -76,7 +76,7 @@ namespace functions {
                     for (unsigned int f = 0; f < tadLength; f++)
                         oZ[f] = OpType::op(oX[f], scalars[r], extraParams);
                 }
-            } 
+            }
             else { // kindOfLoop != nd4j::LoopKind::EWSNONZERO
                 PRAGMA_OMP_PARALLEL_FOR_THREADS(num_threads)
                 for (unsigned int r = 0; r < numTads; r++) {
@@ -87,7 +87,7 @@ namespace functions {
                     for (unsigned int f = 0; f < tadLength; f++)
                         oZ[f * zTadEws] = OpType::op(oX[f * xTadEws], scalars[r], extraParams);
                 }
-            }          
+            }
         }
 
         template<typename X>
@@ -139,7 +139,7 @@ namespace functions {
                                Nd4jLong *zShapeInfo,
                                void *vscalar,
                                void *vextraParams) {
-            
+
             auto x = reinterpret_cast<X *>(vx);
             auto z = reinterpret_cast<X *>(vz);
             auto scalar = reinterpret_cast<X *>(vscalar)[0];
@@ -162,41 +162,41 @@ namespace functions {
             const bool canCastX = nd4j::DataTypeUtils::castShapeInfo<uint>(xShapeInfo, xShapeInfoCast);
 
             nd4j::OmpLaunchHelper info(len);
-                               
+
             if(shape::haveSameShapeAndStrides(xShapeInfo, zShapeInfo)) {
 
                 PRAGMA_OMP_PARALLEL_THREADS(info._numThreads)
                 {
-                    auto threadNum = omp_get_thread_num();                    
+                    auto threadNum = omp_get_thread_num();
                     auto threadOffset = info.getThreadOffset(threadNum);
                     auto ulen = static_cast<unsigned int>(info.getItersPerThread(threadNum));
 
                     PRAGMA_OMP_SIMD
                     for (unsigned int i = 0; i < ulen; i++) {
-                        auto offset = shape::indexOffset(i + threadOffset, xShapeInfo, xShapeInfoCast, len, canCastX);
+                        auto offset = shape::indexOffset(i + threadOffset, xShapeInfo, xShapeInfoCast, canCastX);
                         z[offset] = OpType::op(x[offset], scalar, extraParams);
                     }
                 }
             }
             else {
-                
+
                 uint zShapeInfoCast[MAX_RANK];
                 const bool canCastZ = nd4j::DataTypeUtils::castShapeInfo<uint>(zShapeInfo, zShapeInfoCast);
 
                 PRAGMA_OMP_PARALLEL_THREADS(info._numThreads)
                 {
-                    auto threadNum = omp_get_thread_num();                    
+                    auto threadNum = omp_get_thread_num();
                     auto threadOffset = info.getThreadOffset(threadNum);
                     auto ulen = static_cast<unsigned int>(info.getItersPerThread(threadNum));
 
                     PRAGMA_OMP_SIMD
                     for (unsigned int i = 0; i < ulen; i++) {
-                        auto xOffset = shape::indexOffset(i + threadOffset, xShapeInfo, xShapeInfoCast, len, canCastX);
-                        auto zOffset = shape::indexOffset(i + threadOffset, zShapeInfo, zShapeInfoCast, len, canCastZ);
+                        auto xOffset = shape::indexOffset(i + threadOffset, xShapeInfo, xShapeInfoCast, canCastX);
+                        auto zOffset = shape::indexOffset(i + threadOffset, zShapeInfo, zShapeInfoCast, canCastZ);
                         z[zOffset] = OpType::op(x[xOffset], scalar, extraParams);
                     }
                 }
-            }          
+            }
         }
 
 
@@ -213,7 +213,7 @@ namespace functions {
                 auto x = reinterpret_cast<X *>(vx);
                 auto z = reinterpret_cast<X *>(vz);
                 auto scalar = reinterpret_cast<X *>(vscalar)[0];
-                auto extraParams = reinterpret_cast<X *>(vextraParams); 
+                auto extraParams = reinterpret_cast<X *>(vextraParams);
 
                 nd4j::OmpLaunchHelper info(len);
 
@@ -231,7 +231,7 @@ namespace functions {
                         for (unsigned int i = 0; i < ulen; i++)
                             zi[i] = OpType::op(xi[i], scalar, extraParams);
                     }
-                } 
+                }
                 else {
 
                     PRAGMA_OMP_PARALLEL_THREADS(info._numThreads)
diff --git a/libnd4j/include/loops/cpu/summarystatsreduce.cpp b/libnd4j/include/loops/cpu/summarystatsreduce.cpp
index ed398db28..1f5a7c339 100644
--- a/libnd4j/include/loops/cpu/summarystatsreduce.cpp
+++ b/libnd4j/include/loops/cpu/summarystatsreduce.cpp
@@ -92,7 +92,7 @@ namespace functions {
 
             for (Nd4jLong i = 0; i < length; i++) {
 
-                auto xOffset = shape::indexOffset(i, xShapeInfo, xShapeInfoCast, length, canCast);
+                auto xOffset = shape::indexOffset(i, xShapeInfo, xShapeInfoCast, canCast);
 
                 SummaryStatsData<X> curr;
                 curr.initWithValue(x[xOffset]);
@@ -175,7 +175,7 @@ namespace functions {
                 }
                 else {
                     for (int i = 1; i < tadLength; i ++) {
-                        auto xOffset = shape::indexOffset(i, tadShapeShapeInfo, tadShapeShapeInfoCast, tadLength, canCast);
+                        auto xOffset = shape::indexOffset(i, tadShapeShapeInfo, tadShapeShapeInfoCast, canCast);
 
                         SummaryStatsData <X> indexVal2;
                         indexVal2.initWithValue(tx[xOffset]);
diff --git a/libnd4j/include/loops/cuda/broadcasting.chpp b/libnd4j/include/loops/cuda/broadcasting.chpp
index d930d8cad..086e216e6 100644
--- a/libnd4j/include/loops/cuda/broadcasting.chpp
+++ b/libnd4j/include/loops/cuda/broadcasting.chpp
@@ -42,7 +42,7 @@ static __global__ void broadcastSimple(
         Nd4jLong *zShapeInfo,
         int *dimension,
         int dimensionLength, Nd4jLong *tadOnlyShapeInfo, Nd4jLong *tadOffsets, Nd4jLong *tadOnlyShapeInfoZ, Nd4jLong *tadOffsetsZ) {
-    
+
     functions::broadcast::Broadcast<X,Y,Z>::template transformCuda<OpClass>(x,xShapeInfo,y,yShapeInfo,z,zShapeInfo,dimension,dimensionLength,tadOnlyShapeInfo,tadOffsets,tadOnlyShapeInfoZ,tadOffsetsZ);
 }
 
@@ -64,8 +64,8 @@ static __global__ void broadcastInverseSimple(
 namespace functions {
     namespace broadcast {
 
-        static Nd4jLong __device__ __noinline__ _getIndexOffset(Nd4jLong index, Nd4jLong *shapeInfo, Nd4jLong length) {
-            return shape::getIndexOffset(index, shapeInfo, length);
+        static Nd4jLong __device__ __noinline__ _getIndexOffset(Nd4jLong index, Nd4jLong *shapeInfo) {
+            return shape::getIndexOffset(index, shapeInfo);
         }
 
         static Nd4jLong __device__ __noinline__ _length(Nd4jLong *shapeInfo) {
@@ -154,9 +154,9 @@ namespace functions {
                 else {
                     // it is expected that x and z tads and y array all have the same length
                     for (Nd4jLong i = threadIdx.x; i < tadLength; i+= blockDim.x) {
-                        auto xOffset = _getIndexOffset(i, xShapeInfo,  tadLength);
-                        auto yOffset = _getIndexOffset(i, tadOnlyShapeInfo, tadLength);
-                        auto zOffset = _getIndexOffset(i, tadOnlyShapeInfoZ, tadLength);
+                        auto xOffset = _getIndexOffset(i, xShapeInfo);
+                        auto yOffset = _getIndexOffset(i, tadOnlyShapeInfo);
+                        auto zOffset = _getIndexOffset(i, tadOnlyShapeInfoZ);
                         rZ[zOffset] = OpType::op(x[xOffset], rY[yOffset]);
                     }
                 }
@@ -170,14 +170,14 @@ namespace functions {
 		                              void *vx, Nd4jLong *xShapeInfo,
 		                              void *vy, Nd4jLong *yShapeInfo,
 		                              void *vz, Nd4jLong *zShapeInfo,
-		                              int *dimension, int dimensionLength, 
+		                              int *dimension, int dimensionLength,
                                       Nd4jLong *tadOnlyShapeInfo, Nd4jLong *tadOffsets, Nd4jLong *tadOnlyShapeInfoZ, Nd4jLong *tadOffsetsZ) {
 
             if (tadOnlyShapeInfoZ == nullptr) {
                 tadOnlyShapeInfoZ = tadOnlyShapeInfo;
                 tadOffsetsZ = tadOffsets;
             }
-            
+
             auto x = reinterpret_cast<X*>(vx);
             auto y = reinterpret_cast<Y*>(vy);
             auto z = reinterpret_cast<Z*>(vz);
@@ -212,16 +212,16 @@ namespace functions {
 
 
                 if(tadEWS > 0 && zEWS > 0 && yEWS > 0 && xOrder == yOrder && xOrder == zOrder) {
-                    for (int i = threadIdx.x; i < tadLength; i+= blockDim.x) 
-                        rZ[i * zEWS] = OpType::op(rX[i * tadEWS], y[i * yEWS]); 
+                    for (int i = threadIdx.x; i < tadLength; i+= blockDim.x)
+                        rZ[i * zEWS] = OpType::op(rX[i * tadEWS], y[i * yEWS]);
                 }
                 else {
                     // it is expected that x and z tads and y array all have the same length
                     for (Nd4jLong i = threadIdx.x; i < tadLength; i+= blockDim.x) {
-                        
-                        auto xOffset = _getIndexOffset(i, tadOnlyShapeInfo,  tadLength);
-                        auto yOffset = _getIndexOffset(i, yShapeInfo, tadLength);
-                        auto zOffset = _getIndexOffset(i, tadOnlyShapeInfoZ, tadLength);
+
+                        auto xOffset = _getIndexOffset(i, tadOnlyShapeInfo);
+                        auto yOffset = _getIndexOffset(i, yShapeInfo);
+                        auto zOffset = _getIndexOffset(i, tadOnlyShapeInfoZ);
                         rZ[zOffset] = OpType::op(rX[xOffset], y[yOffset]);
                     }
                 }
diff --git a/libnd4j/include/loops/cuda/broadcasting_bool.cu b/libnd4j/include/loops/cuda/broadcasting_bool.cu
index 8981790f5..aaec44690 100644
--- a/libnd4j/include/loops/cuda/broadcasting_bool.cu
+++ b/libnd4j/include/loops/cuda/broadcasting_bool.cu
@@ -42,7 +42,7 @@ static __global__ void broadcastBoolSimple(
         Nd4jLong *zShapeInfo,
         int *dimension,
         int dimensionLength, Nd4jLong *tadOnlyShapeInfo, Nd4jLong *tadOffsets, Nd4jLong *tadOnlyShapeInfoZ, Nd4jLong *tadOffsetsZ) {
-    
+
     functions::broadcast::BroadcastBool<X, Z>::template transformCuda<OpClass>(x,xShapeInfo,y,yShapeInfo,z,zShapeInfo,dimension,dimensionLength,tadOnlyShapeInfo,tadOffsets,tadOnlyShapeInfoZ,tadOffsetsZ);
 }
 
@@ -145,9 +145,9 @@ namespace functions {
                 else {
                     // it is expected that x and z tads and y array all have the same length
                     for (Nd4jLong i = threadIdx.x; i < tadLength; i+= blockDim.x) {
-                        auto xOffset = shape::getIndexOffset(i, xShapeInfo,  tadLength);
-                        auto yOffset = shape::getIndexOffset(i, tadOnlyShapeInfo, tadLength);
-                        auto zOffset = shape::getIndexOffset(i, tadOnlyShapeInfoZ, tadLength);
+                        auto xOffset = shape::getIndexOffset(i, xShapeInfo);
+                        auto yOffset = shape::getIndexOffset(i, tadOnlyShapeInfo);
+                        auto zOffset = shape::getIndexOffset(i, tadOnlyShapeInfoZ);
 
                         rZ[zOffset] = OpType::op(x[xOffset], rY[yOffset]);
                     }
@@ -183,13 +183,13 @@ namespace functions {
             __shared__ int numTads;
             __shared__ Nd4jLong yEWS;
             __shared__ Nd4jLong zEWS;
-      
+
             if (threadIdx.x == 0) {
    	            tadLength = shape::length(tadOnlyShapeInfo);//shape::tadLength(xShapeInfo, dimension, dimensionLength);
                 tadEWS = shape::elementWiseStride(tadOnlyShapeInfo);
                 numTads = shape::length(xShapeInfo) / tadLength;
                 yEWS = shape::elementWiseStride(yShapeInfo);
-                zEWS = shape::elementWiseStride(tadOnlyShapeInfoZ);    
+                zEWS = shape::elementWiseStride(tadOnlyShapeInfoZ);
             }
             __syncthreads();
 
@@ -213,9 +213,9 @@ namespace functions {
             else {
                 // it is expected that x and z tads and y array all have the same length
                 for (Nd4jLong i = threadIdx.x; i < tadLength; i+= blockDim.x) {
-                    auto xOffset = shape::getIndexOffset(i, tadOnlyShapeInfo,  tadLength);
-                    auto yOffset = shape::getIndexOffset(i, yShapeInfo, tadLength);
-                    auto zOffset = shape::getIndexOffset(i, tadOnlyShapeInfoZ, tadLength);
+                    auto xOffset = shape::getIndexOffset(i, tadOnlyShapeInfo);
+                    auto yOffset = shape::getIndexOffset(i, yShapeInfo);
+                    auto zOffset = shape::getIndexOffset(i, tadOnlyShapeInfoZ);
 
                     rZ[zOffset] = OpType::op(rX[xOffset], y[yOffset]);
                 }
diff --git a/libnd4j/include/loops/cuda/broadcasting_int.cu b/libnd4j/include/loops/cuda/broadcasting_int.cu
index 38193f35d..fc613a438 100644
--- a/libnd4j/include/loops/cuda/broadcasting_int.cu
+++ b/libnd4j/include/loops/cuda/broadcasting_int.cu
@@ -42,7 +42,7 @@ static __global__ void broadcastIntSimple(
         Nd4jLong *zShapeInfo,
         int *dimension,
         int dimensionLength, Nd4jLong *tadOnlyShapeInfo, Nd4jLong *tadOffsets, Nd4jLong *tadOnlyShapeInfoZ, Nd4jLong *tadOffsetsZ) {
-    
+
     functions::broadcast::BroadcastInt<X>::template transformCuda<OpClass>(x,xShapeInfo,y,yShapeInfo,z,zShapeInfo,dimension,dimensionLength,tadOnlyShapeInfo,tadOffsets,tadOnlyShapeInfoZ,tadOffsetsZ);
 }
 
@@ -139,9 +139,9 @@ namespace functions {
                 else {
                     // it is expected that x and z tads and y array all have the same length
                     for (Nd4jLong i = threadIdx.x; i < tadLength; i+= blockDim.x) {
-                        auto xOffset = shape::getIndexOffset(i, xShapeInfo,  tadLength);
-                        auto yOffset = shape::getIndexOffset(i, tadOnlyShapeInfo, tadLength);
-                        auto zOffset = shape::getIndexOffset(i, tadOnlyShapeInfoZ, tadLength);
+                        auto xOffset = shape::getIndexOffset(i, xShapeInfo);
+                        auto yOffset = shape::getIndexOffset(i, tadOnlyShapeInfo);
+                        auto zOffset = shape::getIndexOffset(i, tadOnlyShapeInfoZ);
 
                         rZ[zOffset] = OpType::op(x[xOffset], rY[yOffset]);
                     }
@@ -177,13 +177,13 @@ namespace functions {
             __shared__ int numTads;
             __shared__ Nd4jLong yEWS;
             __shared__ Nd4jLong zEWS;
-      
+
             if (threadIdx.x == 0) {
    	            tadLength = shape::length(tadOnlyShapeInfo);//shape::tadLength(xShapeInfo, dimension, dimensionLength);
                 tadEWS = shape::elementWiseStride(tadOnlyShapeInfo);
                 numTads = shape::length(xShapeInfo) / tadLength;
                 yEWS = shape::elementWiseStride(yShapeInfo);
-                zEWS = shape::elementWiseStride(tadOnlyShapeInfoZ);    
+                zEWS = shape::elementWiseStride(tadOnlyShapeInfoZ);
             }
             __syncthreads();
 
@@ -207,9 +207,9 @@ namespace functions {
             else {
                 // it is expected that x and z tads and y array all have the same length
                 for (Nd4jLong i = threadIdx.x; i < tadLength; i+= blockDim.x) {
-                    auto xOffset = shape::getIndexOffset(i, tadOnlyShapeInfo,  tadLength);
-                    auto yOffset = shape::getIndexOffset(i, yShapeInfo, tadLength);
-                    auto zOffset = shape::getIndexOffset(i, tadOnlyShapeInfoZ, tadLength);
+                    auto xOffset = shape::getIndexOffset(i, tadOnlyShapeInfo);
+                    auto yOffset = shape::getIndexOffset(i, yShapeInfo);
+                    auto zOffset = shape::getIndexOffset(i, tadOnlyShapeInfoZ);
 
                     rZ[zOffset] = OpType::op(rX[xOffset], y[yOffset]);
                 }
diff --git a/libnd4j/include/loops/cuda/indexreduce.cu b/libnd4j/include/loops/cuda/indexreduce.cu
index 5f0cf07ae..8a560e416 100644
--- a/libnd4j/include/loops/cuda/indexreduce.cu
+++ b/libnd4j/include/loops/cuda/indexreduce.cu
@@ -246,12 +246,12 @@ namespace functions {
                 if (dimensionLength > 1 || tadEWS < 1) {
 
                     for (int r = blockIdx.x; r < numTads; r += gridDim.x) {
-                        
+
                         auto tadOffsetForBlock = tadOffsets[r];
                         sPartials[threadIdx.x] = OpType::startingIndexValue(dx);
 
-                        for(int i = threadIdx.x;i < tadLength; i += blockDim.x) {                            
-                            auto xOffset = tadOffsetForBlock + shape::getIndexOffset(i, tadOnlyShapeInfo, tadLength);
+                        for(int i = threadIdx.x;i < tadLength; i += blockDim.x) {
+                            auto xOffset = tadOffsetForBlock + shape::getIndexOffset(i, tadOnlyShapeInfo);
                             IndexValue<X> comp {dx[xOffset], i};
                             sPartials[threadIdx.x] = OpType::update(sPartials[threadIdx.x], comp, extraParams);
                         }
@@ -297,9 +297,9 @@ namespace functions {
                         reduction = OpType::update(reduction, indexVal, extraParams);
                     }
                 } else {
-                                        
-                    for(Nd4jLong i = tid;i < n; i += blockDim.x * gridDim.x) {                                                
-                        auto offset = shape::getIndexOffset(i, xShapeInfo, n);
+
+                    for(Nd4jLong i = tid;i < n; i += blockDim.x * gridDim.x) {
+                        auto offset = shape::getIndexOffset(i, xShapeInfo);
                         IndexValue<X> indexVal = {dx[offset], i};
                         reduction = OpType::update(reduction, indexVal, extraParams);
                     }
diff --git a/libnd4j/include/loops/cuda/inplace_loops/reduce_same_inplace.h b/libnd4j/include/loops/cuda/inplace_loops/reduce_same_inplace.h
index 3c79f443b..5df583e61 100644
--- a/libnd4j/include/loops/cuda/inplace_loops/reduce_same_inplace.h
+++ b/libnd4j/include/loops/cuda/inplace_loops/reduce_same_inplace.h
@@ -115,7 +115,7 @@ namespace functions {
                     sPartials[threadIdx.x] = OpType::update(sPartials[threadIdx.x], OpType::op(x[i * xEws], extraParams), extraParams);
             else
                 for (int i = tid; i < len; i += blockDim.x * gridDim.x)
-                    sPartials[threadIdx.x] = OpType::update(sPartials[threadIdx.x], OpType::op(x[shape::getIndexOffset(i, xShapeInfo, len)], extraParams), extraParams);
+                    sPartials[threadIdx.x] = OpType::update(sPartials[threadIdx.x], OpType::op(x[shape::getIndexOffset(i, xShapeInfo)], extraParams), extraParams);
 
             __syncthreads();
             aggregatePartials<OpType>(sPartials, threadIdx.x, nd4j::math::nd4j_min<int>(blockDim.x, len), extraParams);
diff --git a/libnd4j/include/loops/cuda/inplace_loops/scalar_inplace.h b/libnd4j/include/loops/cuda/inplace_loops/scalar_inplace.h
index cb87ea461..9e061003d 100644
--- a/libnd4j/include/loops/cuda/inplace_loops/scalar_inplace.h
+++ b/libnd4j/include/loops/cuda/inplace_loops/scalar_inplace.h
@@ -73,7 +73,7 @@ namespace functions {
 
 
             for (Nd4jLong i = tid; i < length; i+= totalThreads) {
-                z[shape::getIndexOffset(i, zShapeInfo, length)] = OpType::op(y[shape::getIndexOffset(i, yShapeInfo, length)], scalar, params);
+                z[shape::getIndexOffset(i, zShapeInfo)] = OpType::op(y[shape::getIndexOffset(i, yShapeInfo)], scalar, params);
             }
         }
     }
diff --git a/libnd4j/include/loops/cuda/inplace_loops/transform_strict_inplace.h b/libnd4j/include/loops/cuda/inplace_loops/transform_strict_inplace.h
index e3f653350..b10b23d09 100644
--- a/libnd4j/include/loops/cuda/inplace_loops/transform_strict_inplace.h
+++ b/libnd4j/include/loops/cuda/inplace_loops/transform_strict_inplace.h
@@ -72,8 +72,8 @@ namespace functions {
 
 
             for (Nd4jLong i = tid; i < length; i+= gridDim.x * blockDim.x) {
-                auto xOffset2 = shape::getIndexOffset(i, shapeInfo,  length);
-                auto zOffset2 = shape::getIndexOffset(i, zShapeInfo, length);
+                auto xOffset2 = shape::getIndexOffset(i, shapeInfo);
+                auto zOffset2 = shape::getIndexOffset(i, zShapeInfo);
                 result[zOffset2] = OpType::op(dy[xOffset2], params);
             }
         }
diff --git a/libnd4j/include/loops/cuda/legacy/reduce.legacy b/libnd4j/include/loops/cuda/legacy/reduce.legacy
index 7b365f9fe..1ae7985de 100644
--- a/libnd4j/include/loops/cuda/legacy/reduce.legacy
+++ b/libnd4j/include/loops/cuda/legacy/reduce.legacy
@@ -169,7 +169,7 @@ namespace functions {
 
             template <>
             _CUDA_H void ReduceFunction<float>::execReduceScalar(dim3 launchDims, cudaStream_t *stream, int opNum, float *x, Nd4jLong *xShapeInfo, float *extraParams, float *z, Nd4jLong *zShapeInfo, int *dimension, int dimensionLength, float *reductionBuffer, Nd4jLong *tadOnlyShapeInfo) {
-                
+
                 DISPATCH_SIMPLE(reduceScalarSimple, float, PARAMS(x, xShapeInfo, extraParams, z, zShapeInfo, nullptr, 1, reductionBuffer, tadOnlyShapeInfo), OPS_A(REDUCE_OPS))
 
 				nd4j::DebugHelper::checkErrorCode(stream, "execReduceScalarFloat(...) failed");
@@ -177,7 +177,7 @@ namespace functions {
 
             template <>
             _CUDA_H void ReduceFunction<float16>::execReduceScalar(dim3 launchDims, cudaStream_t *stream, int opNum, float16 *x, Nd4jLong *xShapeInfo, float16 *extraParams, float16 *z, Nd4jLong *zShapeInfo, int *dimension, int dimensionLength, float16 *reductionBuffer, Nd4jLong *tadOnlyShapeInfo) {
-                
+
                 DISPATCH_SIMPLE(reduceScalarSimple, float16, PARAMS(x, xShapeInfo, extraParams, z, zShapeInfo, nullptr, 1, reductionBuffer, tadOnlyShapeInfo), OPS_A(REDUCE_OPS))
 
 				nd4j::DebugHelper::checkErrorCode(stream, "execReduceScalarHalf(...) failed");
@@ -185,7 +185,7 @@ namespace functions {
 
             template <>
             _CUDA_H void ReduceFunction<double>::execReduceScalar(dim3 launchDims, cudaStream_t *stream, int opNum, double *x, Nd4jLong *xShapeInfo, double *extraParams, double *z, Nd4jLong *zShapeInfo, int *dimension, int dimensionLength, double *reductionBuffer, Nd4jLong *tadOnlyShapeInfo) {
-                
+
                 DISPATCH_SIMPLE(reduceScalarSimple, double, PARAMS(x, xShapeInfo, extraParams, z, zShapeInfo, nullptr, 1, reductionBuffer, tadOnlyShapeInfo), OPS_A(REDUCE_OPS))
 
 				nd4j::DebugHelper::checkErrorCode(stream, "execReduceScalarDouble(...) failed");
@@ -294,7 +294,7 @@ namespace functions {
 
                         for (int i = threadIdx.x; i < tadLength; i += blockDim.x) {
 						    shape::ind2subC(tadRank, tadShape, i, tadLength, xCoord);
-						    auto xOffset = shape::getOffset(tadOffsetForBlock, tadShape, tadStride, xCoord, tadRank);
+						    auto xOffset = shape::getOffset(tadOnlyShapeInfo, xCoord);
 
 						    sPartials[threadIdx.x] = OpType::update(sPartials[threadIdx.x], OpType::op(dx[xOffset], extraParams), extraParams);
 					    }
@@ -358,7 +358,7 @@ namespace functions {
 					for (int i = tid; i < n; i += blockDim.x * gridDim.x) {
 						shape::ind2subC(rank, xShape, i, n, ind2sub);
 
-						auto offset = shape::getOffset(0, xShape, xStride, ind2sub, rank);
+						auto offset = shape::getOffset(xShapeInfo, ind2sub);
 						sPartials[threadIdx.x] = OpType::update(sPartials[threadIdx.x], OpType::op(dx[offset], extraParams), extraParams);
 					}
 				}
@@ -461,7 +461,7 @@ namespace functions {
 
 					for (int i = threadIdx.x; i < tadLength; i += blockDim.x) {
 						shape::ind2subC(tadRank, tadShape, i, tadLength, xCoord);
-						auto xOffset = shape::getOffset(tadOffsetForBlock, tadShape, tadStride, xCoord, tadRank);
+						auto xOffset = shape::getOffset(tadOnlyShapeInfo, xCoord);
 
 						sPartials[threadIdx.x] = OpType::update(sPartials[threadIdx.x], OpType::op(dx[xOffset], extraParams), extraParams);
 					}
@@ -526,7 +526,7 @@ namespace functions {
 
 					for (int i = threadIdx.x; i < tadLength; i += blockDim.x) {
 						shape::ind2subC(tadRank, tadShape, i, tadLength, xCoord);
-						auto xOffset = shape::getOffset(tadOffsetForBlock, tadShape, tadStride, xCoord, tadRank);
+						auto xOffset = shape::getOffset(tadOnlyShapeInfo, xCoord);
 
 						sPartials[threadIdx.x] = OpType::update(sPartials[threadIdx.x], OpType::op(dx[xOffset], extraParams), extraParams);
 					}
diff --git a/libnd4j/include/loops/cuda/legacy/scalar_temp.legacy b/libnd4j/include/loops/cuda/legacy/scalar_temp.legacy
index 73cb9c6ff..7bc30271f 100644
--- a/libnd4j/include/loops/cuda/legacy/scalar_temp.legacy
+++ b/libnd4j/include/loops/cuda/legacy/scalar_temp.legacy
@@ -88,8 +88,8 @@ static inline __device__ void transformCuda(T scalar, T *dy, int *shapeInfo, T *
 
         for (Nd4jLong i = tid; i < length; i+= totalThreads) {
             shape::ind2sub(xRank, xShape, i, length, xIdx);
-            int xOffset2 = shape::getOffset(0, xShape, xStride, xIdx, xRank);
-            int resultOffset = shape::getOffset(0, zShape, zStride, xIdx, zRank);
+            int xOffset2 = shape::getOffset(shapeInfo, xIdx);
+            int resultOffset = shape::getOffset(0resultShapeInfo, xIdx);
             result[resultOffset] = OpType::op(dy[xOffset2],scalar, params);
         }
     }
diff --git a/libnd4j/include/loops/cuda/legacy/transform.legacy b/libnd4j/include/loops/cuda/legacy/transform.legacy
index ed321e79c..6a8344916 100644
--- a/libnd4j/include/loops/cuda/legacy/transform.legacy
+++ b/libnd4j/include/loops/cuda/legacy/transform.legacy
@@ -111,7 +111,7 @@ __device__ void transformSimpleGeneric(
 		manager->init(sizeof(UnifiedSharedMemory), 0, sizeof(functions::transform::Transform<T>), sizeof(shape::TAD), xRank);
 	}
 	__syncthreads();
-	
+
     functions::transform::Transform<T>::template transformCuda<OpClass>(
 	    dy,
 	    xShapeInfo,
@@ -161,7 +161,7 @@ namespace functions {
 
         template <>
         _CUDA_H void Transform<float>::executeTransformShaped(dim3 launchDims, cudaStream_t *stream, int opNum, float *x, Nd4jLong *xShape, int xRank, float *extraParams, float *z, Nd4jLong *zShape, int zRank, int *allocationPointer, float *reductionPointer,  Nd4jLong *tadShapeInfo, Nd4jLong *tadOffsets) {
-            
+
             DISPATCH_SIMPLE(transformShaped, float, PARAMS(x, xShape, xRank, extraParams, z, zShape, zRank, allocationPointer, reductionPointer, tadShapeInfo, tadOffsets), OPS_A(TRANSFORM_OPS))
 
 
@@ -170,16 +170,16 @@ namespace functions {
 
         template <>
         _CUDA_H void Transform<float16>::executeTransformShaped(dim3 launchDims, cudaStream_t *stream, int opNum, float16 *x, Nd4jLong *xShape, int xRank, float16 *extraParams, float16 *z, Nd4jLong *zShape, int zRank, int *allocationPointer, float16 *reductionPointer,  Nd4jLong *tadShapeInfo, Nd4jLong *tadOffsets) {
-            
+
             DISPATCH_SIMPLE(transformShaped, float16, PARAMS(x, xShape, xRank, extraParams, z, zShape, zRank, allocationPointer, reductionPointer, tadShapeInfo, tadOffsets), OPS_A(TRANSFORM_OPS))
-            
+
             if (nd4j::Environment::getInstance()->isDebug())
 		        checkCudaErrors(cudaStreamSynchronize(*stream));
         }
 
         template <>
         _CUDA_H void Transform<double>::executeTransformShaped(dim3 launchDims, cudaStream_t *stream, int opNum, double *x, Nd4jLong *xShape, int xRank, double *extraParams, double *z, Nd4jLong *zShape, int zRank, int *allocationPointer, double *reductionPointer,  Nd4jLong *tadShapeInfo, Nd4jLong *tadOffsets) {
-            
+
             DISPATCH_SIMPLE(transformShaped, double, PARAMS(x, xShape, xRank, extraParams, z, zShape, zRank, allocationPointer, reductionPointer, tadShapeInfo, tadOffsets), OPS_A(TRANSFORM_OPS))
 
             DEBUG_KERNEL(stream, opNum);
@@ -226,13 +226,13 @@ namespace functions {
 		        }
 		        else {
 			        Nd4jLong xCoord[MAX_RANK];
-			
+
 		    	    for (Nd4jLong i = tid; i < length; i+= gridDim.x * blockDim.x) {
 						shape::ind2sub(xRank,shape::shapeOf(shapeInfo),i, length, xCoord);
-						
-				        auto xOffset2 = shape::getOffset(0, xShape, xStride, xCoord, xRank);
-						auto resultOffset2 = shape::getOffset(0,xShape,shape::stride(resultShapeInfo),xCoord,xRank);
-						
+
+				        auto xOffset2 = shape::getOffset(shapeInfo, xCoord);
+						auto resultOffset2 = shape::getOffset(resultShapeInfo, xCoord);
+
 	    			    result[resultOffset2] = OpType::op(dy[xOffset2], params);
 		    	    }
 		        }
@@ -249,7 +249,7 @@ namespace functions {
 			T *result,
 			Nd4jLong resultStride,
 			int *allocationPointer, T *reductionPointer, UnifiedSharedMemory *manager) {
-		
+
             int totalThreads = gridDim.x * blockDim.x;
 		    Nd4jLong i = blockIdx.x * blockDim.x + threadIdx.x;
 
diff --git a/libnd4j/include/loops/cuda/pairwise.chpp b/libnd4j/include/loops/cuda/pairwise.chpp
index 3f7134887..d3252d862 100644
--- a/libnd4j/include/loops/cuda/pairwise.chpp
+++ b/libnd4j/include/loops/cuda/pairwise.chpp
@@ -28,11 +28,11 @@ using namespace simdOps;
 
 ////////////////////////////////////////////////////////////////////////////////
 template <typename X, typename Y, typename Z, typename OpType>
-__global__ static void pairwiseSimpleShaped(void* vx, Nd4jLong *xShapeInfo, 
-											void *vy, Nd4jLong *yShapeInfo, 
-											void *vz, Nd4jLong *zShapeInfo, 
+__global__ static void pairwiseSimpleShaped(void* vx, Nd4jLong *xShapeInfo,
+											void *vy, Nd4jLong *yShapeInfo,
+											void *vz, Nd4jLong *zShapeInfo,
 											void *vextraParams) {
-	
+
 	auto x = reinterpret_cast<X*>(vx);
 	auto y = reinterpret_cast<Y*>(vy);
 	auto z = reinterpret_cast<Z*>(vz);
@@ -67,17 +67,17 @@ __global__ static void pairwiseSimpleShaped(void* vx, Nd4jLong *xShapeInfo,
 	}
 	else if (vx == vz) {
 		for (Nd4jLong i = tid; i < len; i += gridDim.x * blockDim.x) {
-			auto xOffset = shape::getIndexOffset(i, xShapeInfo, len);
-			auto yOffset = shape::getIndexOffset(i, yShapeInfo, len);
-				
+			auto xOffset = shape::getIndexOffset(i, xShapeInfo);
+			auto yOffset = shape::getIndexOffset(i, yShapeInfo);
+
 			z[xOffset] = OpType::op(x[xOffset], y[yOffset], extraParams);
 		}
 	}
 	else {
 		for (Nd4jLong i = tid; i < len; i += gridDim.x * blockDim.x) {
-			auto xOffset = shape::getIndexOffset(i, xShapeInfo, len);
-			auto yOffset = shape::getIndexOffset(i, yShapeInfo, len);
-			auto zOffset = shape::getIndexOffset(i, zShapeInfo, len);
+			auto xOffset = shape::getIndexOffset(i, xShapeInfo);
+			auto yOffset = shape::getIndexOffset(i, yShapeInfo);
+			auto zOffset = shape::getIndexOffset(i, zShapeInfo);
 
 			z[zOffset] = OpType::op(x[xOffset], y[yOffset], extraParams);
 		}
diff --git a/libnd4j/include/loops/cuda/pairwise_bool.cu b/libnd4j/include/loops/cuda/pairwise_bool.cu
index 62f040191..414aadd30 100644
--- a/libnd4j/include/loops/cuda/pairwise_bool.cu
+++ b/libnd4j/include/loops/cuda/pairwise_bool.cu
@@ -67,17 +67,17 @@ __global__ static void pairwiseSimpleShaped(void* vx, Nd4jLong *xShapeInfo,
 	}
 	else if (vx == vz) {
 		for (Nd4jLong i = tid; i < len; i += gridDim.x * blockDim.x) {
-			auto xOffset = shape::getIndexOffset(i, xShapeInfo, len);
-			auto yOffset = shape::getIndexOffset(i, yShapeInfo, len);
-				
+			auto xOffset = shape::getIndexOffset(i, xShapeInfo);
+			auto yOffset = shape::getIndexOffset(i, yShapeInfo);
+
 			z[xOffset] = OpType::op(x[xOffset], y[yOffset], extraParams);
 		}
 	}
 	else {
 		for (Nd4jLong i = tid; i < len; i += gridDim.x * blockDim.x) {
-			auto xOffset = shape::getIndexOffset(i, xShapeInfo, len);
-			auto yOffset = shape::getIndexOffset(i, yShapeInfo, len);
-			auto zOffset = shape::getIndexOffset(i, zShapeInfo, len);
+			auto xOffset = shape::getIndexOffset(i, xShapeInfo);
+			auto yOffset = shape::getIndexOffset(i, yShapeInfo);
+			auto zOffset = shape::getIndexOffset(i, zShapeInfo);
 
 			z[zOffset] = OpType::op(x[xOffset], y[yOffset], extraParams);
 		}
@@ -105,7 +105,7 @@ void _CUDA_H PairWiseBoolTransform<X,Z>::intermediateShaped(dim3& launchDims, cu
 template<typename X, typename Y>
 void PairWiseBoolTransform<X,Y>::executeCudaShaped(dim3& launchDims, cudaStream_t *stream, int opNum, void *vx, Nd4jLong *xShapeInfo, void *vy, Nd4jLong *yShapeInfo, void *vz, Nd4jLong *zShapeInfo, void *vextraParams) {
     auto xType = nd4j::DataTypeUtils::fromT<X>();
-    auto yType = nd4j::DataTypeUtils::fromT<Y>();    
+    auto yType = nd4j::DataTypeUtils::fromT<Y>();
 
 	DISPATCH_BY_OPNUM_TT(intermediateShaped, PARAMS(launchDims, stream, vx, xShapeInfo, vy, yShapeInfo, vz, zShapeInfo, vextraParams), PAIRWISE_BOOL_OPS);
 }
@@ -166,7 +166,7 @@ void PairWiseBoolTransform<X,Y>::executeCudaShaped(dim3& launchDims, cudaStream_
     }
 
 
-      
+
     BUILD_DOUBLE_TEMPLATE(template class ND4J_EXPORT PairWiseBoolTransform, , LIBND4J_TYPES, BOOL_TYPES);
 }
 }
diff --git a/libnd4j/include/loops/cuda/pairwise_int.cu b/libnd4j/include/loops/cuda/pairwise_int.cu
index 5cc12846c..2bedb4a82 100644
--- a/libnd4j/include/loops/cuda/pairwise_int.cu
+++ b/libnd4j/include/loops/cuda/pairwise_int.cu
@@ -67,17 +67,17 @@ __global__ static void pairwiseSimpleShaped(void* vx, Nd4jLong *xShapeInfo,
 	}
 	else if (vx == vz) {
 		for (Nd4jLong i = tid; i < len; i += gridDim.x * blockDim.x) {
-			auto xOffset = shape::getIndexOffset(i, xShapeInfo, len);
-			auto yOffset = shape::getIndexOffset(i, yShapeInfo, len);
-				
+			auto xOffset = shape::getIndexOffset(i, xShapeInfo);
+			auto yOffset = shape::getIndexOffset(i, yShapeInfo);
+
 			z[xOffset] = OpType::op(x[xOffset], y[yOffset], extraParams);
 		}
 	}
 	else {
 		for (Nd4jLong i = tid; i < len; i += gridDim.x * blockDim.x) {
-			auto xOffset = shape::getIndexOffset(i, xShapeInfo, len);
-			auto yOffset = shape::getIndexOffset(i, yShapeInfo, len);
-			auto zOffset = shape::getIndexOffset(i, zShapeInfo, len);
+			auto xOffset = shape::getIndexOffset(i, xShapeInfo);
+			auto yOffset = shape::getIndexOffset(i, yShapeInfo);
+			auto zOffset = shape::getIndexOffset(i, zShapeInfo);
 
 			z[zOffset] = OpType::op(x[xOffset], y[yOffset], extraParams);
 		}
@@ -165,7 +165,7 @@ void PairWiseIntTransform<X>::executeCudaShaped(dim3& launchDims, cudaStream_t *
     }
 
 
-      
+
     BUILD_SINGLE_TEMPLATE(template class ND4J_EXPORT PairWiseIntTransform, , INTEGER_TYPES);
 }
 }
diff --git a/libnd4j/include/loops/cuda/random.cu b/libnd4j/include/loops/cuda/random.cu
index 727f0868f..3bf06ae91 100644
--- a/libnd4j/include/loops/cuda/random.cu
+++ b/libnd4j/include/loops/cuda/random.cu
@@ -116,7 +116,7 @@ namespace functions {
                 auto y = reinterpret_cast<T*>(vy);
                 auto z = reinterpret_cast<T*>(vz);
                 auto extraArguments = reinterpret_cast<T*>(vextraArguments);
-                
+
                 if (OpClass::requiresSpecial) {
                     OpClass::specialOpCuda(state, x, xShapeBuffer, y, yShapeBuffer, z, zShapeBuffer, extraArguments);
                     return;
@@ -166,10 +166,10 @@ namespace functions {
                     }
                 } else {
                     for (Nd4jLong i = tid; i < length; i += blockDim.x * gridDim.x) {
-                        
-                        auto xOffset2 = shape::getIndexOffset(i, xShapeBuffer, length);
-                        auto yOffset2 = shape::getIndexOffset(i, yShapeBuffer, length);
-                        auto zOffset2 = shape::getIndexOffset(i, zShapeBuffer, length);                        
+
+                        auto xOffset2 = shape::getIndexOffset(i, xShapeBuffer);
+                        auto yOffset2 = shape::getIndexOffset(i, yShapeBuffer);
+                        auto zOffset2 = shape::getIndexOffset(i, zShapeBuffer);
 
                             z[zOffset2] = OpClass::op(x[xOffset2], y[yOffset2], i, length, buffer, extraArguments);
                         }
@@ -224,11 +224,11 @@ namespace functions {
                         z[e * zEWS] = OpClass::op(x[e * xEWS], e, length, buffer, extraArguments);
                     }
                 } else {
-                    
+
                     for (Nd4jLong i = blockIdx.x * blockDim.x + threadIdx.x; i < length; i += blockDim.x * gridDim.x) {
-                        
-                        auto xOffset2 = shape::getIndexOffset(i, xShapeBuffer, length);
-                        auto zOffset2 = shape::getIndexOffset(i, zShapeBuffer, length);
+
+                        auto xOffset2 = shape::getIndexOffset(i, xShapeBuffer);
+                        auto zOffset2 = shape::getIndexOffset(i, zShapeBuffer);
 
                         z[zOffset2] = OpClass::op(x[xOffset2], i, length, buffer, extraArguments);
                     }
@@ -274,9 +274,9 @@ namespace functions {
                         z[i * ews] = OpClass::op(i, length, buffer, extraArguments);
                     }
                 } else {
-                    
-                    for (Nd4jLong i = tid; i < length; i += blockDim.x * gridDim.x) {                        
-                        auto zOffset2 = shape::getIndexOffset(i, zShapeBuffer, length);
+
+                    for (Nd4jLong i = tid; i < length; i += blockDim.x * gridDim.x) {
+                        auto zOffset2 = shape::getIndexOffset(i, zShapeBuffer);
                         z[zOffset2] = OpClass::op(i, length, buffer, extraArguments);
                     }
                 }
@@ -296,7 +296,7 @@ namespace functions {
 
         template <>
         _CUDA_H void RandomFunction<float16>::executeCudaSingle(dim3& launchDims, cudaStream_t* stream, int opNum, Nd4jPointer stateHost, void *vz, Nd4jLong *zShapeBuffer, void *vextraArguments) {
-            
+
             auto z = reinterpret_cast<float16*>(vz);
             auto extraArguments = reinterpret_cast<float16*>(vextraArguments);
 
@@ -320,7 +320,7 @@ namespace functions {
 
         template <>
         _CUDA_H void RandomFunction<double>::executeCudaSingle(dim3& launchDims, cudaStream_t *stream, int opNum, Nd4jPointer stateHost, void *vz, Nd4jLong *zShapeBuffer, void *vextraArguments) {
-            
+
             auto z = reinterpret_cast<double*>(vz);
             auto extraArguments = reinterpret_cast<double*>(vextraArguments);
 
@@ -332,7 +332,7 @@ namespace functions {
 
         template <>
         _CUDA_H void RandomFunction<float>::executeCudaDouble(dim3& launchDims, cudaStream_t* stream, int opNum, Nd4jPointer stateHost, void *vx, Nd4jLong *xShapeBuffer, void *vz, Nd4jLong *zShapeBuffer, void *vextraArguments) {
-            
+
             auto x = reinterpret_cast<float*>(vx);
             auto z = reinterpret_cast<float*>(vz);
             auto extraArguments = reinterpret_cast<float*>(vextraArguments);
@@ -346,7 +346,7 @@ namespace functions {
 
         template <>
         _CUDA_H void RandomFunction<float16>::executeCudaDouble(dim3& launchDims, cudaStream_t* stream, int opNum, Nd4jPointer stateHost, void *vx, Nd4jLong *xShapeBuffer, void *vz, Nd4jLong *zShapeBuffer, void *vextraArguments) {
-            
+
             auto x = reinterpret_cast<float16*>(vx);
             auto z = reinterpret_cast<float16*>(vz);
             auto extraArguments = reinterpret_cast<float16*>(vextraArguments);
@@ -372,7 +372,7 @@ namespace functions {
 
         template <>
         _CUDA_H void RandomFunction<double>::executeCudaDouble(dim3& launchDims, cudaStream_t* stream, int opNum, Nd4jPointer stateHost, void *vx, Nd4jLong *xShapeBuffer, void *vz, Nd4jLong *zShapeBuffer, void *vextraArguments) {
-            
+
             auto x = reinterpret_cast<double*>(vx);
             auto z = reinterpret_cast<double*>(vz);
             auto extraArguments = reinterpret_cast<double*>(vextraArguments);
@@ -385,7 +385,7 @@ namespace functions {
 
         template <>
         _CUDA_H void RandomFunction<float>::executeCudaTriple(dim3& launchDims, cudaStream_t* stream, int opNum, Nd4jPointer stateHost, void *vx, Nd4jLong *xShapeBuffer, void *vy, Nd4jLong *yShapeBuffer, void *vz, Nd4jLong *zShapeBuffer, void *vextraArguments) {
-            
+
 
             auto x = reinterpret_cast<float*>(vx);
             auto y = reinterpret_cast<float*>(vy);
@@ -400,7 +400,7 @@ namespace functions {
 
         template <>
         _CUDA_H void RandomFunction<float16>::executeCudaTriple(dim3& launchDims, cudaStream_t* stream, int opNum, Nd4jPointer stateHost, void *vx, Nd4jLong *xShapeBuffer, void *vy, Nd4jLong *yShapeBuffer, void *vz, Nd4jLong *zShapeBuffer, void *vextraArguments) {
-            
+
             auto x = reinterpret_cast<float16*>(vx);
             auto y = reinterpret_cast<float16*>(vy);
             auto z = reinterpret_cast<float16*>(vz);
diff --git a/libnd4j/include/loops/cuda/reduce/reduce_bool.cu b/libnd4j/include/loops/cuda/reduce/reduce_bool.cu
index fea9bcb63..a785094f1 100644
--- a/libnd4j/include/loops/cuda/reduce/reduce_bool.cu
+++ b/libnd4j/include/loops/cuda/reduce/reduce_bool.cu
@@ -129,7 +129,7 @@ __device__ void ReduceBoolFunction<X,Z>::transformCudaXD( void *vx, Nd4jLong *xS
 
         for (int i = threadIdx.x; i < tadLength; i += blockDim.x) {
 
-            auto xOffset = tadOffsetForBlock + shape::getIndexOffset(i, tadOnlyShapeInfo, tadLength);
+            auto xOffset = tadOffsetForBlock + shape::getIndexOffset(i, tadOnlyShapeInfo);
             sPartials[threadIdx.x] = OpType::update(sPartials[threadIdx.x], OpType::op(x[xOffset], extraParams), extraParams);
         }
         __syncthreads();
@@ -140,7 +140,7 @@ __device__ void ReduceBoolFunction<X,Z>::transformCudaXD( void *vx, Nd4jLong *xS
         __syncthreads();
 
         if (threadIdx.x == 0)
-            z[isPlainOutput ? r : shape::getIndexOffset(r, zShapeInfo, numTads)] = OpType::postProcess(sPartials[threadIdx.x], tadLength, extraParams);
+            z[isPlainOutput ? r : shape::getIndexOffset(r, zShapeInfo)] = OpType::postProcess(sPartials[threadIdx.x], tadLength, extraParams);
     }
 }
 
@@ -180,7 +180,7 @@ __device__ void ReduceBoolFunction<X,Z>::execScalarCuda(void *vx, Nd4jLong *xSha
             sPartials[threadIdx.x] = OpType::update(sPartials[threadIdx.x], OpType::op(x[i * xEws], extraParams), extraParams);
     else
         for (int i = tid; i < len; i += blockDim.x * gridDim.x)
-            sPartials[threadIdx.x] = OpType::update(sPartials[threadIdx.x], OpType::op(x[shape::getIndexOffset(i, xShapeInfo, len)], extraParams), extraParams);
+            sPartials[threadIdx.x] = OpType::update(sPartials[threadIdx.x], OpType::op(x[shape::getIndexOffset(i, xShapeInfo)], extraParams), extraParams);
 
     __syncthreads();
     aggregatePartials<OpType>(sPartials, threadIdx.x, nd4j::math::nd4j_min<int>(blockDim.x, len), extraParams);
diff --git a/libnd4j/include/loops/cuda/reduce/reduce_float.chpp b/libnd4j/include/loops/cuda/reduce/reduce_float.chpp
index ffd075715..ef366caf7 100644
--- a/libnd4j/include/loops/cuda/reduce/reduce_float.chpp
+++ b/libnd4j/include/loops/cuda/reduce/reduce_float.chpp
@@ -129,7 +129,7 @@ __device__ void ReduceFloatFunction<X,Z>::transformCudaXD( void *vx, Nd4jLong *x
         sPartials[threadIdx.x] = OpType::startingValue(x + tadOffsetForBlock);
 
         for (int i = threadIdx.x; i < tadLength; i += blockDim.x) {
-            auto xOffset = tadOffsetForBlock + shape::getIndexOffset(i, tadOnlyShapeInfo, tadLength);
+            auto xOffset = tadOffsetForBlock + shape::getIndexOffset(i, tadOnlyShapeInfo);
             sPartials[threadIdx.x] = OpType::update(sPartials[threadIdx.x], OpType::op(x[xOffset], extraParams), extraParams);
         }
         __syncthreads();
@@ -139,7 +139,7 @@ __device__ void ReduceFloatFunction<X,Z>::transformCudaXD( void *vx, Nd4jLong *x
         __syncthreads();
 
         if (threadIdx.x == 0)
-            z[isPlainOutput ? r : shape::getIndexOffset(r, zShapeInfo, numTads)] = OpType::postProcess(sPartials[threadIdx.x], tadLength, extraParams);
+            z[isPlainOutput ? r : shape::getIndexOffset(r, zShapeInfo)] = OpType::postProcess(sPartials[threadIdx.x], tadLength, extraParams);
     }
 }
 
@@ -179,7 +179,7 @@ __device__ void ReduceFloatFunction<X,Z>::execScalarCuda(void *vx, Nd4jLong *xSh
             sPartials[threadIdx.x] = OpType::update(sPartials[threadIdx.x], OpType::op(x[i * xEws], extraParams), extraParams);
     else
         for (int i = tid; i < len; i += blockDim.x * gridDim.x)
-            sPartials[threadIdx.x] = OpType::update(sPartials[threadIdx.x], OpType::op(x[shape::getIndexOffset(i, xShapeInfo, len)], extraParams), extraParams);
+            sPartials[threadIdx.x] = OpType::update(sPartials[threadIdx.x], OpType::op(x[shape::getIndexOffset(i, xShapeInfo)], extraParams), extraParams);
 
     __syncthreads();
     aggregatePartials<OpType>(sPartials, threadIdx.x, nd4j::math::nd4j_min<int>(blockDim.x, len), extraParams);
diff --git a/libnd4j/include/loops/cuda/reduce/reduce_long.cu b/libnd4j/include/loops/cuda/reduce/reduce_long.cu
index b989298fe..79ab25280 100644
--- a/libnd4j/include/loops/cuda/reduce/reduce_long.cu
+++ b/libnd4j/include/loops/cuda/reduce/reduce_long.cu
@@ -150,7 +150,7 @@ __device__ void ReduceLongFunction<X,Z>::transformCudaXD( void *vx, Nd4jLong *xS
         sPartials[threadIdx.x] = OpType::startingValue(x + tadOffsetForBlock);
 
         for (int i = threadIdx.x; i < tadLength; i += blockDim.x) {
-            auto xOffset = tadOffsetForBlock + shape::getIndexOffset(i, tadOnlyShapeInfo, tadLength);
+            auto xOffset = tadOffsetForBlock + shape::getIndexOffset(i, tadOnlyShapeInfo);
             sPartials[threadIdx.x] = OpType::update(sPartials[threadIdx.x], OpType::op(x[xOffset], extraParams), extraParams);
         }
         __syncthreads();
@@ -160,7 +160,7 @@ __device__ void ReduceLongFunction<X,Z>::transformCudaXD( void *vx, Nd4jLong *xS
         __syncthreads();
 
         if (threadIdx.x == 0)
-            z[isPlainOutput ? r : shape::getIndexOffset(r, zShapeInfo, numTads)] = OpType::postProcess(sPartials[threadIdx.x], tadLength, extraParams);
+            z[isPlainOutput ? r : shape::getIndexOffset(r, zShapeInfo)] = OpType::postProcess(sPartials[threadIdx.x], tadLength, extraParams);
     }
 }
 
@@ -200,7 +200,7 @@ __device__ void ReduceLongFunction<X,Z>::execScalarCuda(void *vx, Nd4jLong *xSha
             sPartials[threadIdx.x] = OpType::update(sPartials[threadIdx.x], OpType::op(x[i * xEws], extraParams), extraParams);
     else
         for (int i = tid; i < len; i += blockDim.x * gridDim.x)
-            sPartials[threadIdx.x] = OpType::update(sPartials[threadIdx.x], OpType::op(x[shape::getIndexOffset(i, xShapeInfo, len)], extraParams), extraParams);
+            sPartials[threadIdx.x] = OpType::update(sPartials[threadIdx.x], OpType::op(x[shape::getIndexOffset(i, xShapeInfo)], extraParams), extraParams);
 
     __syncthreads();
     aggregatePartials<OpType>(sPartials, threadIdx.x, nd4j::math::nd4j_min<int>(blockDim.x, len), extraParams);
diff --git a/libnd4j/include/loops/cuda/reduce/reduce_same.cu b/libnd4j/include/loops/cuda/reduce/reduce_same.cu
index 1c367653c..bcf5bab7f 100644
--- a/libnd4j/include/loops/cuda/reduce/reduce_same.cu
+++ b/libnd4j/include/loops/cuda/reduce/reduce_same.cu
@@ -139,7 +139,7 @@ __device__ void ReduceSameFunction<X>::transformCudaXD( void *vx, Nd4jLong *xSha
         sPartials[threadIdx.x] = OpType::startingValue(x + tadOffsetForBlock);
 
         for (int i = threadIdx.x; i < tadLength; i += blockDim.x) {
-            auto xOffset = tadOffsetForBlock + shape::getIndexOffset(i, tadOnlyShapeInfo, tadLength);
+            auto xOffset = tadOffsetForBlock + shape::getIndexOffset(i, tadOnlyShapeInfo);
             sPartials[threadIdx.x] = OpType::update(sPartials[threadIdx.x], OpType::op(x[xOffset], extraParams), extraParams);
         }
         __syncthreads();
@@ -149,7 +149,7 @@ __device__ void ReduceSameFunction<X>::transformCudaXD( void *vx, Nd4jLong *xSha
         __syncthreads();
 
         if (threadIdx.x == 0)
-            z[isPlainOutput ? r : shape::getIndexOffset(r, zShapeInfo, numTads)] = OpType::postProcess(sPartials[threadIdx.x], tadLength, extraParams);
+            z[isPlainOutput ? r : shape::getIndexOffset(r, zShapeInfo)] = OpType::postProcess(sPartials[threadIdx.x], tadLength, extraParams);
     }
 }
 
@@ -197,7 +197,7 @@ __device__ void ReduceSameFunction<X>::execScalarCuda(void *vx, Nd4jLong *xShape
             sPartials[threadIdx.x] = OpType::update(sPartials[threadIdx.x], OpType::op(x[i * xEws], extraParams), extraParams);
     else
         for (int i = tid; i < len; i += blockDim.x * gridDim.x)
-            sPartials[threadIdx.x] = OpType::update(sPartials[threadIdx.x], OpType::op(x[shape::getIndexOffset(i, xShapeInfo, len)], extraParams), extraParams);
+            sPartials[threadIdx.x] = OpType::update(sPartials[threadIdx.x], OpType::op(x[shape::getIndexOffset(i, xShapeInfo)], extraParams), extraParams);
 
     __syncthreads();
     aggregatePartials<OpType>(sPartials, threadIdx.x, nd4j::math::nd4j_min<int>(blockDim.x, len), extraParams);
diff --git a/libnd4j/include/loops/cuda/reduce3.chpp b/libnd4j/include/loops/cuda/reduce3.chpp
index 01b595da1..fa1ab2e17 100644
--- a/libnd4j/include/loops/cuda/reduce3.chpp
+++ b/libnd4j/include/loops/cuda/reduce3.chpp
@@ -161,8 +161,8 @@ __device__ void Reduce3<X,Z>::execScalarCuda( void *vx, Nd4jLong *xShapeInfo,
         sPartials[threadIdx.x] = OpType::startingValue(x);
 		auto threadCount = gridDim.x * blockDim.x;
         for(Nd4jLong i = tid; i < length; i += threadCount) {
-            auto xOffset = shape::getIndexOffset(i, xShapeInfo, length);
-            auto yOffset = shape::getIndexOffset(i, yShapeInfo, length);
+            auto xOffset = shape::getIndexOffset(i, xShapeInfo);
+            auto yOffset = shape::getIndexOffset(i, yShapeInfo);
             sPartials[threadIdx.x] = OpType::update(sPartials[threadIdx.x], OpType::opAtomic(x[xOffset], y[yOffset], extraZ), extraZ);
         }
     }
@@ -290,7 +290,7 @@ __device__ void Reduce3<X,Z>::transformAll( void *vx, Nd4jLong *xShapeInfo,
     	X *x = dx + xOffsets[r];
 
         if (threadIdx.x < xTadLength && threadIdx.x < maxBlock) {
-        	auto x0 = shape::getIndexOffset(threadIdx.x, xTadShapeInfo, shape::length(xTadShapeInfo));
+        	auto x0 = shape::getIndexOffset(threadIdx.x, xTadShapeInfo);
             tempX[threadIdx.x] = x[x0];
         }
         __syncthreads();
@@ -311,12 +311,12 @@ __device__ void Reduce3<X,Z>::transformAll( void *vx, Nd4jLong *xShapeInfo,
 				// we reset tempX IF we have >1 tiles
                 if (t >= 1 || (limit > 1 && g > 0))
                 	if (threadIdx.x + (t * maxBlock) < xTadLength) {
-                    	auto x0 = shape::getIndexOffset(threadIdx.x + (t * maxBlock), xTadShapeInfo, xTadLength);
+                    	auto x0 = shape::getIndexOffset(threadIdx.x + (t * maxBlock), xTadShapeInfo);
                     	tempX[threadIdx.x] = x[x0];
                  	}
 
                     for (int f = threadIdx.x + (t * maxBlock); f < xTadLength && f < threadIdx.x + ((t + 1) * maxBlock); f += blockDim.x * gridDim.x) {
-                    	auto y0 = shape::getIndexOffset(f, yTadShapeInfo, yTadLength);
+                    	auto y0 = shape::getIndexOffset(f, yTadShapeInfo);
                     	sPartials[threadIdx.x] = OpType::update(sPartials[threadIdx.x], OpType::opAtomic(tempX[threadIdx.x], y[y0], extraZ), extraZ);
                     }
 
@@ -433,8 +433,8 @@ __device__ void Reduce3<X,Z>::transform(void *vx, Nd4jLong *xShapeInfo,
 
             for (int j = threadIdx.x; j < tadLen; j += blockDim.x) {
 
-                Nd4jLong xOffset2 =  xOffset + shape::getIndexOffset(j, tadOnlyShapeInfo, tadLen);
-                Nd4jLong yOffset2 =  yOffset + shape::getIndexOffset(j, yTadOnlyShapeInfo, tadLen);
+                Nd4jLong xOffset2 =  xOffset + shape::getIndexOffset(j, tadOnlyShapeInfo);
+                Nd4jLong yOffset2 =  yOffset + shape::getIndexOffset(j, yTadOnlyShapeInfo);
                 sPartials[threadIdx.x] =  j < blockDim.x ? OpType::opAtomic(x[xOffset2], y[yOffset2], extraZ) : OpType::update(sPartials[threadIdx.x], OpType::opAtomic(x[xOffset2], y[yOffset2], extraZ), extraZ);
 
             }
diff --git a/libnd4j/include/loops/cuda/scalar.chpp b/libnd4j/include/loops/cuda/scalar.chpp
index 503e5c97a..7277138f9 100644
--- a/libnd4j/include/loops/cuda/scalar.chpp
+++ b/libnd4j/include/loops/cuda/scalar.chpp
@@ -33,7 +33,7 @@ using namespace simdOps;
 ////////////////////////////////////////////////////////////////////////////////
 template <typename X, typename Y, typename Z, typename OpType>
 __global__ static void scalarSimpleShaped(void* vx, void *vscalar, Nd4jLong *xShapeInfo, void *vparams, void *vz, Nd4jLong *zShapeInfo, int *allocationBuffer) {
-    
+
     auto scalar = reinterpret_cast<Y*>(vscalar)[0];
     auto x      = reinterpret_cast<X*>(vx);
     auto params = reinterpret_cast<Z*>(vparams);
@@ -61,10 +61,10 @@ __global__ static void scalarSimpleShaped(void* vx, void *vscalar, Nd4jLong *xSh
         }
     } else {
         for (Nd4jLong i = tid; i < length; i += totalThreads) {
-            z[shape::getIndexOffset(i, zShapeInfo, length)] = OpType::op(x[shape::getIndexOffset(i, xShapeInfo, length)], scalar, params);
+            z[shape::getIndexOffset(i, zShapeInfo)] = OpType::op(x[shape::getIndexOffset(i, xShapeInfo)], scalar, params);
         }
     }
-    
+
 }
 
 ////////////////////////////////////////////////////////////////////////////////
@@ -76,7 +76,7 @@ __global__ static void scalarAlongDimension(void *vx, Nd4jLong *xShapeInfo,
                                           int *dimension, int dimensionLength,
                                           Nd4jLong *tadShapeInfo,  Nd4jLong *tadOffsets,
                                           Nd4jLong *tadShapeInfoZ, Nd4jLong *tadOffsetsZ) {
-        
+
     auto x = reinterpret_cast<X*>(vx);
     auto extraParams = reinterpret_cast<Z*>(vextraParams);
     auto z = reinterpret_cast<Z*>(vz);
@@ -114,7 +114,7 @@ __global__ static void scalarAlongDimension(void *vx, Nd4jLong *xShapeInfo,
             auto s = scalars[r];
 
             for (int f = threadIdx.x; f < tadLength; f += blockDim.x)
-                oZ[shape::getIndexOffset(f, tadShapeInfoZ, tadLength)] = OpType::op(oX[shape::getIndexOffset(f, tadShapeInfo, tadLength)], s, extraParams);
+                oZ[shape::getIndexOffset(f, tadShapeInfoZ)] = OpType::op(oX[shape::getIndexOffset(f, tadShapeInfo)], s, extraParams);
         }
     }
 }
@@ -127,7 +127,7 @@ namespace scalar    {
 template<typename X, typename Y, typename Z>
 template<typename OpType>
 void _CUDA_H ScalarTransform<X,Y,Z>::intermediateShaped(dim3& launchDims, cudaStream_t *stream, void *vx, Nd4jLong *xShapeInfo, Nd4jLong *hxShapeInfo, void *vz, Nd4jLong *zShapeInfo, Nd4jLong *hzShapeInfo, void* vscalar, void *vextraParams, int *allocPointer){
-    
+
     auto xEws = shape::elementWiseStride(hxShapeInfo);
     auto xOrder = shape::order(hxShapeInfo);
 
diff --git a/libnd4j/include/loops/cuda/scalar_bool.cu b/libnd4j/include/loops/cuda/scalar_bool.cu
index c6563c9ef..37939b9b9 100644
--- a/libnd4j/include/loops/cuda/scalar_bool.cu
+++ b/libnd4j/include/loops/cuda/scalar_bool.cu
@@ -36,7 +36,7 @@ __global__ void scalarAlongDimension(void *x, Nd4jLong *xShapeInfo,
                                     int *dimension, int dimensionLength,
                                     Nd4jLong *tadShapeInfo, Nd4jLong *tadOffsets,
                                     Nd4jLong *tadShapeInfoZ, Nd4jLong *tadOffsetsZ) {
-        
+
     functions::scalar::ScalarBoolTransform<X,Z>::template transformCuda<OpType>(x, xShapeInfo, extraParams, z, zShapeInfo, scalars, dimension, dimensionLength, tadShapeInfo, tadOffsets, tadShapeInfoZ, tadOffsetsZ);
 }
 
@@ -60,10 +60,10 @@ namespace scalar    {
 ////////////////////////////////////////////////////////////////////////
 template<typename X, typename Z>
 template<typename OpType>
-__device__ void  ScalarBoolTransform<X, Z>::transformCuda(void* vscalar, 
-                                                        void *vy, Nd4jLong *yShapeInfo, 
-                                                        void *vparams, 
-                                                        void *vz, Nd4jLong *zShapeInfo, 
+__device__ void  ScalarBoolTransform<X, Z>::transformCuda(void* vscalar,
+                                                        void *vy, Nd4jLong *yShapeInfo,
+                                                        void *vparams,
+                                                        void *vz, Nd4jLong *zShapeInfo,
                                                         int *allocationBuffer) {
     auto scalar = reinterpret_cast<X*>(vscalar)[0];
     auto y      = reinterpret_cast<X*>(vy);
@@ -73,8 +73,8 @@ __device__ void  ScalarBoolTransform<X, Z>::transformCuda(void* vscalar,
     auto yRank   = shape::rank(yShapeInfo);
     auto yEWS    = shape::elementWiseStride(yShapeInfo);
     auto yShape  = shape::shapeOf(yShapeInfo);
-    auto yStride = shape::stride(yShapeInfo);        
-    
+    auto yStride = shape::stride(yShapeInfo);
+
     auto zRank   = shape::rank(zShapeInfo);
     auto zEWS    = shape::elementWiseStride(zShapeInfo);
     auto zShape  = shape::shapeOf(zShapeInfo);
@@ -89,22 +89,22 @@ __device__ void  ScalarBoolTransform<X, Z>::transformCuda(void* vscalar,
     __syncthreads();
 
     if(yEWS >= 1 && zEWS >= 1 && shape::order(yShapeInfo) == shape::order(zShapeInfo)) {
-            transformCuda<OpType>(len, vscalar, vy, yEWS, vparams, vz, zEWS, allocationBuffer);    
+            transformCuda<OpType>(len, vscalar, vy, yEWS, vparams, vz, zEWS, allocationBuffer);
     }
     else {
-        for (Nd4jLong i = tid; i < len; i+= totalThreads)                        
-            z[shape::getIndexOffset(i, zShapeInfo, len)] = OpType::op(y[shape::getIndexOffset(i, yShapeInfo, len)], scalar, params);
+        for (Nd4jLong i = tid; i < len; i+= totalThreads)
+            z[shape::getIndexOffset(i, zShapeInfo)] = OpType::op(y[shape::getIndexOffset(i, yShapeInfo)], scalar, params);
     }
 }
 
 ////////////////////////////////////////////////////////////////////////
 template<typename X, typename Z>
 template<typename OpType>
-__device__ void  ScalarBoolTransform<X, Z>::transformCuda(Nd4jLong len, 
-                                                          void* vx, 
-                                                          void *vy, Nd4jLong yEWS, 
-                                                          void *vparams, 
-                                                          void *vz, Nd4jLong zEWS, 
+__device__ void  ScalarBoolTransform<X, Z>::transformCuda(Nd4jLong len,
+                                                          void* vx,
+                                                          void *vy, Nd4jLong yEWS,
+                                                          void *vparams,
+                                                          void *vz, Nd4jLong zEWS,
                                                           int *allocationBuffer) {
 
     auto x = reinterpret_cast<X*>(vx)[0];
@@ -130,18 +130,18 @@ __device__ void  ScalarBoolTransform<X, Z>::transformCuda(Nd4jLong len,
 ////////////////////////////////////////////////////////////////////////
 template<typename X, typename Z>
 template<typename OpType>
-__device__ void  ScalarBoolTransform<X, Z>::transformCuda(void *vx, Nd4jLong *xShapeInfo, 
-                                                        void *vextraParams, 
-                                                        void *vz, Nd4jLong *zShapeInfo, 
-                                                        void *vscalars, 
-                                                        int *dimension, int dimensionLength, 
-                                                        Nd4jLong *tadShapeInfo, Nd4jLong *tadOffsets, 
+__device__ void  ScalarBoolTransform<X, Z>::transformCuda(void *vx, Nd4jLong *xShapeInfo,
+                                                        void *vextraParams,
+                                                        void *vz, Nd4jLong *zShapeInfo,
+                                                        void *vscalars,
+                                                        int *dimension, int dimensionLength,
+                                                        Nd4jLong *tadShapeInfo, Nd4jLong *tadOffsets,
                                                         Nd4jLong *tadShapeInfoZ, Nd4jLong *tadOffsetsZ) {
     auto x = reinterpret_cast<X*>(vx);
     auto scalars = reinterpret_cast<X*>(vscalars);
     auto z = reinterpret_cast<Z*>(vz);
     auto extraParams = reinterpret_cast<X*>(vextraParams);
-    
+
     if (tadShapeInfoZ == nullptr) {
         tadShapeInfoZ = tadShapeInfo;
         tadOffsetsZ = tadOffsets;
@@ -174,7 +174,7 @@ __device__ void  ScalarBoolTransform<X, Z>::transformCuda(void *vx, Nd4jLong *xS
             auto s = scalars[r];
 
             for (int f = threadIdx.x; f < tadLength; f += blockDim.x)
-                oZ[shape::getIndexOffset(f, tadShapeInfoZ, tadLength)] = OpType::op(oX[shape::getIndexOffset(f, tadShapeInfo, tadLength)], s, extraParams);
+                oZ[shape::getIndexOffset(f, tadShapeInfoZ)] = OpType::op(oX[shape::getIndexOffset(f, tadShapeInfo)], s, extraParams);
         }
     }
 }
@@ -184,12 +184,12 @@ __device__ void  ScalarBoolTransform<X, Z>::transformCuda(void *vx, Nd4jLong *xS
 template<typename X, typename Z>
 template <typename OpType>
 _CUDA_H void ScalarBoolTransform<X, Z>::intermediateAlongDimension(dim3& launchDims, cudaStream_t *stream,
-                                                                void *x, Nd4jLong *xShapeInfo, 
-                                                                void *z, Nd4jLong *zShapeInfo, 
-                                                                void *scalars, 
-                                                                void *extraParams, 
-                                                                int *dimension, int dimensionLength, 
-                                                                Nd4jLong *tadShapeInfo, Nd4jLong *tadOffsets, 
+                                                                void *x, Nd4jLong *xShapeInfo,
+                                                                void *z, Nd4jLong *zShapeInfo,
+                                                                void *scalars,
+                                                                void *extraParams,
+                                                                int *dimension, int dimensionLength,
+                                                                Nd4jLong *tadShapeInfo, Nd4jLong *tadOffsets,
                                                                 Nd4jLong *tadShapeInfoZ, Nd4jLong *tadOffsetsZ) {
 
     scalarAlongDimension<X, Z, OpType><<<launchDims.x, launchDims.y, launchDims.z, *stream>>>(x, xShapeInfo, extraParams, z, zShapeInfo, scalars, dimension, dimensionLength, tadShapeInfo, tadOffsets, tadShapeInfoZ, tadOffsetsZ);
@@ -200,11 +200,11 @@ _CUDA_H void ScalarBoolTransform<X, Z>::intermediateAlongDimension(dim3& launchD
 template<typename X, typename Z>
 template<typename OpType>
 void _CUDA_H ScalarBoolTransform<X,Z>::intermediateShaped(dim3& launchDims, cudaStream_t *stream,
-                                                            void *vx, Nd4jLong *xShapeInfo, 
-                                                            void *vz, Nd4jLong *zShapeInfo, 
-                                                            void* vscalar, 
+                                                            void *vx, Nd4jLong *xShapeInfo,
+                                                            void *vz, Nd4jLong *zShapeInfo,
+                                                            void* vscalar,
                                                             void *vextraParams, int *allocPointer){
-    
+
     scalarSimpleShaped<X, Z, OpType><<<launchDims.x, launchDims.y, launchDims.z, *stream>>>(vx, vscalar, xShapeInfo, vextraParams, vz, zShapeInfo, allocPointer);
     nd4j::DebugHelper::checkErrorCode(stream, "scalarSimpleShaped(...) failed");
 }
@@ -212,10 +212,10 @@ void _CUDA_H ScalarBoolTransform<X,Z>::intermediateShaped(dim3& launchDims, cuda
 ////////////////////////////////////////////////////////////////////////
 template<typename X, typename Y>
 void ScalarBoolTransform<X,Y>::executeCudaShaped(dim3& launchDims, cudaStream_t *stream,
-                                                int opNum, 
-                                                void *vx, Nd4jLong *xShapeInfo, 
-                                                void *vz, Nd4jLong *zShapeInfo, 
-                                                void* vscalar, 
+                                                int opNum,
+                                                void *vx, Nd4jLong *xShapeInfo,
+                                                void *vz, Nd4jLong *zShapeInfo,
+                                                void* vscalar,
                                                 void *vextraParams) {
 
     if (nd4j::Environment::getInstance()->isDebugAndVerbose())
diff --git a/libnd4j/include/loops/cuda/scalar_int.cu b/libnd4j/include/loops/cuda/scalar_int.cu
index 48f141525..44c73fcb4 100644
--- a/libnd4j/include/loops/cuda/scalar_int.cu
+++ b/libnd4j/include/loops/cuda/scalar_int.cu
@@ -36,7 +36,7 @@ __global__ void scalarAlongDimension(void *x, Nd4jLong *xShapeInfo,
                                     int *dimension, int dimensionLength,
                                     Nd4jLong *tadShapeInfo, Nd4jLong *tadOffsets,
                                     Nd4jLong *tadShapeInfoZ, Nd4jLong *tadOffsetsZ) {
-        
+
     functions::scalar::ScalarIntTransform<X>::template transformCuda<OpType>(x, xShapeInfo, extraParams, z, zShapeInfo, scalars, dimension, dimensionLength, tadShapeInfo, tadOffsets, tadShapeInfoZ, tadOffsetsZ);
 }
 
@@ -60,10 +60,10 @@ namespace scalar    {
 ////////////////////////////////////////////////////////////////////////
 template<typename X>
 template<typename OpType>
-__device__ void  ScalarIntTransform<X>::transformCuda(void* vscalar, 
-                                                        void *vy, Nd4jLong *yShapeInfo, 
-                                                        void *vparams, 
-                                                        void *vz, Nd4jLong *zShapeInfo, 
+__device__ void  ScalarIntTransform<X>::transformCuda(void* vscalar,
+                                                        void *vy, Nd4jLong *yShapeInfo,
+                                                        void *vparams,
+                                                        void *vz, Nd4jLong *zShapeInfo,
                                                         int *allocationBuffer) {
     auto scalar = reinterpret_cast<X*>(vscalar)[0];
     auto y      = reinterpret_cast<X*>(vy);
@@ -73,8 +73,8 @@ __device__ void  ScalarIntTransform<X>::transformCuda(void* vscalar,
     auto yRank   = shape::rank(yShapeInfo);
     auto yEWS    = shape::elementWiseStride(yShapeInfo);
     auto yShape  = shape::shapeOf(yShapeInfo);
-    auto yStride = shape::stride(yShapeInfo);        
-    
+    auto yStride = shape::stride(yShapeInfo);
+
     auto zRank   = shape::rank(zShapeInfo);
     auto zEWS    = shape::elementWiseStride(zShapeInfo);
     auto zShape  = shape::shapeOf(zShapeInfo);
@@ -89,11 +89,11 @@ __device__ void  ScalarIntTransform<X>::transformCuda(void* vscalar,
     __syncthreads();
 
     if(yEWS >= 1 && zEWS >= 1 && shape::order(yShapeInfo) == shape::order(zShapeInfo)) {
-            transformCuda<OpType>(len, vscalar, vy, yEWS, vparams, vz, zEWS, allocationBuffer);    
+            transformCuda<OpType>(len, vscalar, vy, yEWS, vparams, vz, zEWS, allocationBuffer);
     }
     else {
-        for (Nd4jLong i = tid; i < len; i+= totalThreads)                        
-            z[shape::getIndexOffset(i, zShapeInfo, len)] = OpType::op(y[shape::getIndexOffset(i, yShapeInfo, len)], scalar, params);
+        for (Nd4jLong i = tid; i < len; i+= totalThreads)
+            z[shape::getIndexOffset(i, zShapeInfo)] = OpType::op(y[shape::getIndexOffset(i, yShapeInfo)], scalar, params);
     }
 }
 
@@ -101,10 +101,10 @@ __device__ void  ScalarIntTransform<X>::transformCuda(void* vscalar,
 template<typename X>
 template<typename OpType>
 __device__ void  ScalarIntTransform<X>::transformCuda(Nd4jLong len,
-                                                          void* vx, 
-                                                          void *vy, Nd4jLong yEWS, 
-                                                          void *vparams, 
-                                                          void *vz, Nd4jLong zEWS, 
+                                                          void* vx,
+                                                          void *vy, Nd4jLong yEWS,
+                                                          void *vparams,
+                                                          void *vz, Nd4jLong zEWS,
                                                           int *allocationBuffer) {
 
     auto x = reinterpret_cast<X*>(vx)[0];
@@ -131,17 +131,17 @@ __device__ void  ScalarIntTransform<X>::transformCuda(Nd4jLong len,
 template<typename X>
 template<typename OpType>
 __device__ void  ScalarIntTransform<X>::transformCuda(void *vx, Nd4jLong *xShapeInfo,
-                                                        void *vextraParams, 
-                                                        void *vz, Nd4jLong *zShapeInfo, 
-                                                        void *vscalars, 
-                                                        int *dimension, int dimensionLength, 
-                                                        Nd4jLong *tadShapeInfo, Nd4jLong *tadOffsets, 
+                                                        void *vextraParams,
+                                                        void *vz, Nd4jLong *zShapeInfo,
+                                                        void *vscalars,
+                                                        int *dimension, int dimensionLength,
+                                                        Nd4jLong *tadShapeInfo, Nd4jLong *tadOffsets,
                                                         Nd4jLong *tadShapeInfoZ, Nd4jLong *tadOffsetsZ) {
     auto x = reinterpret_cast<X*>(vx);
     auto scalars = reinterpret_cast<X*>(vscalars);
     auto z = reinterpret_cast<X*>(vz);
     auto extraParams = reinterpret_cast<X*>(vextraParams);
-    
+
     if (tadShapeInfoZ == nullptr) {
         tadShapeInfoZ = tadShapeInfo;
         tadOffsetsZ = tadOffsets;
@@ -174,7 +174,7 @@ __device__ void  ScalarIntTransform<X>::transformCuda(void *vx, Nd4jLong *xShape
             auto s = scalars[r];
 
             for (int f = threadIdx.x; f < tadLength; f += blockDim.x)
-                oZ[shape::getIndexOffset(f, tadShapeInfoZ, tadLength)] = OpType::op(oX[shape::getIndexOffset(f, tadShapeInfo, tadLength)], s, extraParams);
+                oZ[shape::getIndexOffset(f, tadShapeInfoZ)] = OpType::op(oX[shape::getIndexOffset(f, tadShapeInfo)], s, extraParams);
         }
     }
 }
@@ -184,12 +184,12 @@ __device__ void  ScalarIntTransform<X>::transformCuda(void *vx, Nd4jLong *xShape
 template<typename X>
 template <typename OpType>
 _CUDA_H void ScalarIntTransform<X>::intermediateAlongDimension(dim3& launchDims, cudaStream_t *stream,
-                                                                void *x, Nd4jLong *xShapeInfo, 
-                                                                void *z, Nd4jLong *zShapeInfo, 
-                                                                void *scalars, 
-                                                                void *extraParams, 
-                                                                int *dimension, int dimensionLength, 
-                                                                Nd4jLong *tadShapeInfo, Nd4jLong *tadOffsets, 
+                                                                void *x, Nd4jLong *xShapeInfo,
+                                                                void *z, Nd4jLong *zShapeInfo,
+                                                                void *scalars,
+                                                                void *extraParams,
+                                                                int *dimension, int dimensionLength,
+                                                                Nd4jLong *tadShapeInfo, Nd4jLong *tadOffsets,
                                                                 Nd4jLong *tadShapeInfoZ, Nd4jLong *tadOffsetsZ) {
 
     scalarAlongDimension<X, OpType><<<launchDims.x, launchDims.y, launchDims.z, *stream>>>(x, xShapeInfo, extraParams, z, zShapeInfo, scalars, dimension, dimensionLength, tadShapeInfo, tadOffsets, tadShapeInfoZ, tadOffsetsZ);
@@ -199,21 +199,21 @@ _CUDA_H void ScalarIntTransform<X>::intermediateAlongDimension(dim3& launchDims,
 template<typename X>
 template<typename OpType>
 void _CUDA_H ScalarIntTransform<X>::intermediateShaped(dim3& launchDims, cudaStream_t *stream,
-                                                            void *vx, Nd4jLong *xShapeInfo, 
-                                                            void *vz, Nd4jLong *zShapeInfo, 
-                                                            void* vscalar, 
+                                                            void *vx, Nd4jLong *xShapeInfo,
+                                                            void *vz, Nd4jLong *zShapeInfo,
+                                                            void* vscalar,
                                                             void *vextraParams, int *allocPointer){
-    
+
     scalarSimpleShaped<X, OpType><<<launchDims.x, launchDims.y, launchDims.z, *stream>>>(vx, vscalar, xShapeInfo, vextraParams, vz, zShapeInfo, allocPointer);
 }
 
 ////////////////////////////////////////////////////////////////////////
 template<typename X>
 void ScalarIntTransform<X>::executeCudaShaped(dim3& launchDims, cudaStream_t *stream,
-                                                int opNum, 
-                                                void *vx, Nd4jLong *xShapeInfo, 
-                                                void *vz, Nd4jLong *zShapeInfo, 
-                                                void* vscalar, 
+                                                int opNum,
+                                                void *vx, Nd4jLong *xShapeInfo,
+                                                void *vz, Nd4jLong *zShapeInfo,
+                                                void* vscalar,
                                                 void *vextraParams) {
 
     if (nd4j::Environment::getInstance()->isDebugAndVerbose())
diff --git a/libnd4j/include/loops/cuda/specials/bitonicArbitraryStep.cu b/libnd4j/include/loops/cuda/specials/bitonicArbitraryStep.cu
index 8ee950c25..13ad1d5b4 100644
--- a/libnd4j/include/loops/cuda/specials/bitonicArbitraryStep.cu
+++ b/libnd4j/include/loops/cuda/specials/bitonicArbitraryStep.cu
@@ -80,8 +80,8 @@ __global__ void bitonicArbitraryStepKernelKey(void *vx, Nd4jLong *xShapeInfo, vo
             int it = (reverse) ? i + j + half : i + window - j - 1;
             int ij = i+j;
             if (it < length && ij < length ) {
-                int posIT = shape::getIndexOffset(it, xShapeInfo, xLength);
-                int posIJ = shape::getIndexOffset(ij, xShapeInfo, xLength);
+                int posIT = shape::getIndexOffset(it, xShapeInfo);
+                int posIJ = shape::getIndexOffset(ij, xShapeInfo);
 
                 X v0 = x[posIJ];
                 X v1 = x[posIT];
@@ -160,8 +160,8 @@ __global__ void execBitonicArbitraryStepKernel(void *vx, Nd4jLong *xShapeInfo, i
             int it = (reverse) ? i + j + half : i + window - j - 1;
             int ij = i+j;
             if (it < length && ij < length ) {
-                int posIT = shape::getIndexOffset(it, xShapeInfo, xLength);
-                int posIJ = shape::getIndexOffset(ij, xShapeInfo, xLength);
+                int posIT = shape::getIndexOffset(it, xShapeInfo);
+                int posIJ = shape::getIndexOffset(ij, xShapeInfo);
 
                 shmem[threadIdx.x] = x[posIJ];
                 shmem[threadIdx.x + blockDim.x] = x[posIT];
diff --git a/libnd4j/include/loops/cuda/specials/bitonicSortStep.cu b/libnd4j/include/loops/cuda/specials/bitonicSortStep.cu
index d9b2ec74c..6bd1e8a33 100644
--- a/libnd4j/include/loops/cuda/specials/bitonicSortStep.cu
+++ b/libnd4j/include/loops/cuda/specials/bitonicSortStep.cu
@@ -46,8 +46,8 @@ __global__ void bitonicSortStepKernelKey(void *vx, Nd4jLong *xShapeInfo, void *v
 
     /* The threads with the lowest ids sort the array. */
     if ((ixj)>i) {
-        int posI = shape::getIndexOffset(i, xShapeInfo, xLength);
-        int posIXJ = shape::getIndexOffset(ixj, xShapeInfo, xLength);
+        int posI = shape::getIndexOffset(i, xShapeInfo);
+        int posIXJ = shape::getIndexOffset(ixj, xShapeInfo);
 
         if ((i&k)==0) {
             /* Sort ascending */
@@ -100,8 +100,8 @@ __global__ void bitonicSortStepKernel(void *vx, Nd4jLong *xShapeInfo, int j, int
 
     /* The threads with the lowest ids sort the array. */
     if ((ixj)>i) {
-        int posI = shape::getIndexOffset(i, xShapeInfo, xLength);
-        int posIXJ = shape::getIndexOffset(ixj, xShapeInfo, xLength);
+        int posI = shape::getIndexOffset(i, xShapeInfo);
+        int posIXJ = shape::getIndexOffset(ixj, xShapeInfo);
 
         if ((i&k)==0) {
             /* Sort ascending */
diff --git a/libnd4j/include/loops/cuda/specials/concatKernel.cu b/libnd4j/include/loops/cuda/specials/concatKernel.cu
index 5d788c4d1..b6ba2f00e 100644
--- a/libnd4j/include/loops/cuda/specials/concatKernel.cu
+++ b/libnd4j/include/loops/cuda/specials/concatKernel.cu
@@ -139,19 +139,19 @@ namespace nd4j {
 
                     Nd4jLong sub[MAX_RANK];
 
-                    shape::index2coords(shape::rank(zTadShape),shape::shapeOf(zTadShape), arrOffset, sub, shape::order(zTadShape));
-                    
-                    Nd4jLong baseOffset = shape::getOffset(0,shape::shapeOf(zTadShape),shape::stride(zTadShape), sub, shape::rank(zTadShape));
+                    shape::index2coords(arrOffset, zTadShape, sub);
+
+                    Nd4jLong baseOffset = shape::getOffset(zTadShape, sub);
 
                     resultTAD += baseOffset;
 
                     auto yRank = shape::rank(currentTad);
                     auto tadRank = shape::rank(zTadShape);
 
-                    shape::index2coords(yRank, shape::shapeOf(currentTad), 0, sub);
+                    shape::index2coords(0, currentTad, sub);
 
-                    auto yOffset = shape::getOffset(0, shape::shapeOf(currentTad), shape::stride(currentTad), sub, yRank);
-                    resultOffset = shape::getOffset(0, shape::shapeOf(zTadShape), shape::stride(zTadShape), sub, tadRank);
+                    auto yOffset = shape::getOffset(currentTad, sub);
+                    resultOffset = shape::getOffset(zTadShape, sub);
 
                     resultTAD[resultOffset] =  dataTAD[yOffset];
                 }
@@ -168,8 +168,8 @@ namespace nd4j {
 
                     Nd4jLong sub[MAX_RANK];
 
-                    shape::index2coords(shape::rank(zTadShape),shape::shapeOf(zTadShape), arrOffset, sub);
-                    Nd4jLong baseOffset = shape::getOffset(0,shape::shapeOf(zTadShape),shape::stride(zTadShape), sub, shape::rank(zTadShape));
+                    shape::index2coords(arrOffset, zTadShape, sub);
+                    Nd4jLong baseOffset = shape::getOffset(zTadShape, sub);
 
                     resultTAD += baseOffset;
 
@@ -203,8 +203,8 @@ namespace nd4j {
                                 auto yRank = shape::rank(currentTad);
 
                                 for (int i = threadIdx.x; i < yLength; i+= blockDim.x) {
-                                    shape::index2coords(yRank, shape::shapeOf(currentTad), i, yIdx);
-                                    auto yOffset = shape::getOffset(0, shape::shapeOf(currentTad), shape::stride(currentTad), yIdx, yRank);
+                                    shape::index2coords(i, currentTad, yIdx);
+                                    auto yOffset = shape::getOffset(currentTad, yIdx);
 
                                     resultTAD[baseIdx + i * tadEWS] =  dataTAD[yOffset];
                                 }
@@ -220,11 +220,11 @@ namespace nd4j {
                             auto tadRank = shape::rank(zTadShape);
 
                             for (int i = threadIdx.x; i < yLength; i+= blockDim.x) {
-                                shape::index2coords(yRank, shape::shapeOf(currentTad), i, yIdx);
-                                shape::index2coords(tadRank, shape::shapeOf(zTadShape), i, zIdx);
+                                shape::index2coords(i, currentTad, yIdx);
+                                shape::index2coords(i, zTadShape, zIdx);
 
-                                auto yOffset = shape::getOffset(0, shape::shapeOf(currentTad), shape::stride(currentTad), yIdx, yRank);
-                                auto resultOffset = shape::getOffset(0, shape::shapeOf(zTadShape), shape::stride(zTadShape), zIdx, tadRank);
+                                auto yOffset = shape::getOffset(currentTad, yIdx);
+                                auto resultOffset = shape::getOffset(zTadShape, zIdx);
 
                                 resultTAD[resultOffset] =  dataTAD[yOffset];
                             }
diff --git a/libnd4j/include/loops/cuda/specials/fillDimensionalIsMax.cu b/libnd4j/include/loops/cuda/specials/fillDimensionalIsMax.cu
index 70da24715..e39ff6bec 100644
--- a/libnd4j/include/loops/cuda/specials/fillDimensionalIsMax.cu
+++ b/libnd4j/include/loops/cuda/specials/fillDimensionalIsMax.cu
@@ -53,7 +53,7 @@ namespace nd4j {
             if (dimensionLength > 1 || tadEWS < 1) {
 
                 for (Nd4jLong e = threadIdx.x; e < tadLength; e += blockDim.x) {
-                    auto xOffset = tadOffsetForBlock + shape::getIndexOffset(e, tadOnlyShapeInfo, tadLength);
+                    auto xOffset = tadOffsetForBlock + shape::getIndexOffset(e, tadOnlyShapeInfo);
                     dZ[xOffset] = (e == highestElement ? (T) 1 : (T) 0);
                 }
             } else {
diff --git a/libnd4j/include/loops/cuda/specials/fillIsMax.cu b/libnd4j/include/loops/cuda/specials/fillIsMax.cu
index 0851968ba..c9ed51d28 100644
--- a/libnd4j/include/loops/cuda/specials/fillIsMax.cu
+++ b/libnd4j/include/loops/cuda/specials/fillIsMax.cu
@@ -30,7 +30,7 @@ namespace nd4j {
         int tid = blockIdx.x * blockDim.x + threadIdx.x;
 
         for (Nd4jLong i = tid; i < length; i += blockDim.x * gridDim.x)
-            dz[shape::getIndexOffset(i, xShapeInfo, length)] = (i == idx ? (T) 1 : (T) 0);
+            dz[shape::getIndexOffset(i, xShapeInfo)] = (i == idx ? (T) 1 : (T) 0);
     }
 
 ////////////////////////////////////////////////////////////////////////
diff --git a/libnd4j/include/loops/cuda/specials/flatten.cu b/libnd4j/include/loops/cuda/specials/flatten.cu
index b820acae9..faec2ec90 100644
--- a/libnd4j/include/loops/cuda/specials/flatten.cu
+++ b/libnd4j/include/loops/cuda/specials/flatten.cu
@@ -20,6 +20,7 @@
 //
 
 #include <loops/special_kernels.h>
+#include <ops/declarable/helpers/flatten.h>
 
 namespace nd4j {
 
@@ -34,34 +35,26 @@ __global__ void flattenKernel(
 
     auto z = reinterpret_cast<T*>(vz);
     auto y = reinterpret_cast<T*>(vy);
-    
+
     __shared__ Nd4jLong lenY, yOrder, zEWS, yEWS;
 
-    if (threadIdx.x == 0) {                
-        
+    if (threadIdx.x == 0) {
+
         yEWS = shape::elementWiseStride(yShapeInfo);
         zEWS = shape::elementWiseStride(zShapeInfo);
         lenY = shape::length(yShapeInfo);
     }
     __syncthreads();
 
-    Nd4jLong tid = blockIdx.x * blockDim.x + threadIdx.x;        
-        
-    if (zEWS >= 1 && yEWS >= 1 && yOrder == order) {
- 
-        for (int i = tid; i < lenY; i += gridDim.x * blockDim.x)
-            z[i * zEWS + dOffset] = y[i * yEWS];
-    } 
-    else {
-        
-        for(auto i = tid; i < lenY; i += gridDim.x * blockDim.x)
-            z[i * zEWS + dOffset] = y[shape::getIndexOrderOffset(i, yShapeInfo, lenY, order)];
-    } 
+    Nd4jLong tid = blockIdx.x * blockDim.x + threadIdx.x;
+
+    for(auto i = tid; i < lenY; i += gridDim.x * blockDim.x)
+        z[i * zEWS + dOffset] = y[ops::helpers::getIndexOffsetOrdered(i, yShapeInfo, order)];
 }
 
 ////////////////////////////////////////////////////////////////////////
 template <typename T>
-__host__ void flattenKernelGeneric(dim3& launchDims, cudaStream_t *stream, 
+__host__ void flattenKernelGeneric(dim3& launchDims, cudaStream_t *stream,
                             Nd4jPointer *extraPointers,
                             int dOffset,
                             char order,
diff --git a/libnd4j/include/loops/cuda/specials/oesTad.cu b/libnd4j/include/loops/cuda/specials/oesTad.cu
index 8cc6c0766..9f41ffbb9 100644
--- a/libnd4j/include/loops/cuda/specials/oesTad.cu
+++ b/libnd4j/include/loops/cuda/specials/oesTad.cu
@@ -54,8 +54,8 @@ __global__ void execOesTadKernelKey(void *vx, Nd4jLong *xShapeInfo,
                 for (int tid = threadIdx.x; tid < xTadLength; tid += blockDim.x) {
                     auto top = 2 * tid + 1;
                     if (top < xTadLength) {
-                        auto t0 = shape::getIndexOffset(top - 1, tadShapeInfo, xTadLength);
-                        auto t1 = shape::getIndexOffset(top, tadShapeInfo, xTadLength);
+                        auto t0 = shape::getIndexOffset(top - 1, tadShapeInfo);
+                        auto t1 = shape::getIndexOffset(top, tadShapeInfo);
 
                         if (!descending == (dx[t0] > dx[t1])) {
                             X dt0 = dx[t0];
@@ -72,8 +72,8 @@ __global__ void execOesTadKernelKey(void *vx, Nd4jLong *xShapeInfo,
                 for (int tid = threadIdx.x; tid < xTadLength; tid += blockDim.x) {
                     auto top = 2 * tid + 2;
                     if (top < xTadLength) {
-                        auto t0 = shape::getIndexOffset(top - 1, tadShapeInfo, xTadLength);
-                        auto t1 = shape::getIndexOffset(top, tadShapeInfo, xTadLength);
+                        auto t0 = shape::getIndexOffset(top - 1, tadShapeInfo);
+                        auto t1 = shape::getIndexOffset(top, tadShapeInfo);
 
                         if (!descending == (dx[t0] > dx[t1])) {
                             X dt0 = dx[t0];
@@ -126,7 +126,7 @@ __global__ void execOesTadKernel(void *vx, Nd4jLong *xShapeInfo,
         int iterations = xTadLength;
         if (cached) {
             for (int tid = threadIdx.x; tid < xTadLength; tid += blockDim.x) {
-                auto t0 = shape::getIndexOffset(tid, tadShapeInfo, xTadLength);
+                auto t0 = shape::getIndexOffset(tid, tadShapeInfo);
                 shmem[tid] = dx[t0];
             }
 
@@ -140,8 +140,8 @@ __global__ void execOesTadKernel(void *vx, Nd4jLong *xShapeInfo,
                 for (int tid = threadIdx.x; tid < xTadLength; tid += blockDim.x) {
                     auto top = 2 * tid + 1;
                     if (top < xTadLength) {
-                        auto t0 = cached ? top - 1 : shape::getIndexOffset(top - 1, tadShapeInfo, xTadLength);
-                        auto t1 = cached ? top : shape::getIndexOffset(top, tadShapeInfo, xTadLength);
+                        auto t0 = cached ? top - 1 : shape::getIndexOffset(top - 1, tadShapeInfo);
+                        auto t1 = cached ? top : shape::getIndexOffset(top, tadShapeInfo);
 
                         if (!descending == (dx[t0] > dx[t1])) {
                             T dt0 = dx[t0];
@@ -154,8 +154,8 @@ __global__ void execOesTadKernel(void *vx, Nd4jLong *xShapeInfo,
                 for (int tid = threadIdx.x; tid < xTadLength; tid += blockDim.x) {
                     auto top = 2 * tid + 2;
                     if (top < xTadLength) {
-                        auto t0 = cached ? top - 1 : shape::getIndexOffset(top - 1, tadShapeInfo, xTadLength);
-                        auto t1 = cached ? top : shape::getIndexOffset(top, tadShapeInfo, xTadLength);
+                        auto t0 = cached ? top - 1 : shape::getIndexOffset(top - 1, tadShapeInfo);
+                        auto t1 = cached ? top : shape::getIndexOffset(top, tadShapeInfo);
 
                         if (!descending == (dx[t0] > dx[t1])) {
                             T dt0 = dx[t0];
@@ -172,7 +172,7 @@ __global__ void execOesTadKernel(void *vx, Nd4jLong *xShapeInfo,
         if (cached) {
             dx = x + tadOffsets[r];
             for (int tid = threadIdx.x; tid < xTadLength; tid += blockDim.x) {
-                auto t0 = shape::getIndexOffset(tid, tadShapeInfo, xTadLength);
+                auto t0 = shape::getIndexOffset(tid, tadShapeInfo);
                 dx[t0] = shmem[tid];
             }
         }
diff --git a/libnd4j/include/loops/cuda/specials/pullRowsKernel.cu b/libnd4j/include/loops/cuda/specials/pullRowsKernel.cu
index 39db62099..9730565e6 100644
--- a/libnd4j/include/loops/cuda/specials/pullRowsKernel.cu
+++ b/libnd4j/include/loops/cuda/specials/pullRowsKernel.cu
@@ -53,8 +53,8 @@ namespace nd4j {
                 T *rZ = z + zTadOffsets[idx];
 
                 for (int i = threadIdx.x; i < tadLength; i += blockDim.x) {
-                    auto xOffset = shape::getIndexOffset(i, tadShapeInfo, tadLength);
-                    auto zOffset = shape::getIndexOffset(i, zTadShapeInfo, tadLength);
+                    auto xOffset = shape::getIndexOffset(i, tadShapeInfo);
+                    auto zOffset = shape::getIndexOffset(i, zTadShapeInfo);
                     rZ[zOffset] = rX[xOffset];
                 }
             }
diff --git a/libnd4j/include/loops/cuda/specials/setDiagonalKernel.cu b/libnd4j/include/loops/cuda/specials/setDiagonalKernel.cu
index adea72bc4..809a318ee 100644
--- a/libnd4j/include/loops/cuda/specials/setDiagonalKernel.cu
+++ b/libnd4j/include/loops/cuda/specials/setDiagonalKernel.cu
@@ -23,23 +23,43 @@
 namespace nd4j {
 
 ////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
+// set up given value to upper diagonal given
+// buffer - input buffer
+// shape - input shape
+// value - given value
+// diagonal - given upper diagonal (acceptable negative values also, 0 - the main diagonal)
+// row, cols - height and width of given matrix (MxN, rows = M, cols = N)
+//
     template <typename T>
     static __global__ void setDiagValueUpperKernel(void* buffer, Nd4jLong* shape, T value, int diagonal, Nd4jLong rows,
             Nd4jLong cols) {
 
-        Nd4jLong  rank = shape::rank(shape);
-        int totalThreads = blockDim.x;
-        T* array = reinterpret_cast<T*>(buffer);
+        __shared__ Nd4jLong  rank;
+        __shared__ T* array;
+
+        if (0 == threadIdx.x) {
+            rank = shape::rank(shape);
+            array = reinterpret_cast<T *>(buffer);
+        }
+        __syncthreads();
+
         for (Nd4jLong i = blockIdx.x; i < rows; i += gridDim.x) {
-            for (int j = threadIdx.x; j < cols; j += totalThreads) {
+            for (int j = threadIdx.x; j < cols; j += blockDim.x) {
                 Nd4jLong coords[2] = {i, j};
-                Nd4jLong xOffset = shape::getOffset(0, shape::shapeOf(shape), shape::stride(shape), coords, rank);
+                Nd4jLong xOffset = shape::getOffset(shape, coords);
                 if (i + diagonal <= j)
                     array[xOffset] = value;
             }
         }
     }
 ////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
+// set up given value to lower given diagonal
+// buffer - input buffer
+// shape - input shape
+// value - given value
+// diagonal - given lower diagonal (acceptable negative values also, 0 - the main diagonal)
+// row, cols - height and width of given matrix (MxN, rows = M, cols = N)
+//
 
     template <typename T>
     static __global__ void setDiagValueLowerKernel(void* buffer, Nd4jLong* shape, T value, int diagonal, Nd4jLong rows, Nd4jLong cols) {
@@ -48,7 +68,7 @@ namespace nd4j {
         for (Nd4jLong i = blockIdx.x; i < rows; i += gridDim.x) {
             for (int j = threadIdx.x; j < cols; j += totalThreads) {
                 Nd4jLong coords[2] = {i, j};
-                auto xOffset = shape::getOffset(0, shape::shapeOf(shape), shape::stride(shape), coords, rank);
+                auto xOffset = shape::getOffset(shape, coords);
                 if (i + diagonal >= j)
                     *(reinterpret_cast<T*>(buffer) + xOffset) = value;
             }
@@ -96,7 +116,4 @@ namespace nd4j {
             int diagonal, Nd4jLong rows, Nd4jLong cols, cudaStream_t& stream), LIBND4J_TYPES);
 ////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
 
-
-
-
 }
\ No newline at end of file
diff --git a/libnd4j/include/loops/cuda/specials/shuffleKernel.cu b/libnd4j/include/loops/cuda/specials/shuffleKernel.cu
index 6e7d4ad3b..c842cad4a 100644
--- a/libnd4j/include/loops/cuda/specials/shuffleKernel.cu
+++ b/libnd4j/include/loops/cuda/specials/shuffleKernel.cu
@@ -92,7 +92,7 @@ namespace nd4j {
                         } else {
                             for (Nd4jLong i = threadIdx.x; i < tadLength; i += blockDim.x) {
 
-                                auto xOffset = shape::getIndexOffset(i, tadOnlyShapeInfo[f], tadLength);
+                                auto xOffset = shape::getIndexOffset(i, tadOnlyShapeInfo[f]);
                                 auto yOffset = newOffset + xOffset;
                                 xOffset += oldOffset;
 
diff --git a/libnd4j/include/loops/cuda/specials/swapUnsafeKernel.cu b/libnd4j/include/loops/cuda/specials/swapUnsafeKernel.cu
index bf49d788e..1b7820f64 100644
--- a/libnd4j/include/loops/cuda/specials/swapUnsafeKernel.cu
+++ b/libnd4j/include/loops/cuda/specials/swapUnsafeKernel.cu
@@ -23,22 +23,31 @@
 namespace nd4j {
 
     ////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
+    // kernel to swap two NDArrays vals as linear sequences
+    // input - theSecondBuffer/Shape from input NDArray
+    // output - theFirstBuffer/Shape from input NDArray
     template <typename T>
     static __global__ void swapUnsafeKernel(void* theFirstBuffer, Nd4jLong* theFirstShape, void* theSecondBuffer, Nd4jLong* theSecondShape) {
         auto tid = blockIdx.x * blockDim.x + threadIdx.x;
         int totalThreads = gridDim.x * blockDim.x;
-        Nd4jLong resultLength = shape::length(theFirstShape);
-        //const auto resultLength = shape::length(outputShape);
-//        if (shape::order(outputShape) == 'c') {           //  ews == 1 always here
+
+        __shared__ Nd4jLong resultLength;
+        __shared__ T* input;
+        __shared__ T* output;
+        if (0 == threadIdx.x) {
+           resultLength = shape::length(theFirstShape);
+           input = reinterpret_cast<T*>(theSecondBuffer);
+           output = reinterpret_cast<T*>(theFirstBuffer);
+        }
+        __syncthreads();
+
         for (int i = tid; i < resultLength; i += totalThreads) {
             auto xEws = shape::order(theFirstShape)  == 'c'? shape::elementWiseStride(theFirstShape) :1;
             auto yEws = shape::order(theSecondShape) == 'c'? shape::elementWiseStride(theSecondShape):1;
-            //if (shape::order(theFirstShape) ==)
-            auto xOffset = shape::getIndexOffset(i * xEws, theFirstShape, resultLength);
-            auto yOffset = shape::getIndexOffset(i * yEws, theSecondShape, resultLength);
-            T temp = *(reinterpret_cast<T*>(theFirstBuffer) + xOffset);
-            *(reinterpret_cast<T*>(theFirstBuffer) + xOffset) = *(reinterpret_cast<T*>(theSecondBuffer) + yOffset);
-            *(reinterpret_cast<T*>(theSecondBuffer) + yOffset) = temp;
+
+            auto xOffset = shape::getIndexOffset(i * xEws, theFirstShape);
+            auto yOffset = shape::getIndexOffset(i * yEws, theSecondShape);
+            nd4j::math::nd4j_swap(output[xOffset], input[yOffset]);
         }
     }
 
diff --git a/libnd4j/include/loops/cuda/specials/tearKernel.cu b/libnd4j/include/loops/cuda/specials/tearKernel.cu
index 9f78f14da..e12aa485f 100644
--- a/libnd4j/include/loops/cuda/specials/tearKernel.cu
+++ b/libnd4j/include/loops/cuda/specials/tearKernel.cu
@@ -61,8 +61,8 @@ namespace nd4j {
             } else {
 
                 for (Nd4jLong j = threadIdx.x; j < tadLength; j += blockDim.x) {
-                    auto xOffset = shape::getIndexOffset(j, tadShapeInfo, tadLength);
-                    auto zOffset = shape::getIndexOffset(j, zShapeInfo, tadLength);
+                    auto xOffset = shape::getIndexOffset(j, tadShapeInfo);
+                    auto zOffset = shape::getIndexOffset(j, zShapeInfo);
 
                     z[zOffset] = s[xOffset];
                 }
diff --git a/libnd4j/include/loops/cuda/specials/tileKernel.cu b/libnd4j/include/loops/cuda/specials/tileKernel.cu
index 7d2e87e2d..257942484 100644
--- a/libnd4j/include/loops/cuda/specials/tileKernel.cu
+++ b/libnd4j/include/loops/cuda/specials/tileKernel.cu
@@ -21,8 +21,8 @@
 #include <loops/special_kernels.h>
 
 namespace nd4j {
-    static Nd4jLong __device__ __noinline__ _getIndexOffset(Nd4jLong index, Nd4jLong *shapeInfo, Nd4jLong length) {
-        return shape::getIndexOffset(index, shapeInfo, length);
+    static Nd4jLong __device__ __noinline__ _getIndexOffset(Nd4jLong index, Nd4jLong *shapeInfo) {
+        return shape::getIndexOffset(index, shapeInfo);
     }
 
     static Nd4jLong __device__ __noinline__ _subArrayOffset(Nd4jLong index, Nd4jLong *shapeInfoA, Nd4jLong *shapeInfoB) {
@@ -33,16 +33,19 @@ namespace nd4j {
         return shape::length(shapeInfo);
     }
 
-////////////////////////////////////////////////////////////////////////
+////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
+//  tileKernel:
+//  input: (inputBuffer and inputShape) - NDArray buffer and shape to tile
+//  output: (outputBuffer and outputShape) - NDArray to tile input
+//  resultLength - length for output array
     template<typename T>
     static __global__ void
     tileKernel(void const *inputBuffer, Nd4jLong *inputShape, void *outputBuffer, Nd4jLong *outputShape,
                Nd4jLong resultLength) {
 ////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
 //        Original code to transform in cuda-based
-        auto tid = blockIdx.x * blockDim.x + threadIdx.x;
+        auto tid = blockIdx.x * blockDim.x + threadIdx.x; // copy linear sequence of elements, so one-level threading
         int totalThreads = gridDim.x * blockDim.x;
-        //const auto resultLength = shape::length(outputShape);
         if (shape::order(outputShape) == 'c') {           //  ews == 1 always here
             for (int i = tid; i < resultLength; i += totalThreads) {
                 auto yOffset = _subArrayOffset(i, outputShape, inputShape);
@@ -50,7 +53,7 @@ namespace nd4j {
             }
         } else {
             for (int i = tid; i < resultLength; i += totalThreads) {
-                auto xOffset = _getIndexOffset(i, outputShape, resultLength);
+                auto xOffset = _getIndexOffset(i, outputShape);
                 auto yOffset = _subArrayOffset(i, outputShape, inputShape);
                 *(reinterpret_cast<T *>(outputBuffer) + xOffset) = *(reinterpret_cast<T const *>(inputBuffer) + yOffset);
             }
@@ -60,6 +63,7 @@ namespace nd4j {
 
     BUILD_SINGLE_TEMPLATE(template __global__ void tileKernel,(void const* inputBuffer, Nd4jLong* inputShape, void* outputBuffer, Nd4jLong* outputShape, Nd4jLong resultLength), LIBND4J_TYPES);
 
+////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
     template<typename T>
     void tileKernelH(void const *inputBuffer, Nd4jLong *inputShape, void *outputBuffer, Nd4jLong *outputShape, Nd4jLong resultLength, cudaStream_t *stream) {
         dim3 launchDims(256, 512, 8192);
@@ -68,6 +72,8 @@ namespace nd4j {
 
     BUILD_SINGLE_TEMPLATE(template void tileKernelH, (void const* inputBuffer, Nd4jLong* inputShape, void* outputBuffer, Nd4jLong* outputShape, Nd4jLong resultLength, cudaStream_t *stream), LIBND4J_TYPES);
 
+////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
+// enhancement for tileKernel to different input and output data types: X - output type, Y - input type
     template<typename X, typename Y>
     static __global__ void
     tileKernelDouble(void const *inputBuffer, Nd4jLong *inputShape, void *outputBuffer, Nd4jLong *outputShape, Nd4jLong resultLength, Nd4jLong ews) {
@@ -89,7 +95,7 @@ namespace nd4j {
 
             for (int i = tid; i < resultLength; i += totalThreads) {
 
-                auto xOffset = _getIndexOffset(i, outputShape, resultLength);
+                auto xOffset = _getIndexOffset(i, outputShape);
                 auto yOffset = _subArrayOffset(i, outputShape, inputShape);
                 *(reinterpret_cast<X *>(outputBuffer) + xOffset) = static_cast<X>(*(reinterpret_cast<Y const *>(inputBuffer) + yOffset));
             }
diff --git a/libnd4j/include/loops/cuda/summarystatsreduce.cu b/libnd4j/include/loops/cuda/summarystatsreduce.cu
index deca80217..4867f5de1 100644
--- a/libnd4j/include/loops/cuda/summarystatsreduce.cu
+++ b/libnd4j/include/loops/cuda/summarystatsreduce.cu
@@ -40,7 +40,7 @@ namespace functions {
 
 template <typename X, typename Z>
 void _CUDA_G summaryStatsReduceT(int op, void *dx, Nd4jLong *xShapeInfo, int xRank, void *extraParams, void *z, Nd4jLong *zShapeInfo, int zRank, int *dimension, int dimensionLength, int postProcessOrNot,bool biasCorrected,int *allocationBuffer, void *reductionBuffer, Nd4jLong *tadOnlyShapeInfo, Nd4jLong *tadOffsets) {
-            
+
     functions::summarystats::SummaryStatsReduce<X,Z>::transform(op,dx,xShapeInfo,extraParams,z,zShapeInfo,dimension,dimensionLength,biasCorrected,allocationBuffer,reductionBuffer,tadOnlyShapeInfo,tadOffsets);
 }
 
@@ -103,12 +103,12 @@ void _CUDA_G summaryStatsReduceT(int op, void *dx, Nd4jLong *xShapeInfo, int xRa
 			 */
         template<typename X, typename Z>
         template<typename OpType>
-        _CUDA_D void SummaryStatsReduce<X,Z>::transform(void *vx, Nd4jLong *xShapeInfo, 
-                                                        void *vextraParams, 
-                                                        void *vz, Nd4jLong *zShapeInfo, 
-                                                        int *dimension, int dimensionLength, 
-                                                        int postProcessOrNot, 
-                                                        int *allocationBuffer, void *vreductionBuffer, 
+        _CUDA_D void SummaryStatsReduce<X,Z>::transform(void *vx, Nd4jLong *xShapeInfo,
+                                                        void *vextraParams,
+                                                        void *vz, Nd4jLong *zShapeInfo,
+                                                        int *dimension, int dimensionLength,
+                                                        int postProcessOrNot,
+                                                        int *allocationBuffer, void *vreductionBuffer,
                                                         Nd4jLong *tadOnlyShapeInfo, Nd4jLong *tadOffsets) {
 
             auto dx = static_cast<X*>(vx);
@@ -204,7 +204,7 @@ void _CUDA_G summaryStatsReduceT(int op, void *dx, Nd4jLong *xShapeInfo, int xRa
                         sPartials[threadIdx.x] = val;
 
                         for (int i = threadIdx.x; i < tadLength; i += blockDim.x) {
-                            auto xOffset = tadOffsetForBlock + shape::getIndexOffset(i, tadOnlyShapeInfo, tadLength);
+                            auto xOffset = tadOffsetForBlock + shape::getIndexOffset(i, tadOnlyShapeInfo);
                             SummaryStatsData<X> indexVal2;
                             indexVal2.initWithValue(dx[xOffset]);
 
@@ -264,8 +264,8 @@ void _CUDA_G summaryStatsReduceT(int op, void *dx, Nd4jLong *xShapeInfo, int xRa
                 else {
 
                     for (Nd4jLong i = tid; i < n; i += blockDim.x * gridDim.x) {
-                        
-                        auto offset = shape::getIndexOffset(i, xShapeInfo, n);                        
+
+                        auto offset = shape::getIndexOffset(i, xShapeInfo);
                         SummaryStatsData<X> indexVal2;
                         indexVal2.initWithValue(dx[offset]);
                         reduction = update(reduction, indexVal2, extraParams);
@@ -279,7 +279,7 @@ void _CUDA_G summaryStatsReduceT(int op, void *dx, Nd4jLong *xShapeInfo, int xRa
 
                 if (gridDim.x > 1) {
                     __shared__ bool amLast;
-                    unsigned int *tc = (unsigned int *)reductionBuffer;                    
+                    unsigned int *tc = (unsigned int *)reductionBuffer;
                     tid = threadIdx.x;
                     if (threadIdx.x == 0) {
                         SummaryStatsData<X> *pBuffer = (SummaryStatsData<X>*) reductionBuffer;
@@ -338,9 +338,9 @@ void _CUDA_G summaryStatsReduceT(int op, void *dx, Nd4jLong *xShapeInfo, int xRa
 
         template <typename X, typename Z>
         _CUDA_H void SummaryStatsReduce<X,Z>::execSummaryStatsReduceScalar(dim3& launchDims, cudaStream_t *stream, int opNum, void *vx, Nd4jLong *xShapeInfo, Nd4jLong *hxShapeInfo, void *vextraParams, void *vz, Nd4jLong *zShapeInfo, Nd4jLong *hzShapeInfo, Nd4jLong *tadShapeInfo, Nd4jLong *tadOffsets, bool biasCorrected, void *reductionBuffer) {
-            
+
             auto x = static_cast<X*>(vx);
-            auto extraParams = static_cast<Z*>(vextraParams);                                        
+            auto extraParams = static_cast<Z*>(vextraParams);
             auto z = reinterpret_cast<Z*>(vz);
             auto reductionPointerA = reinterpret_cast<Z*>(reductionBuffer);
 
diff --git a/libnd4j/include/loops/cuda/transform/transform_any.cu b/libnd4j/include/loops/cuda/transform/transform_any.cu
index 34f56380a..18b53cea7 100644
--- a/libnd4j/include/loops/cuda/transform/transform_any.cu
+++ b/libnd4j/include/loops/cuda/transform/transform_any.cu
@@ -36,7 +36,7 @@ __global__ void transformAnySimple(void *x, Nd4jLong *xShapeInfo, int xRank,
 								int *allocationPointer,
 								void *reductionPointer,
 								Nd4jLong *tadShapeInfo, Nd4jLong *tadOffsets) {
-	
+
 	functions::transform::TransformAny<X,Z>::template transformCuda<OpType>(x,xShapeInfo,params,z,zShapeInfo,allocationPointer,reductionPointer,tadShapeInfo, tadOffsets);
 }
 
@@ -57,7 +57,7 @@ namespace functions {
         __device__ void TransformAny<X,Z>::transformCuda(void *vx, Nd4jLong *xShapeInfo,
         												void *vparams,
         												void *vz, Nd4jLong *zShapeInfo,
-        												int *allocationPointer, void *vreductionPointer, 
+        												int *allocationPointer, void *vreductionPointer,
         												Nd4jLong *tadShapeInfo, Nd4jLong *tadOffsets) {
 
         	auto x = reinterpret_cast<X*>(vx);
@@ -70,9 +70,9 @@ namespace functions {
         	__shared__ char xOrder;
             __shared__ char zOrder;
             __shared__ Nd4jLong length;
-            
+
 	        if (threadIdx.x == 0) {
-    	                               	                            
+
         		xEws = shape::elementWiseStride(xShapeInfo);
             	zEws = shape::elementWiseStride(zShapeInfo);
                 xOrder = shape::order(xShapeInfo);
@@ -84,26 +84,26 @@ namespace functions {
 	    	auto tid = blockIdx.x * blockDim.x + threadIdx.x;
 			int totalThreads = gridDim.x * blockDim.x;
 
-		    if(xEws > 0 && zEws > 0 && xOrder == zOrder) {								
-					
+		    if(xEws > 0 && zEws > 0 && xOrder == zOrder) {
+
 				for (int i = tid; i < length; i += totalThreads)
-					z[i * zEws] = OpType::op(x[i * xEws], params);				
+					z[i * zEws] = OpType::op(x[i * xEws], params);
 		    }
-		    else {			        
+		    else {
 				if(vx == vz) {
 					for (Nd4jLong i = tid; i < length; i+= totalThreads) {
-						auto xOffset = shape::getIndexOffset(i, xShapeInfo,  length);						
+						auto xOffset = shape::getIndexOffset(i, xShapeInfo);
 	    		    	z[xOffset] = OpType::op(x[xOffset], params);
-		    	   	}		    	    
+		    	   	}
 				}
 				else {
 		    	   	for (Nd4jLong i = tid; i < length; i+= totalThreads) {
-						auto xOffset = shape::getIndexOffset(i, xShapeInfo,  length);
-						auto zOffset = shape::getIndexOffset(i, zShapeInfo, length);				        
+						auto xOffset = shape::getIndexOffset(i, xShapeInfo);
+						auto zOffset = shape::getIndexOffset(i, zShapeInfo);
 	    		    	z[zOffset] = OpType::op(x[xOffset], params);
 		    	   	}
 		    	}
-		    }	        
+		    }
 	    };
 
 
diff --git a/libnd4j/include/loops/cuda/transform/transform_bool.cu b/libnd4j/include/loops/cuda/transform/transform_bool.cu
index 52e6b4a10..e88a4274b 100644
--- a/libnd4j/include/loops/cuda/transform/transform_bool.cu
+++ b/libnd4j/include/loops/cuda/transform/transform_bool.cu
@@ -68,16 +68,16 @@ namespace functions {
 		    if(OpType::requiresSpecial) {
 			    OpType::execSpecialCuda(x,xShapeInfo,z,zShapeInfo,params, allocationPointer, reductionPointer, tadShapeInfo, tadOffsets);
 			    return;
-		    } 
+		    }
 		    else {
 		    	__shared__ Nd4jLong xEws;
     	        __shared__ Nd4jLong zEws;
         	    __shared__ char xOrder;
             	__shared__ char zOrder;
             	__shared__ Nd4jLong length;
-            
+
 	            if (threadIdx.x == 0) {
-    	                               	                            
+
         	        xEws = shape::elementWiseStride(xShapeInfo);
             	    zEws = shape::elementWiseStride(zShapeInfo);
                 	xOrder = shape::order(xShapeInfo);
@@ -87,28 +87,28 @@ namespace functions {
             	__syncthreads();
 
 	    	    auto tid = blockIdx.x * blockDim.x + threadIdx.x;
-				int totalThreads = gridDim.x * blockDim.x;                
+				int totalThreads = gridDim.x * blockDim.x;
+
+		        if(xEws > 0 && zEws > 0 && xOrder == zOrder) {
 
-		        if(xEws > 0 && zEws > 0 && xOrder == zOrder) {								
-					
 					for (int i = tid; i < length; i += totalThreads)
-						z[i * zEws] = OpType::op(x[i * xEws], params);				
+						z[i * zEws] = OpType::op(x[i * xEws], params);
 		        }
-		        else {			        
+		        else {
 					if(vx == vz) {
 						for (Nd4jLong i = tid; i < length; i+= totalThreads) {
-							auto xOffset = shape::getIndexOffset(i, xShapeInfo,  length);						
+							auto xOffset = shape::getIndexOffset(i, xShapeInfo);
 	    			    	z[xOffset] = OpType::op(x[xOffset], params);
-		    	    	}		    	    
+		    	    	}
 					}
 					else {
 		    	    	for (Nd4jLong i = tid; i < length; i+= totalThreads) {
-							auto xOffset = shape::getIndexOffset(i, xShapeInfo,  length);
-							auto zOffset = shape::getIndexOffset(i, zShapeInfo, length);				        
-	    			    	z[zOffset] = OpType::op(x[xOffset], params); 							
+							auto xOffset = shape::getIndexOffset(i, xShapeInfo);
+							auto zOffset = shape::getIndexOffset(i, zShapeInfo);
+	    			    	z[zOffset] = OpType::op(x[xOffset], params);
 		    	    	}
 		    		}
-		        }		       
+		        }
 	        }
 	    };
 
diff --git a/libnd4j/include/loops/cuda/transform/transform_float.cu b/libnd4j/include/loops/cuda/transform/transform_float.cu
index 6fe7b18d1..44ddb0246 100644
--- a/libnd4j/include/loops/cuda/transform/transform_float.cu
+++ b/libnd4j/include/loops/cuda/transform/transform_float.cu
@@ -35,7 +35,7 @@ __global__ void transformFloatSimple(void *x, Nd4jLong *xShapeInfo, int xRank,
 								int *allocationPointer,
 								void *reductionPointer,
 								Nd4jLong *tadShapeInfo, Nd4jLong *tadOffsets) {
-	
+
 	functions::transform::TransformFloat<X,Z>::template transformCuda<OpType>(
 	    											x, xShapeInfo,
 	    											params,
@@ -64,7 +64,7 @@ namespace functions {
 											void *vparams,
 											void *vz,
 											Nd4jLong *zShapeInfo,
-											int *allocationPointer, void *vreductionPointer, 
+											int *allocationPointer, void *vreductionPointer,
 											Nd4jLong *tadShapeInfo, Nd4jLong *tadOffsets) {
 
         	auto x = reinterpret_cast<X*>(vx);
@@ -75,7 +75,7 @@ namespace functions {
 		    if(OpType::requiresSpecial) {
 			    OpType::execSpecialCuda(x,xShapeInfo,z,zShapeInfo,params, allocationPointer, reductionPointer, tadShapeInfo, tadOffsets);
 			    return;
-		    } 
+		    }
 		    else {
 
 	            __shared__ Nd4jLong xEws;
@@ -83,9 +83,9 @@ namespace functions {
         	    __shared__ char xOrder;
             	__shared__ char zOrder;
             	__shared__ Nd4jLong length;
-            
+
 	            if (threadIdx.x == 0) {
-    	                               	                            
+
         	        xEws = shape::elementWiseStride(xShapeInfo);
             	    zEws = shape::elementWiseStride(zShapeInfo);
                 	xOrder = shape::order(xShapeInfo);
@@ -95,24 +95,24 @@ namespace functions {
             	__syncthreads();
 
 	    	    auto tid = blockIdx.x * blockDim.x + threadIdx.x;
-				int totalThreads = gridDim.x * blockDim.x;                
+				int totalThreads = gridDim.x * blockDim.x;
+
+		        if(xEws > 0 && zEws > 0 && xOrder == zOrder) {
 
-		        if(xEws > 0 && zEws > 0 && xOrder == zOrder) {								
-					
 					for (Nd4jLong i = tid; i < length; i += totalThreads)
                         z[i * zEws] = OpType::op(x[i * xEws], params);
 		        }
-		        else {			        
+		        else {
 					if(vx == vz) {
 						for (Nd4jLong i = tid; i < length; i+= totalThreads) {
-							auto xOffset = shape::getIndexOffset(i, xShapeInfo,  length);						
+							auto xOffset = shape::getIndexOffset(i, xShapeInfo);
 	    			    	z[xOffset] = OpType::op(x[xOffset], params);
-		    	    	}		    	    
+		    	    	}
 					}
 					else {
 		    	    	for (Nd4jLong i = tid; i < length; i+= totalThreads) {
-							auto xOffset = shape::getIndexOffset(i, xShapeInfo,  length);
-							auto zOffset = shape::getIndexOffset(i, zShapeInfo, length);				        
+							auto xOffset = shape::getIndexOffset(i, xShapeInfo);
+							auto zOffset = shape::getIndexOffset(i, zShapeInfo);
 	    			    	z[zOffset] = OpType::op(x[xOffset], params);
 		    	    	}
 		    		}
diff --git a/libnd4j/include/loops/cuda/transform/transform_same.cu b/libnd4j/include/loops/cuda/transform/transform_same.cu
index 6c533ac3a..e59381fba 100644
--- a/libnd4j/include/loops/cuda/transform/transform_same.cu
+++ b/libnd4j/include/loops/cuda/transform/transform_same.cu
@@ -95,14 +95,14 @@ namespace functions {
 		        else {
 					if(vx == vz) {
 						for (Nd4jLong i = tid; i < length; i+= totalThreads) {
-							auto xOffset = shape::getIndexOffset(i, xShapeInfo,  length);
+							auto xOffset = shape::getIndexOffset(i, xShapeInfo);
 	    			    	z[xOffset] = OpType::op(x[xOffset], params);
 		    	    	}
 					}
 					else {
 		    	    	for (Nd4jLong i = tid; i < length; i+= totalThreads) {
-							auto xOffset = shape::getIndexOffset(i, xShapeInfo,  length);
-							auto zOffset = shape::getIndexOffset(i, zShapeInfo, length);
+							auto xOffset = shape::getIndexOffset(i, xShapeInfo);
+							auto zOffset = shape::getIndexOffset(i, zShapeInfo);
 	    			    	z[zOffset] = OpType::op(x[xOffset], params);
 		    	    	}
 		    		}
diff --git a/libnd4j/include/loops/cuda/transform/transform_strict.cu b/libnd4j/include/loops/cuda/transform/transform_strict.cu
index a0989b0e6..0befdf35f 100644
--- a/libnd4j/include/loops/cuda/transform/transform_strict.cu
+++ b/libnd4j/include/loops/cuda/transform/transform_strict.cu
@@ -35,7 +35,7 @@ __global__ void transformStrictSimple(void *x, Nd4jLong *xShapeInfo, int xRank,
 								int *allocationPointer,
 								void *reductionPointer,
 								Nd4jLong *tadShapeInfo, Nd4jLong *tadOffsets) {
-	
+
 	functions::transform::TransformStrict<X>::template transformCuda<OpType>(x,xShapeInfo,params,z,zShapeInfo,allocationPointer,reductionPointer,tadShapeInfo, tadOffsets);
 }
 
@@ -97,14 +97,14 @@ namespace functions {
 		        else {
 					if(vx == vz) {
 						for (Nd4jLong i = tid; i < length; i+= totalThreads) {
-							auto xOffset = shape::getIndexOffset(i, xShapeInfo,  length);
+							auto xOffset = shape::getIndexOffset(i, xShapeInfo);
 	    			    	z[xOffset] = OpType::op(x[xOffset], params);
 		    	    	}
 					}
 					else {
 		    	    	for (Nd4jLong i = tid; i < length; i+= totalThreads) {
-							auto xOffset = shape::getIndexOffset(i, xShapeInfo,  length);
-							auto zOffset = shape::getIndexOffset(i, zShapeInfo, length);
+							auto xOffset = shape::getIndexOffset(i, xShapeInfo);
+							auto zOffset = shape::getIndexOffset(i, zShapeInfo);
 	    			    	z[zOffset] = OpType::op(x[xOffset], params);
 		    	    	}
 		    		}
diff --git a/libnd4j/include/ops/declarable/generic/nn/convo/conv3d.cpp b/libnd4j/include/ops/declarable/generic/nn/convo/conv3d.cpp
index f7dddec32..38138de0e 100644
--- a/libnd4j/include/ops/declarable/generic/nn/convo/conv3d.cpp
+++ b/libnd4j/include/ops/declarable/generic/nn/convo/conv3d.cpp
@@ -24,6 +24,7 @@
 
 #include <ops/declarable/CustomOperations.h>
 #include <ops/declarable/helpers/convolutions.h>
+#include <ops/declarable/helpers/addBias.h>
 #include <MmulHelper.h>
 
 namespace nd4j {
@@ -80,7 +81,8 @@ CUSTOM_OP_IMPL(conv3dnew, 2, 1, false, 0, 13) {
     MmulHelper::tensorDot(&columns, weights, output, {1,2,3,4}, {3,0,1,2}, permutForOutput);
 
     if(bias)
-        output->applyBroadcast(broadcast::Add, {indIOioC}, bias);
+        // output->applyBroadcast(broadcast::Add, {indIOioC}, bias);
+        helpers::addBias(block, *output, *bias, *output, isNCDHW);
 
      if(!isNCDHW)
         delete input;
diff --git a/libnd4j/include/ops/declarable/generic/nn/convo/deconv2d.cpp b/libnd4j/include/ops/declarable/generic/nn/convo/deconv2d.cpp
index 3a7450fc7..f69b6c0f9 100644
--- a/libnd4j/include/ops/declarable/generic/nn/convo/deconv2d.cpp
+++ b/libnd4j/include/ops/declarable/generic/nn/convo/deconv2d.cpp
@@ -27,7 +27,7 @@
 #include <declarable/helpers/convolutions.h>
 #include <ops/declarable/helpers/im2col.h>
 #include <ops/declarable/helpers/col2im.h>
-
+#include <ops/declarable/helpers/addBias.h>
 
 namespace nd4j {
 namespace ops  {
@@ -80,7 +80,8 @@ CUSTOM_OP_IMPL(deconv2d, 2, 1, false, 0, 9) {
 
     //----- add biases if required -----//
     if(bias)
-        output->applyBroadcast(broadcast::Add, {1}, bias);
+        // output->applyBroadcast(broadcast::Add, {1}, bias);
+        helpers::addBias(block, *output, *bias, *output, true);
 
      if(!isNCHW)
         delete output;
diff --git a/libnd4j/include/ops/declarable/generic/nn/convo/deconv3d.cpp b/libnd4j/include/ops/declarable/generic/nn/convo/deconv3d.cpp
index 20d0e991e..f875e4693 100644
--- a/libnd4j/include/ops/declarable/generic/nn/convo/deconv3d.cpp
+++ b/libnd4j/include/ops/declarable/generic/nn/convo/deconv3d.cpp
@@ -23,6 +23,7 @@
 
 #include <ops/declarable/CustomOperations.h>
 #include <ops/declarable/helpers/convolutions.h>
+#include <ops/declarable/helpers/addBias.h>
 #include <MmulHelper.h>
 
 namespace nd4j {
@@ -79,7 +80,8 @@ CUSTOM_OP_IMPL(deconv3d, 2, 1, false, 0, 13) {
 
     //----- add biases if required -----//
     if(bias)
-        output->applyBroadcast(broadcast::Add,{1}, bias);
+        // output->applyBroadcast(broadcast::Add,{1}, bias);
+        helpers::addBias(block, *output, *bias, *output, true);
 
     if(!isNCDHW)
         delete output;
diff --git a/libnd4j/include/ops/declarable/generic/parity_ops/bias_add.cpp b/libnd4j/include/ops/declarable/generic/parity_ops/bias_add.cpp
index 3309c6104..f5a65079a 100644
--- a/libnd4j/include/ops/declarable/generic/parity_ops/bias_add.cpp
+++ b/libnd4j/include/ops/declarable/generic/parity_ops/bias_add.cpp
@@ -15,107 +15,111 @@
  ******************************************************************************/
 
 //
-//  @author raver119@gmail.com
+// @author raver119@gmail.com
+// @author Yurii Shyrma (iuriish@yahoo.com)
 //
 
 #include <op_boilerplate.h>
 #if NOT_EXCLUDED(OP_biasadd)
 
 #include <ops/declarable/CustomOperations.h>
+#include<ops/declarable/helpers/addBias.h>
 
 namespace nd4j {
-    namespace ops {
-        DECLARE_TYPES(biasadd) {
-            getOpDescriptor()
-                    ->setAllowedInputTypes(nd4j::DataType::ANY)
-                    ->setAllowedOutputTypes({ALL_FLOATS});
-        }
+namespace ops {
 
-        CUSTOM_OP_IMPL(biasadd, 2, 1, true, 0, 0) {
-            //REQUIRE_OK(this->validateInput2D(block));
-            auto input = INPUT_VARIABLE(0);
-            auto bias = INPUT_VARIABLE(1);
+////////////////////////////////////////////////////////////////////
+CUSTOM_OP_IMPL(biasadd, 2, 1, true, 0, 0) {
 
-            REQUIRE_TRUE(bias->isRowVector(), 0, "Bias array should be a vector");
+    auto input = INPUT_VARIABLE(0);
+    auto bias = INPUT_VARIABLE(1);
 
-            auto z = OUTPUT_VARIABLE(0);
+    auto output = OUTPUT_VARIABLE(0);
 
-            if (input->isMatrix())
-                input->addRowVector(bias, z);
-            else {
-                // TODO: we might want to use NDArray::applyTrueBroadcast here, like AddOp does
-                std::vector<Nd4jLong> shape({-1, bias->lengthOf()});
-                //nd4j_debug("Reshaping to: [%i, %i]\n", -1, (int) bias->lengthOf());
-                auto tArr = input->reshape(input->ordering(), shape);
-                auto zArr = z->reshape(z->ordering(), shape);
-                tArr.addRowVector(bias, &zArr);
-            }
+    const bool isNCHW = !block.getBArguments()->empty() ? B_ARG(0) : false;
+    const int channelDim = isNCHW ? 1 : input->rankOf() - 1;      // second or last
 
-            STORE_RESULT(*z);
+    REQUIRE_TRUE(bias->rankOf() == 1, 0, "BIASADD CUSTOM_OP: bias array should have rank = 1, but got %i instead !", bias->rankOf());
 
-            return Status::OK();
-        }
-        DECLARE_SYN(bias_add, biasadd);
+    REQUIRE_TRUE(bias->sizeAt(0) == input->sizeAt(channelDim), 0, "BIASADD CUSTOM_OP: shapes of bias %s and input %s arrays are not suitable for broadcast operation along channel dimension %i !", ShapeUtils::shapeAsString(bias).c_str(), ShapeUtils::shapeAsString(input).c_str(), channelDim);
 
-        DECLARE_SHAPE_FN(biasadd) {
-            auto xShape = inputShape->at(0);
-            auto yShape = inputShape->at(1);
+    REQUIRE_TRUE(output->isSameShape(input), 0, "BIASADD CUSTOM_OP: wrong shape of output array, expected is %s but got %s instead !", ShapeUtils::shapeAsString(input).c_str(), ShapeUtils::shapeAsString(output).c_str());
 
-            auto dtype = ArrayOptions::dataType(yShape);
-            return SHAPELIST(ConstantShapeHelper::getInstance()->createShapeInfo(ShapeDescriptor(xShape, dtype)));
-        }
+    helpers::addBias(block, *input, *bias, *output, isNCHW);
+    // input->applyBroadcast(nd4j::broadcast::Add, {channelDim}, bias, output);
 
-        DECLARE_TYPES(biasadd_bp) {
-            getOpDescriptor()
-                    ->setAllowedInputTypes(nd4j::DataType::ANY)
-                    ->setAllowedOutputTypes({ALL_FLOATS});
-        }
+    return Status::OK();
+}
+DECLARE_SYN(bias_add, biasadd);
 
-        CUSTOM_OP_IMPL(biasadd_bp, 3, 2, false, 0, 0) {
-            auto input = INPUT_VARIABLE(0);
-            auto bias = INPUT_VARIABLE(1);
-            auto epsilonNext = INPUT_VARIABLE(2);
+////////////////////////////////////////////////////////////////////
+DECLARE_SHAPE_FN(biasadd) {
+    auto xShape = inputShape->at(0);
+    auto yShape = inputShape->at(1);
 
-            auto epsilon = OUTPUT_VARIABLE(0);
-            auto gradB = OUTPUT_VARIABLE(1);
+    auto dtype = ArrayOptions::dataType(yShape);
+    return SHAPELIST(ConstantShapeHelper::getInstance()->createShapeInfo(ShapeDescriptor(xShape, dtype)));
+}
 
-            epsilon->assign(epsilonNext);
+DECLARE_TYPES(biasadd) {
+    getOpDescriptor()
+            ->setAllowedInputTypes(nd4j::DataType::ANY)
+            ->setAllowedOutputTypes({ALL_FLOATS});
+}
 
-            // cnn case
-            if (input->rankOf() == 4) {
-                auto epsilonNext2d = epsilonNext->permute({1, 0, 2, 3});
-                epsilonNext2d.reshapei('c', {(int) bias->lengthOf(), -1});
+////////////////////////////////////////////////////////////////////
+CUSTOM_OP_IMPL(biasadd_bp, 3, 2, false, 0, 0) {
+    auto input = INPUT_VARIABLE(0);
+    auto bias = INPUT_VARIABLE(1);
+    auto epsilonNext = INPUT_VARIABLE(2);
 
-                auto sum = epsilonNext2d.reduceAlongDimension(reduce::Sum, {1});
-                gradB->assign(sum);
+    auto epsilon = OUTPUT_VARIABLE(0);
+    auto gradB = OUTPUT_VARIABLE(1);
 
-                delete sum;
-            } else if (input->rankOf() == 2) {
-                // regular fully-connected case
-                auto sum = epsilonNext->reduceAlongDimension(reduce::Sum, {0});
-                gradB->assign(sum);
-                
-                delete sum;
-            }
+    epsilon->assign(epsilonNext);
 
-            return ND4J_STATUS_OK;
-        }
-        DECLARE_SYN(BiasAddGrad, biasadd_bp);
+    // cnn case
+    if (input->rankOf() == 4) {
+        auto epsilonNext2d = epsilonNext->permute({1, 0, 2, 3});
+        epsilonNext2d.reshapei('c', {(int) bias->lengthOf(), -1});
 
-        DECLARE_SHAPE_FN(biasadd_bp) {
-            auto input = inputShape->at(0);
-            auto bias = inputShape->at(1);
+        auto sum = epsilonNext2d.reduceAlongDimension(reduce::Sum, {1});
+        gradB->assign(sum);
 
-            Nd4jLong* epsShape;
-            Nd4jLong* gradShape;
+        delete sum;
+    } else if (input->rankOf() == 2) {
+        // regular fully-connected case
+        auto sum = epsilonNext->reduceAlongDimension(reduce::Sum, {0});
+        gradB->assign(sum);
 
-            COPY_SHAPE(input, epsShape);
-            COPY_SHAPE(bias, gradShape);
-
-            return SHAPELIST(CONSTANT(epsShape), CONSTANT(gradShape));
-
-        }
+        delete sum;
     }
+
+    return ND4J_STATUS_OK;
+}
+DECLARE_SYN(BiasAddGrad, biasadd_bp);
+
+DECLARE_SHAPE_FN(biasadd_bp) {
+    auto input = inputShape->at(0);
+    auto bias = inputShape->at(1);
+
+    Nd4jLong* epsShape;
+    Nd4jLong* gradShape;
+
+    COPY_SHAPE(input, epsShape);
+    COPY_SHAPE(bias, gradShape);
+
+    return SHAPELIST(CONSTANT(epsShape), CONSTANT(gradShape));
+}
+
+DECLARE_TYPES(biasadd_bp) {
+    getOpDescriptor()
+            ->setAllowedInputTypes(nd4j::DataType::ANY)
+            ->setAllowedOutputTypes({ALL_FLOATS});
+}
+
+
+}
 }
 
 #endif
\ No newline at end of file
diff --git a/libnd4j/include/ops/declarable/generic/parity_ops/matrix_diag.cpp b/libnd4j/include/ops/declarable/generic/parity_ops/matrix_diag.cpp
index c430fd4d2..c3e73da84 100644
--- a/libnd4j/include/ops/declarable/generic/parity_ops/matrix_diag.cpp
+++ b/libnd4j/include/ops/declarable/generic/parity_ops/matrix_diag.cpp
@@ -43,14 +43,15 @@ DECLARE_SHAPE_FN(matrix_diag) {
     auto in = inputShape->at(0);
     int inRank = shape::rank(in);
 
+    //  if for example diagonal array has shape [A,B,C] then output array has shape [A,B,C,C]
+
     int outRank = inRank + 1;
-    auto lastDimension = shape::sizeAt(in, -1);
 
     ALLOCATE(outShapeInfo, block.getWorkspace(), shape::shapeInfoLength(outRank), Nd4jLong);
     outShapeInfo[0] = outRank;
     for(int i = 0; i < inRank; ++i)
         outShapeInfo[i + 1] = shape::sizeAt(in, i);
-    outShapeInfo[outRank] = lastDimension;
+    outShapeInfo[outRank] = shape::sizeAt(in, -1);
 
     ShapeUtils::updateStridesAndType(outShapeInfo, in, shape::order(in));
 
diff --git a/libnd4j/include/ops/declarable/generic/transforms/layer_norm.cpp b/libnd4j/include/ops/declarable/generic/transforms/layer_norm.cpp
index 8ab5fa32f..06656b9de 100644
--- a/libnd4j/include/ops/declarable/generic/transforms/layer_norm.cpp
+++ b/libnd4j/include/ops/declarable/generic/transforms/layer_norm.cpp
@@ -23,7 +23,7 @@
 
 #include <ops/declarable/CustomOperations.h>
 #include <ops/declarable/helpers/reverse.h>
-
+#include <ops/declarable/helpers/addBias.h>
 
 namespace nd4j {
 namespace ops  {
@@ -59,7 +59,8 @@ namespace ops  {
         output->applyBroadcast(nd4j::broadcast::Multiply, {dimC}, gain);
         if(bias != nullptr) {
             // output->applyTrueBroadcast(nd4j::BroadcastOpsTuple::Add(), bias, output);
-            output->applyBroadcast(nd4j::broadcast::Add, {dimC}, bias);
+            // output->applyBroadcast(nd4j::broadcast::Add, {dimC}, bias);
+            helpers::addBias(block, *output, *bias, *output, isNCHW);
         }
 
         return Status::OK();
diff --git a/libnd4j/include/ops/declarable/headers/parity_ops.h b/libnd4j/include/ops/declarable/headers/parity_ops.h
index bb7f306bd..e30ff86a5 100644
--- a/libnd4j/include/ops/declarable/headers/parity_ops.h
+++ b/libnd4j/include/ops/declarable/headers/parity_ops.h
@@ -79,36 +79,44 @@ namespace nd4j {
         * Inserts elements provided by diagonal array into the main diagonal of innermost matrices of input array
         *
         * Input arrays:
-        *    input:    input array, considered as batch of matrices
-        *    diagonal: array containing elements to be inserted into input array,
-        *              following rank condition should be satisfied: diagonal_rank = input_rank - 1,
-        *              the shapes of diagonal and input arrays must be equal except last dimension of input array,
-        *              for example if input_shape = [A,B,C,D] then diagonal_shape = [A,B,C],
-        *              also last dimension of diagonal array should be equal to smaller of last and last but one input dimensions
-        *              that is: diagonal_shape[-1] = min(input_shape[-1], input_shape[-2])
+        *  0: input array, considered as batch of matrices
+        *  1: diagonal array containing elements to be inserted into input array,
+        *     following rank condition should be satisfied: diagonal_rank = input_rank - 1,
+        *     the shapes of diagonal and input arrays must be equal except last dimension of input array,
+        *     for example if input_shape = [A,B,C,D] then diagonal_shape = [A,B,C],
+        *     also last dimension of diagonal array should be equal to smaller of last and last but one input dimensions
+        *     that is: diagonal_shape[-1] = min(input_shape[-1], input_shape[-2])
         *
         * Output array:
-        *    has the same shape as input, corresponding diagonal elements are substituted
+        *  0: has the same shape as input, corresponding diagonal elements are substituted
         */
         #if NOT_EXCLUDED(OP_matrix_set_diag)
         DECLARE_CONFIGURABLE_OP(matrix_set_diag, 2, 1, false, 0, 0);
         #endif
 
         /**
-         * Returns a batched matrix tensor with diagonal values given (as TF.matrix_diag).
-         */
+        * Inserts elements provided by diagonal array into the main diagonal of innermost matrices of output array,
+        * rest output elements are set to zeros
+        *
+        * Input array:
+        *    diagonal: array containing elements to be inserted into output array,
+        *              following rank condition is present: diagonal_rank = ouput_rank - 1
+        *
+        * Output array:
+        *   0: is considered as batch of matrices, if for example diagonal array has shape [A,B,C] then output array has shape [A,B,C,C]
+        */
         DECLARE_CUSTOM_OP(matrix_diag, 1, 1, false, 0, 0);
 
         /**
         * This op calculates regularized incomplete beta integral Ix(a, b).
         * Implementation is based on two algorithms depending on input values of a and b:
-        * - when a and b are both >  maxValue (3000.), then apply Gauss-Legendre quadrature method
-        * - when a and b are both <= maxValue (3000.), then apply modified Lentz’s algorithm for continued fractions
+        * - when a and b are both >  maxValue (3000.), then Gauss-Legendre quadrature method is applied
+        * - when a and b are both <= maxValue (3000.), then modified Lentz’s algorithm for continued fractions is applied
         *
         * Input arrays:
-        *    a: define power t^{a-1}, must be > 0, type float.
-        *    b: define power (1-t)^{b-1}, must be > 0, type float.
-        *    x: define upper limit of integration, must be within (0 <= x <= 1) range, type float.
+        *    a: defines power t^{a-1}, must be > 0, type float.
+        *    b: defines power (1-t)^{b-1}, must be > 0, type float.
+        *    x: defines upper limit of integration, must be within (0 <= x <= 1) range, type float.
         *
         * Output array:
         *    0: values of  regularized incomplete beta integral that corresponds to variable upper limit x, type float
diff --git a/libnd4j/include/ops/declarable/helpers/addBias.h b/libnd4j/include/ops/declarable/helpers/addBias.h
index 3d9fdec88..c754c07de 100644
--- a/libnd4j/include/ops/declarable/helpers/addBias.h
+++ b/libnd4j/include/ops/declarable/helpers/addBias.h
@@ -22,14 +22,15 @@
 #define LIBND4J_ADDBIAS_H
 
 #include <ops/declarable/helpers/helpers.h>
+#include <graph/Context.h>
 
 namespace nd4j    {
 namespace ops     {
 namespace helpers {
 
 
-	void addBias(NDArray& input, const NDArray& bias, const bool isNCHW);
-    
+	void addBias(graph::Context& block, const NDArray& input, const NDArray& bias, NDArray& output, const bool isNCHW);
+
 
 }
 }
diff --git a/libnd4j/include/ops/declarable/helpers/cpu/activations.cpp b/libnd4j/include/ops/declarable/helpers/cpu/activations.cpp
index fbc395fc6..bd29094ec 100644
--- a/libnd4j/include/ops/declarable/helpers/cpu/activations.cpp
+++ b/libnd4j/include/ops/declarable/helpers/cpu/activations.cpp
@@ -91,19 +91,19 @@ static void softMaxForVector_(void *input, Nd4jLong *inShapeInfo, void *output,
 
 PRAGMA_OMP_SIMD_ARGS(reduction(OMP_MAXT:max))
         for (int i = 0; i < length; i++) {
-            const Nd4jLong offset = shape::getIndexOffset(i, inShapeInfo, length);
+            const Nd4jLong offset = shape::getIndexOffset(i, inShapeInfo);
             max = nd4j::math::nd4j_max<T>(max, inBuff[offset]);
         }
 
 PRAGMA_OMP_PARALLEL_FOR_SIMD_ARGS(reduction(OMP_SUMT:sum))
         for (int i = 0; i < length; i++) {
-            const Nd4jLong offset = shape::getIndexOffset(i, inShapeInfo, length);
+            const Nd4jLong offset = shape::getIndexOffset(i, inShapeInfo);
             outBuff[offset] = nd4j::math::nd4j_exp<T, T>(inBuff[offset] - max);
             sum += outBuff[offset];
         }
 PRAGMA_OMP_SIMD
         for (int i = 0; i < length; i++) {
-            const Nd4jLong offset = shape::getIndexOffset(i, inShapeInfo, length);
+            const Nd4jLong offset = shape::getIndexOffset(i, inShapeInfo);
             outBuff[offset] /= sum;
             outBuff[offset] *= (1.f - outBuff[offset]);     // derivative
         }
diff --git a/libnd4j/include/ops/declarable/helpers/cpu/addBias.cpp b/libnd4j/include/ops/declarable/helpers/cpu/addBias.cpp
index 09cdf5d4e..0e6e1f777 100644
--- a/libnd4j/include/ops/declarable/helpers/cpu/addBias.cpp
+++ b/libnd4j/include/ops/declarable/helpers/cpu/addBias.cpp
@@ -28,70 +28,116 @@ namespace helpers {
 
 //////////////////////////////////////////////////////////////////////////
 template <typename X, typename Y>
-static void addBias_(NDArray& input, const NDArray& bias, const bool isNCHW) {
+static void addBias_(const NDArray& input, const NDArray& bias, NDArray &output, const bool isNCHW) {
 
-    // input  [bS, oH, oW, oC] (NHWC) or [bS, oC, oH, oW] (NCHW)
-    // bias   [oC]
+    // bias [oC]
 
-          X* inBuff   = input.bufferAsT<X>();
-    const Y* biasBuff = bias.bufferAsT<Y>();    
+    // if(input_rank == 4)
+        // input and output have same shapes: [bS, oH, oW, oC] (NHWC) or [bS, oC, oH, oW] (NCHW)
+    // if(input_rank == 5)
+        // input and output have same shapes: [bS, oD, oH, oW, oC] (NHWC) or [bS, oD, oC, oH, oW] (NCHW)
+    // else
+        // apply applyBroadCast
 
-    int bS, iC, iH, iW, oC, oH, oW;                             // batch size, input channels, input height/width, output channels, output height/width;
-    bS = input.sizeAt(0);    
-    const Nd4jLong stride0 = input.stridesOf()[0];
-    const Nd4jLong stride1 = input.stridesOf()[1];
-    const Nd4jLong stride2 = input.stridesOf()[2];
 
-    uint biasShapeInfoCast[MAX_RANK];    
-    bool canCastBias = nd4j::DataTypeUtils::castShapeInfo(bias.getShapeInfo(), biasShapeInfoCast);
-    
-    if(isNCHW) {
-        
-        oC = input.sizeAt(1);
-        oH = input.sizeAt(2);
-        oW = input.sizeAt(3);
+    const X* x = input.bufferAsT<X>();
+    const Y* y = bias.bufferAsT<Y>();
+          X* z = output.bufferAsT<X>();
 
-        const int oHoW = oH*oW;
+    const bool inOutAreSame = x == z;
 
-        PRAGMA_OMP_PARALLEL_FOR_COLLAPSE(2)
-        for (int i = 0; i < bS; ++i) {
-            for (int c = 0; c < oC; ++c) {
-                
-                auto biasOffset = shape::indexOffset(c, bias.getShapeInfo(), biasShapeInfoCast, oC, canCastBias);
-                auto inOffset = i * stride0 + c * stride1;
+    const uint bS           = output.sizeAt(0);              // batch size
+    const Nd4jLong yStrideC = bias.stridesOf()[0];
+    const Nd4jLong zStrideB = output.stridesOf()[0];
 
-                PRAGMA_OMP_SIMD
-                for (uint k = 0; k < oHoW; ++k)
-                    inBuff[inOffset + k] += static_cast<X>(biasBuff[biasOffset]);
-            }
+    if(output.rankOf() == 4) {
+
+        const uint C  = isNCHW ? output.sizeAt(1) : output.sizeAt(3);     // channels
+        const uint oH = isNCHW ? output.sizeAt(2) : output.sizeAt(1);     // height
+        const uint oW = isNCHW ? output.sizeAt(3) : output.sizeAt(2);     // width
+
+        const Nd4jLong zStrideC = isNCHW ? output.stridesOf()[1] : output.stridesOf()[3];
+        const Nd4jLong zStrideH = isNCHW ? output.stridesOf()[2] : output.stridesOf()[1];
+        const Nd4jLong zStrideW = isNCHW ? output.stridesOf()[3] : output.stridesOf()[2];
+
+        if(inOutAreSame) {
+
+            PRAGMA_OMP_PARALLEL_FOR_ARGS(collapse(4))
+            for(uint b = 0; b < bS; ++b)
+                for(uint c = 0; c < C; ++c)
+                    for(uint h = 0; h < oH ; ++h)
+                        for(uint w = 0; w < oW ; ++w)
+                            z[b*zStrideB + c*zStrideC + h*zStrideH + w*zStrideW] += static_cast<X>(y[c*yStrideC]);
+        }
+        else {
+
+            const Nd4jLong xStrideB = input.stridesOf()[0];
+            const Nd4jLong xStrideC = isNCHW ? input.stridesOf()[1] : input.stridesOf()[3];
+            const Nd4jLong xStrideH = isNCHW ? input.stridesOf()[2] : input.stridesOf()[1];
+            const Nd4jLong xStrideW = isNCHW ? input.stridesOf()[3] : input.stridesOf()[2];
+
+            PRAGMA_OMP_PARALLEL_FOR_ARGS(collapse(4))
+            for(uint b = 0; b < bS; ++b)
+                for(uint c = 0; c < C; ++c)
+                    for(uint h = 0; h < oH ; ++h)
+                        for(uint w = 0; w < oW ; ++w)
+                            z[b*zStrideB + c*zStrideC + h*zStrideH + w*zStrideW] = x[b*xStrideB + c*xStrideC + h*xStrideH + w*xStrideW] +  static_cast<X>(y[c*yStrideC]);
+        }
+    }
+    else if(output.rankOf() == 5) {
+
+        const uint C  = isNCHW ? output.sizeAt(1) : output.sizeAt(4);     // channels
+        const uint oD = isNCHW ? output.sizeAt(2) : output.sizeAt(1);     // depth
+        const uint oH = isNCHW ? output.sizeAt(3) : output.sizeAt(2);     // height
+        const uint oW = isNCHW ? output.sizeAt(4) : output.sizeAt(3);     // width
+
+        const Nd4jLong zStrideC = isNCHW ? output.stridesOf()[1] : output.stridesOf()[4];
+        const Nd4jLong zStrideD = isNCHW ? output.stridesOf()[2] : output.stridesOf()[1];
+        const Nd4jLong zStrideH = isNCHW ? output.stridesOf()[3] : output.stridesOf()[2];
+        const Nd4jLong zStrideW = isNCHW ? output.stridesOf()[4] : output.stridesOf()[3];
+
+        if(inOutAreSame) {
+
+            PRAGMA_OMP_PARALLEL_FOR_ARGS(collapse(5))
+            for(uint b = 0; b < bS; ++b)
+                for(uint c = 0; c < C; ++c)
+                    for(uint d = 0; d < oD ; ++d)
+                        for(uint h = 0; h < oH ; ++h)
+                            for(uint w = 0; w < oW ; ++w)
+                                z[b*zStrideB + c*zStrideC + d*zStrideD + h*zStrideH + w*zStrideW] += static_cast<X>(y[c*yStrideC]);
+        }
+        else {
+
+            const Nd4jLong xStrideB = input.stridesOf()[0];
+            const Nd4jLong xStrideC = isNCHW ? input.stridesOf()[1] : input.stridesOf()[4];
+            const Nd4jLong xStrideD = isNCHW ? input.stridesOf()[2] : input.stridesOf()[1];
+            const Nd4jLong xStrideH = isNCHW ? input.stridesOf()[3] : input.stridesOf()[2];
+            const Nd4jLong xStrideW = isNCHW ? input.stridesOf()[4] : input.stridesOf()[3];
+
+            PRAGMA_OMP_PARALLEL_FOR_ARGS(collapse(5))
+            for(uint b = 0; b < bS; ++b)
+                for(uint c = 0; c < C; ++c)
+                    for(uint d = 0; d < oD ; ++d)
+                        for(uint h = 0; h < oH ; ++h)
+                            for(uint w = 0; w < oW ; ++w)
+                                z[b*zStrideB + c*zStrideC + d*zStrideD + h*zStrideH + w*zStrideW] = x[b*xStrideB + c*xStrideC + d*xStrideD + h*xStrideH + w*xStrideW] + static_cast<X>(y[c*yStrideC]);
         }
     }
     else {
-        
-        oC = input.sizeAt(3);
-        oH = input.sizeAt(1);
-        oW = input.sizeAt(2);
-
-        PRAGMA_OMP_PARALLEL_FOR
-        for (int i = 0; i < bS*oH*oW; ++i) {
-
-            PRAGMA_OMP_SIMD
-            for (int c = 0; c < oC; ++c) {
-                auto biasOffset = shape::indexOffset(c, bias.getShapeInfo(), biasShapeInfoCast, oC, canCastBias);
-                inBuff[i * oC + c] += static_cast<X>(biasBuff[biasOffset]);
-            }                            
-        }
-    }        
+         const int channelDim = isNCHW ? 1 : input.rankOf() - 1;      // second or last
+         const_cast<NDArray&>(input).applyBroadcast(nd4j::broadcast::Add, {channelDim}, &bias, &output);
+    }
 }
 
 //////////////////////////////////////////////////////////////////////////
-void addBias(NDArray& input, const NDArray& bias, const bool isNCHW) {
+void addBias(nd4j::graph::Context& block, const NDArray& input, const NDArray& bias, NDArray& output, const bool isNCHW) {
 
-    BUILD_DOUBLE_SELECTOR(input.dataType(), bias.dataType(), addBias_, (input, bias, isNCHW), FLOAT_TYPES, FLOAT_TYPES);
+    // bias.rankOf() == 1 ? bias : bias.reshape(bias.ordering(), {bias.lengthOf()})
+    BUILD_DOUBLE_SELECTOR(input.dataType(), bias.dataType(), addBias_, (input, bias, output, isNCHW), FLOAT_TYPES, FLOAT_TYPES);
 }
 
 
-BUILD_DOUBLE_TEMPLATE(template void addBias_, (NDArray& input, const NDArray& bias, const bool isNCHW), FLOAT_TYPES, FLOAT_TYPES);
+BUILD_DOUBLE_TEMPLATE(template void addBias_, (const NDArray& input, const NDArray& bias, NDArray& output, const bool isNCHW), FLOAT_TYPES, FLOAT_TYPES);
 
 }
 }
diff --git a/libnd4j/include/ops/declarable/helpers/cpu/batchnorm.cpp b/libnd4j/include/ops/declarable/helpers/cpu/batchnorm.cpp
index 0c12a2896..d6c4da4a1 100644
--- a/libnd4j/include/ops/declarable/helpers/cpu/batchnorm.cpp
+++ b/libnd4j/include/ops/declarable/helpers/cpu/batchnorm.cpp
@@ -84,7 +84,7 @@ static void batchnorm_(const NDArray* input, const NDArray* mean, const NDArray*
                 const Nd4jLong end   = start + step;
 
                 // calculate offset for mean, variance, gamma, beta (all of them have the same shape)
-                auto offsetSmall = shape::indexOffset(j, meanShapeInfo, meanShapeInfoCast, lenSmall, canCastMean);
+                auto offsetSmall = shape::indexOffset(j, meanShapeInfo, meanShapeInfoCast, canCastMean);
                 // calculate offset for input and output (all of them have the same shape)
                 shape::outerArrayOffsets(inOffsets, j, inShapeInfo, meanShapeInfo, memBuff, dimsToExclude.data());
 
@@ -114,7 +114,7 @@ static void batchnorm_(const NDArray* input, const NDArray* mean, const NDArray*
                 const Nd4jLong end   = start + step;
 
                 // calculate offset for mean, variance, gamma, beta (all of them have the same shape)
-                auto offsetSmall = shape::indexOffset(j, meanShapeInfo, meanShapeInfoCast, lenSmall, canCastMean);
+                auto offsetSmall = shape::indexOffset(j, meanShapeInfo, meanShapeInfoCast, canCastMean);
                 // calculate offset for input and output (all of them have the same shape)
                 shape::outerArrayOffsets(inOffsets, j, inShapeInfo, meanShapeInfo, memBuff, dimsToExclude.data());
 
diff --git a/libnd4j/include/ops/declarable/helpers/cpu/betaInc.cpp b/libnd4j/include/ops/declarable/helpers/cpu/betaInc.cpp
index 681b4eb63..bba3e8acb 100644
--- a/libnd4j/include/ops/declarable/helpers/cpu/betaInc.cpp
+++ b/libnd4j/include/ops/declarable/helpers/cpu/betaInc.cpp
@@ -29,7 +29,7 @@ namespace helpers {
 
 ///////////////////////////////////////////////////////////////////
 // modified Lentz’s algorithm for continued fractions,
-// reference: Lentz, W.J. 1976, “Generating Bessel Functions in Mie Scattering Calculations Using Continued Fractions,”
+// reference: Lentz, W.J. 1976, “Generating Bessel Functions in Mie Scattering Calculations Using Continued Fractions”
 template <typename T>
 static T continuedFraction(const T a, const T b, const T x) {
 
@@ -122,9 +122,8 @@ static void betaIncForArray(nd4j::LaunchContext * context, const NDArray& a, con
 	int xLen = x.lengthOf();
 
     PRAGMA_OMP_PARALLEL_FOR_IF(xLen > Environment::getInstance()->elementwiseThreshold())
-	for(int i = 0; i < xLen; ++i) {
-		output.p(i, betaIncCore<T>(a.e<T>(i), b.e<T>(i), x.e<T>(i)));
-	}
+	for(int i = 0; i < xLen; ++i)
+		output.t<T>(i) = betaIncCore<T>(a.t<T>(i), b.t<T>(i), x.t<T>(i));
 }
 
 ///////////////////////////////////////////////////////////////////
diff --git a/libnd4j/include/ops/declarable/helpers/cpu/convolutions.cpp b/libnd4j/include/ops/declarable/helpers/cpu/convolutions.cpp
index a03e1f7ac..93d00220e 100644
--- a/libnd4j/include/ops/declarable/helpers/cpu/convolutions.cpp
+++ b/libnd4j/include/ops/declarable/helpers/cpu/convolutions.cpp
@@ -288,7 +288,7 @@ namespace nd4j {
             //----- add biases if required -----//
             if(bias)
                 // output->applyBroadcast(broadcast::Add, {indIOioC}, bias);
-                helpers::addBias(*output, *bias, isNCHW);
+                helpers::addBias(block, *output, *bias, *output, isNCHW);
 
             if(!isNCHW)
                 delete input;
@@ -370,7 +370,7 @@ namespace nd4j {
 
 //////////////////////////////////////////////////////////////////////////
         template <typename X, typename Y>
-        static void depthwiseConv2d_(const NDArray* input, const NDArray* weights, const NDArray* bias, NDArray* output, const int kH, const int kW, const int sH, const int sW, int pH, int pW, const int dH, const int dW, const int isSameMode, const int isNCHW) {
+        static void depthwiseConv2d_(nd4j::graph::Context& block, const NDArray* input, const NDArray* weights, const NDArray* bias, NDArray* output, const int kH, const int kW, const int sH, const int sW, int pH, int pW, const int dH, const int dW, const int isSameMode, const int isNCHW) {
 
             // input     [bS, iH, iW, iC] (NHWC) or [bS, iC, iH, iW] (NCHW)
             // weights   [kH, kW, iC, mC] always
@@ -417,7 +417,8 @@ namespace nd4j {
             MmulHelper::tensorDot(&columns, weights, &outputReshaped, modifColumns, {{2,0,1,3},{iC,kH*kW,mC}}, modifOutput);              // [iC, bS*oH*oW, kW*kH] x [iC, kH*kW, mC] = [iC, bS*oH*oW, mC]
 
             if(bias)
-                output->applyBroadcast(broadcast::Add, {indIOioC}, bias);
+                // output->applyBroadcast(broadcast::Add, {indIOioC}, bias);
+                helpers::addBias(block, *output, *bias, *output, isNCHW);
 
             if(!isNCHW)
                 delete input;
@@ -1661,7 +1662,7 @@ namespace nd4j {
             BUILD_SINGLE_SELECTOR_TWICE(input->dataType(), conv2dBP_, (block, input, weights, bias, gradO, gradI, gradW, gradB, kH, kW, sH, sW, pH, pW, dH, dW, isSameMode, isNCHW), FLOAT_TYPES);
         }
         void ConvolutionUtils::depthwiseConv2d(nd4j::graph::Context& block, const NDArray* input, const NDArray* weights, const NDArray* bias, NDArray* output, const int kH, const int kW, const int sH, const int sW, int pH, int pW, const int dH, const int dW, const int isSameMode, const int isNCHW) {
-            BUILD_SINGLE_SELECTOR_TWICE(input->dataType(), depthwiseConv2d_, (input, weights, bias, output, kH, kW, sH, sW, pH, pW, dH, dW, isSameMode, isNCHW), FLOAT_TYPES);
+            BUILD_SINGLE_SELECTOR_TWICE(input->dataType(), depthwiseConv2d_, (block, input, weights, bias, output, kH, kW, sH, sW, pH, pW, dH, dW, isSameMode, isNCHW), FLOAT_TYPES);
         }
         void ConvolutionUtils::depthwiseConv2dBP(nd4j::graph::Context& block, const NDArray* input, const NDArray* weights, const NDArray* bias, const NDArray* gradO, NDArray* gradI, NDArray* gradW, NDArray* gradB, const int kH, const int kW, const int sH, const int sW, int pH, int pW, const int dH, const int dW, const int isSameMode, const int isNCHW) {
             BUILD_SINGLE_SELECTOR_TWICE(input->dataType(), depthwiseConv2dBP_, (input, weights, bias, gradO, gradI, gradW, gradB, kH, kW, sH, sW, pH, pW, dH, dW, isSameMode, isNCHW), FLOAT_TYPES);
diff --git a/libnd4j/include/ops/declarable/helpers/cpu/flatten.cpp b/libnd4j/include/ops/declarable/helpers/cpu/flatten.cpp
index 6a08064fc..30d4d3f7a 100644
--- a/libnd4j/include/ops/declarable/helpers/cpu/flatten.cpp
+++ b/libnd4j/include/ops/declarable/helpers/cpu/flatten.cpp
@@ -37,24 +37,16 @@ namespace nd4j {
                     cOffset += inputs[e]->lengthOf();
                 }
 
-                Nd4jLong xCoord[MAX_RANK];
-
                 // actually transferring data
                 for (int e = 0; e < numArrays; e++) {
                     auto z = reinterpret_cast<T *>(output->bufferWithOffset(offsets[e]));
 
                     auto xBuffer = inputs[e]->bufferAsT<T>();
                     auto xShapeInfo = inputs[e]->shapeInfo();
-                    auto xShape = shape::shapeOf(xShapeInfo);
-                    auto xStride = shape::stride(xShapeInfo);
-                    auto xRank = shape::rank(xShapeInfo);
                     auto xLength = inputs[e]->lengthOf();
-                    
-                    for (uint i = 0; i < xLength; i++) {
-                        shape::index2coords(xRank, xShape, i, xLength, xCoord, order);
-                        auto xOffset = shape::getOffset(0, xShape, xStride, xCoord, xRank);
-                        z[i] = xBuffer[xOffset];
-                    }                    
+
+                    for (uint i = 0; i < xLength; i++)
+                        z[i] = xBuffer[getIndexOffsetOrdered(i, xShapeInfo, order)];
                 }
             }
 
diff --git a/libnd4j/include/ops/declarable/helpers/cpu/ismax.cpp b/libnd4j/include/ops/declarable/helpers/cpu/ismax.cpp
index af4e96e2e..def210457 100644
--- a/libnd4j/include/ops/declarable/helpers/cpu/ismax.cpp
+++ b/libnd4j/include/ops/declarable/helpers/cpu/ismax.cpp
@@ -30,7 +30,7 @@ namespace helpers {
 
 template <typename X, typename Z>
 static void ismax_(const NDArray* input, NDArray* output, const std::vector<int>& dimensions) {
-                        
+
     if (input->isVector()) {
         int dimensionsLength = dimensions.size();
         int length = input->lengthOf();
@@ -169,7 +169,7 @@ static void ismax_(const NDArray* input, NDArray* output, const std::vector<int>
                         for (int i = 0; i < tadLength; i++) {
                             rZ[i] = maxIdx == i ? (Z) 1 : (Z) 0;
                         }
-                    } 
+                    }
                     else if (tadEWS > 1 && zEWS > 1) {
                         for (int i = 0; i < tadLength; i++) {
                             if (rX[i * tadEWS] > maxValue) {
@@ -184,7 +184,7 @@ static void ismax_(const NDArray* input, NDArray* output, const std::vector<int>
                         }
                     } else {
                         for (int i = 0; i < tadLength; i++) {
-                            auto xOffset = shape::getIndexOffset(i, tadShapeShapeInfo, tadLength);
+                            auto xOffset = shape::getIndexOffset(i, tadShapeShapeInfo);
                             if (rX[xOffset] > maxValue) {
                                 maxIdx = i;
                                 maxValue = rX[xOffset];
@@ -193,7 +193,7 @@ static void ismax_(const NDArray* input, NDArray* output, const std::vector<int>
 
                         PRAGMA_OMP_SIMD
                         for (int i = 0; i < tadLength; i++) {
-                            auto zOffset = shape::getIndexOffset(i, tadPackZ.primaryShapeInfo(), tadLength);
+                            auto zOffset = shape::getIndexOffset(i, tadPackZ.primaryShapeInfo());
                             rZ[zOffset] = maxIdx == i ? (Z) 1 : (Z) 0;
                         }
                     }
diff --git a/libnd4j/include/ops/declarable/helpers/cpu/lup.cpp b/libnd4j/include/ops/declarable/helpers/cpu/lup.cpp
index 1e3c798e2..76817078b 100644
--- a/libnd4j/include/ops/declarable/helpers/cpu/lup.cpp
+++ b/libnd4j/include/ops/declarable/helpers/cpu/lup.cpp
@@ -50,13 +50,13 @@ namespace helpers {
         int n = inputMatrix->rows();
         invertedMatrix->assign(0.f);
 
-        PRAGMA_OMP_PARALLEL_FOR_IF(n > Environment::getInstance()->elementwiseThreshold())
+       // PRAGMA_OMP_PARALLEL_FOR_IF(n > Environment::getInstance()->elementwiseThreshold())
         for (int i = 0; i < n; i++)
             invertedMatrix->p(i, i, 1.0f);
 
         if (inputMatrix->isIdentityMatrix()) return;
 
-        PRAGMA_OMP_PARALLEL_FOR_IF(n > Environment::getInstance()->elementwiseThreshold())
+        //PRAGMA_OMP_PARALLEL_FOR_IF(n > Environment::getInstance()->elementwiseThreshold())
         for (int i = 1; i < n; i++)
             invertedMatrix->t<T>(i, i - 1) = -inputMatrix->t<T>(i, i - 1);
 
@@ -83,11 +83,11 @@ namespace helpers {
             return;
         }
 
-        PRAGMA_OMP_PARALLEL_FOR_IF(n > Environment::getInstance()->elementwiseThreshold())
+        //PRAGMA_OMP_PARALLEL_FOR_IF(n > Environment::getInstance()->elementwiseThreshold())
         for (int i = 0; i < n; i++)
             invertedMatrix->t<T>(i, i) /= inputMatrix->t<T>(i, i);
 
-        PRAGMA_OMP_PARALLEL_FOR_IF(n > Environment::getInstance()->elementwiseThreshold())
+        //PRAGMA_OMP_PARALLEL_FOR_IF(n > Environment::getInstance()->elementwiseThreshold())
         for (int i = 0; i < n - 1; i++)
             invertedMatrix->t<T>(i, i + 1) -= (inputMatrix->t<T>(i, i + 1) * invertedMatrix->t<T>(i + 1, i + 1) / inputMatrix->t<T>(i, i));
 
@@ -124,7 +124,7 @@ namespace helpers {
         for(int i = 0; i < rowNum; i++ ) {
             pivotValue = T(0.0);
             pivot = -1;
-            PRAGMA_OMP_PARALLEL_FOR //_ARGS(firstprivate(pivot,pivotValue))
+            //PRAGMA_OMP_PARALLEL_FOR //_ARGS(firstprivate(pivot,pivotValue))
             for(int rowCounter = i; rowCounter < rowNum; rowCounter++ ) {
                 if (nd4j::math::nd4j_abs(compoundMatrix.t<T>(rowCounter, i)) > pivotValue) {
                     pivotValue = nd4j::math::nd4j_abs(compoundMatrix.t<T>(rowCounter, i));
@@ -140,7 +140,7 @@ namespace helpers {
 
                 for( int j = i + 1; j < rowNum; j++ ) {
                     compoundMatrix.t<T>(j, i) /= compoundMatrix.t<T>(i, i);
-                    PRAGMA_OMP_PARALLEL_FOR
+                    //PRAGMA_OMP_PARALLEL_FOR
                     for( int k = i + 1; k < rowNum; k++ ) {
                         compoundMatrix.t<T>(j, k) -= compoundMatrix.t<T>(j, i) * compoundMatrix.t<T>(i, k);
                     }
diff --git a/libnd4j/include/ops/declarable/helpers/cpu/matrixSetDiag.cpp b/libnd4j/include/ops/declarable/helpers/cpu/matrixSetDiag.cpp
index e974755ac..9a2034fd0 100644
--- a/libnd4j/include/ops/declarable/helpers/cpu/matrixSetDiag.cpp
+++ b/libnd4j/include/ops/declarable/helpers/cpu/matrixSetDiag.cpp
@@ -52,14 +52,14 @@ void matrixSetDiag_(const NDArray& input, const NDArray& diagonal, NDArray& outp
     PRAGMA_OMP_PARALLEL_FOR_ARGS(firstprivate(coords))
     for (Nd4jLong i = 0; i < xLen; ++i) {
 
-        shape::index2coords(xRank, xShapeInfo + 1, i, xLen, coords.data());
+        shape::index2coords(i, xShapeInfo, coords.data());
 
-        const auto xOffset = shape::getOffset(0, xShapeInfo + 1, xShapeInfo + xRank + 1, coords.data(), xRank);
-        const auto zOffset = areSameOffsets ? xOffset : shape::getOffset(0, zShapeInfo + 1, zShapeInfo + xRank + 1, coords.data(), xRank);
+        const auto xOffset = shape::getOffset(xShapeInfo, coords.data());
+        const auto zOffset = areSameOffsets ? xOffset : shape::getOffset(zShapeInfo, coords.data());
 
         // condition to be on diagonal of innermost matrix
         if(coords[xRank - 2] == coords[xRank - 1])
-            z[zOffset] = y[shape::getOffset(0, yShapeInfo + 1, yShapeInfo + xRank, coords.data(), xRank - 1)];
+            z[zOffset] = y[shape::getOffset(yShapeInfo, coords.data())];
         else
             z[zOffset] = zeroPad ? static_cast<T>(0) : x[xOffset];
     }
diff --git a/libnd4j/include/ops/declarable/helpers/cpu/one_hot.cpp b/libnd4j/include/ops/declarable/helpers/cpu/one_hot.cpp
index ea273d33b..a83518899 100644
--- a/libnd4j/include/ops/declarable/helpers/cpu/one_hot.cpp
+++ b/libnd4j/include/ops/declarable/helpers/cpu/one_hot.cpp
@@ -73,12 +73,12 @@ namespace nd4j {
                         if (idx < 0 || idx >= tLen) {
                             PRAGMA_OMP_SIMD
                             for (unsigned int t = 0; t < tLen; t++) {
-                                cO[shape::getIndexOffset(t, tadPack.primaryShapeInfo(), tLen)] = zero;
+                                cO[shape::getIndexOffset(t, tadPack.primaryShapeInfo())] = zero;
                             }
                         } else {
                             PRAGMA_OMP_SIMD
                             for (unsigned int t = 0; t < tLen; t++) {
-                                cO[shape::getIndexOffset(t, tadPack.primaryShapeInfo(), tLen)] = idx == t ? one : zero;
+                                cO[shape::getIndexOffset(t, tadPack.primaryShapeInfo())] = idx == t ? one : zero;
                             }
                         }
                     }
diff --git a/libnd4j/include/ops/declarable/helpers/cpu/prefix.cpp b/libnd4j/include/ops/declarable/helpers/cpu/prefix.cpp
index bd14fbd8d..f46346876 100644
--- a/libnd4j/include/ops/declarable/helpers/cpu/prefix.cpp
+++ b/libnd4j/include/ops/declarable/helpers/cpu/prefix.cpp
@@ -53,8 +53,8 @@ namespace nd4j {
 
                         for (Nd4jLong e = length - 1; e >= 0; --e) {
 
-                            auto xOffset = shape::getIndexOffset(e, xShapeInfo, length);
-                            auto zOffset = shape::getIndexOffset(e, zShapeInfo, length);
+                            auto xOffset = shape::getIndexOffset(e, xShapeInfo);
+                            auto zOffset = shape::getIndexOffset(e, zShapeInfo);
                             sum = op == scalar::Add ? simdOps::Add<T, T, T>::op(sum, x[xOffset]) : simdOps::Multiply<T, T, T>::op(sum, x[xOffset]);
 
                             if (!exclusive)
@@ -83,8 +83,8 @@ namespace nd4j {
 
                         for (int e = 0; e < length; e++) {
 
-                            auto xOffset = shape::getIndexOffset(e, xShapeInfo, length);
-                            auto zOffset = shape::getIndexOffset(e, zShapeInfo, length);
+                            auto xOffset = shape::getIndexOffset(e, xShapeInfo);
+                            auto zOffset = shape::getIndexOffset(e, zShapeInfo);
                             sum = op == scalar::Add ? simdOps::Add<T, T, T>::op(sum, x[xOffset]) : simdOps::Multiply<T, T, T>::op(sum, x[xOffset]);
 
                             if (!exclusive)
diff --git a/libnd4j/include/ops/declarable/helpers/cpu/reverse.cpp b/libnd4j/include/ops/declarable/helpers/cpu/reverse.cpp
index 0922a1248..83deeca88 100644
--- a/libnd4j/include/ops/declarable/helpers/cpu/reverse.cpp
+++ b/libnd4j/include/ops/declarable/helpers/cpu/reverse.cpp
@@ -60,7 +60,7 @@ static void reverseArray(nd4j::LaunchContext * context, void *vinArr, Nd4jLong *
 //                        inArr[e] = inArr[idx];
 //                        inArr[idx] = tmp;
                     }
-                } 
+                }
                 else if (inEWS > 1) {
                     PRAGMA_OMP_PARALLEL_FOR
                     for (Nd4jLong e = 0; e < numOfElemsToReverse / 2; e++) {
@@ -71,19 +71,19 @@ static void reverseArray(nd4j::LaunchContext * context, void *vinArr, Nd4jLong *
 //                        inArr[idx1] = tmp;
                         swap(inArr, idx1, idx2);
                     }
-                } 
+                }
                 else {
 
                     PRAGMA_OMP_PARALLEL_FOR
                     for (Nd4jLong e = 0; e < numOfElemsToReverse / 2; e++) {
-                   
-                        auto inOffset  = shape::getIndexOffset(e, inShapeBuffer, inLength);
-                        auto outOffset = shape::getIndexOffset(sLength - e, inShapeBuffer, inLength);
+
+                        auto inOffset  = shape::getIndexOffset(e, inShapeBuffer);
+                        auto outOffset = shape::getIndexOffset(sLength - e, inShapeBuffer);
                         //outArr[outOffset] = inArr[inOffset];
                         swap(outArr, inOffset, outOffset);
                     }
                 }
-            } 
+            }
             else {
                 // single step phase here
                 auto outEWS = shape::elementWiseStride(outShapeBuffer);
@@ -92,15 +92,15 @@ static void reverseArray(nd4j::LaunchContext * context, void *vinArr, Nd4jLong *
                 if (inEWS == 1 && outEWS == 1 && inOrder == outOrder) {
 
                     PRAGMA_OMP_PARALLEL_FOR
-                    for (Nd4jLong e = 0; e < numOfElemsToReverse; e++) 
-                        outArr[sLength - e] = inArr[e];                    
+                    for (Nd4jLong e = 0; e < numOfElemsToReverse; e++)
+                        outArr[sLength - e] = inArr[e];
 
                     if(inLength != numOfElemsToReverse) {
                         PRAGMA_OMP_PARALLEL_FOR
                         for (Nd4jLong e = numOfElemsToReverse; e < inLength; e++)
                             outArr[e] = inArr[e];
                     }
-                } 
+                }
                 else if (inEWS >= 1 && outEWS >= 1 && inOrder == outOrder) {
 
                     PRAGMA_OMP_PARALLEL_FOR
@@ -112,14 +112,14 @@ static void reverseArray(nd4j::LaunchContext * context, void *vinArr, Nd4jLong *
                         for (Nd4jLong e = numOfElemsToReverse; e < inLength; e++)
                             outArr[e * outEWS] = inArr[e * inEWS];
                     }
-                } 
+                }
                 else {
 
                     PRAGMA_OMP_PARALLEL_FOR
                     for (Nd4jLong e = 0; e < numOfElemsToReverse; e++) {
 
-                        auto inOffset = shape::getIndexOffset(e, inShapeBuffer, inLength);
-                        auto outOffset = shape::getIndexOffset(sLength - e, outShapeBuffer, outLength);
+                        auto inOffset = shape::getIndexOffset(e, inShapeBuffer);
+                        auto outOffset = shape::getIndexOffset(sLength - e, outShapeBuffer);
                         outArr[outOffset] = inArr[inOffset];
                     }
 
@@ -128,9 +128,9 @@ static void reverseArray(nd4j::LaunchContext * context, void *vinArr, Nd4jLong *
                         PRAGMA_OMP_PARALLEL_FOR
                         for (Nd4jLong e = numOfElemsToReverse; e < inLength; e++) {
 
-                            auto inOffset  = shape::getIndexOffset(e, inShapeBuffer, inLength);
-                            auto outOffset = shape::getIndexOffset(e, outShapeBuffer, outLength);
-                            outArr[outOffset] = inArr[inOffset];        
+                            auto inOffset  = shape::getIndexOffset(e, inShapeBuffer);
+                            auto outOffset = shape::getIndexOffset(e, outShapeBuffer);
+                            outArr[outOffset] = inArr[inOffset];
                         }
                     }
                 }
@@ -151,7 +151,7 @@ static void _reverseSequence(nd4j::LaunchContext * context, const NDArray* input
             helpers::reverseArray<T>(context, const_cast<NDArray*>(input)->getBuffer(), const_cast<NDArray*>(input)->getShapeInfo(), output->getBuffer(), output->getShapeInfo(), seqLengths->e<int>(0));
     }
     else {
-            
+
         if(seqDim > batchDim)
             --seqDim;
 
@@ -163,7 +163,7 @@ static void _reverseSequence(nd4j::LaunchContext * context, const NDArray* input
         for(int i = 0; i < inSubArrsSet->size(); ++i) {
 
             Nd4jLong numOfElemsToReverse = seqLengths->e<Nd4jLong>(i);
-        
+
             if(numOfElemsToReverse == 0 || numOfElemsToReverse == 1) {
                 outSubArrsSet->at(i)->assign(inSubArrsSet->at(i));
             }
@@ -172,7 +172,7 @@ static void _reverseSequence(nd4j::LaunchContext * context, const NDArray* input
                 auto outInnerSet = outSubArrsSet->at(i)->allTensorsAlongDimension({seqDim});
                 for(int j = 0; j < inInnerSet->size(); ++j)
                     helpers::reverseArray<T>(context, inInnerSet->at(j)->getBuffer(), inInnerSet->at(j)->getShapeInfo(), outInnerSet->at(j)->getBuffer(), outInnerSet->at(j)->getShapeInfo(), numOfElemsToReverse);
-            
+
                 delete inInnerSet;
                 delete outInnerSet;
             }
@@ -195,12 +195,12 @@ void reverse(nd4j::LaunchContext * context, const NDArray* input, NDArray* outpu
 
     auto listOut = output->allTensorsAlongDimension(dimensions);
     auto listIn  = input->allTensorsAlongDimension(dimensions);
-       
+
     NDArray *subArrIn, *subArrOut;
 
     for(int i = 0; i < listIn->size(); ++i) {               // listIn->size() = listOut->size()
         subArrIn   = listIn->at(i);
-        subArrOut  = listOut->at(i);        
+        subArrOut  = listOut->at(i);
         BUILD_SINGLE_SELECTOR(input->dataType(), helpers::reverseArray, (context, subArrIn->getBuffer(), subArrIn->getShapeInfo(), subArrOut->getBuffer(), subArrOut->getShapeInfo()), LIBND4J_TYPES);
     }
 
diff --git a/libnd4j/include/ops/declarable/helpers/cpu/roll.cpp b/libnd4j/include/ops/declarable/helpers/cpu/roll.cpp
index da3cb3259..b3b65f816 100644
--- a/libnd4j/include/ops/declarable/helpers/cpu/roll.cpp
+++ b/libnd4j/include/ops/declarable/helpers/cpu/roll.cpp
@@ -43,7 +43,7 @@ namespace helpers {
             int remainShift = fullLen % actualShift; 
             
             // stage 1) swap last actualShift elements with first ones.
-            PRAGMA_OMP_PARALLEL_FOR_IF(actualShift > Environment::getInstance()->elementwiseThreshold())
+            //PRAGMA_OMP_PARALLEL_FOR //_IF(actualShift > Environment::getInstance()->elementwiseThreshold())
             for (int e = 0; e < actualShift; ++e) {
                 int sourceIndex = fullLen - actualShift + e;
 
@@ -56,7 +56,7 @@ namespace helpers {
             }
 
             // stage 2) swap swapped actualShift elements with rest remainShiftCount times.
-            PRAGMA_OMP_PARALLEL_FOR_IF(shiftCount > Environment::getInstance()->tadThreshold())
+            //PRAGMA_OMP_PARALLEL_FOR //_IF(shiftCount > Environment::getInstance()->tadThreshold())
             for (int count = 1; count < shiftCount; ++count) {
                 for (int e = 0; e < actualShift; ++e) {
                     int destinationIndex = fullLen - (count + 1) * actualShift + e;
@@ -91,7 +91,7 @@ namespace helpers {
             output->assign(input);
 
         auto source = output; //input;
-        for (auto i = 0; i < axes.size(); i++) {
+        for (size_t i = 0; i < axes.size(); i++) {
             int axe = axes[i];
             if (axe == source->rankOf() - 1) {// last dimension
                 std::unique_ptr<ResultSet> listOfTensors(source->allTensorsAlongDimension({axe}));
@@ -115,7 +115,7 @@ namespace helpers {
 
                 std::unique_ptr<ResultSet> listOfTensors(source->allTensorsAlongDimension({dims}));
                 std::unique_ptr<ResultSet> listOfOutTensors(output->allTensorsAlongDimension({dims}));
-            
+                //
                 int fullLen = listOfTensors->size();
                 int sizeAt = input->sizeAt(axe);
 
diff --git a/libnd4j/include/ops/declarable/helpers/cpu/s_t_b.cpp b/libnd4j/include/ops/declarable/helpers/cpu/s_t_b.cpp
index cc97e3c5b..5b4c44874 100644
--- a/libnd4j/include/ops/declarable/helpers/cpu/s_t_b.cpp
+++ b/libnd4j/include/ops/declarable/helpers/cpu/s_t_b.cpp
@@ -116,15 +116,15 @@ static void batchToSpaceND_(const NDArray& input, const NDArray& crop, NDArray&
 
     for (Nd4jLong i = 0; i < zLen; ++i) {
 
-        shape::index2coords(rank, output.shapeOf(), i, zLen, coords.data());
+        shape::index2coords(i, output.getShapeInfo(), coords.data());
 
-        const auto zOffset = shape::getOffset(0, output.shapeOf(), output.stridesOf(), coords.data(), rank);
+        const auto zOffset = shape::getOffset(output.getShapeInfo(), coords.data());
 
         // evaluate spatial coordinates for x
         for(uint j = 1; j <= numOfSpatialDims; ++j)
             coords[j] += crop.e<uint>(j - 1, 0);       // add crop left
 
-        z[zOffset] = x[shape::getOffset(0, input.shapeOf(), input.stridesOf(), coords.data(), rank)];
+        z[zOffset] = x[shape::getOffset(input.getShapeInfo(), coords.data())];
     }
 }
 
@@ -298,9 +298,9 @@ static void spaceToBatchND_(const NDArray& input, const NDArray& padding, NDArra
     PRAGMA_OMP_PARALLEL_FOR_ARGS(schedule(guided) firstprivate(coords))
     for (Nd4jLong i = 0; i < zLen; ++i) {
 
-        shape::index2coords(rank, output.shapeOf(), i, zLen, coords.data());
+        shape::index2coords(i, output.getShapeInfo(), coords.data());
 
-        const auto zOffset = shape::getOffset(0, output.shapeOf(), output.stridesOf(), coords.data(), rank);
+        const auto zOffset = shape::getOffset(output.getShapeInfo(), coords.data());
 
         bool within = true;
 
@@ -318,7 +318,7 @@ static void spaceToBatchND_(const NDArray& input, const NDArray& padding, NDArra
         }
 
         if(within)
-            z[zOffset] = x[shape::getOffset(0, input.shapeOf(), input.stridesOf(), coords.data(), rank)];
+            z[zOffset] = x[shape::getOffset(input.getShapeInfo(), coords.data())];
         else
             z[zOffset] = 0.f;
     }
diff --git a/libnd4j/include/ops/declarable/helpers/cpu/transforms.cpp b/libnd4j/include/ops/declarable/helpers/cpu/transforms.cpp
index a92e6713b..9e04ed4df 100644
--- a/libnd4j/include/ops/declarable/helpers/cpu/transforms.cpp
+++ b/libnd4j/include/ops/declarable/helpers/cpu/transforms.cpp
@@ -178,8 +178,6 @@ void pad_(const int mode, const NDArray& input, const NDArray& paddings, NDArray
 
     const Nd4jLong* xShape  = input.shapeOf();
     const Nd4jLong* zShape  = output.shapeOf();
-    const Nd4jLong* xStride = input.stridesOf();
-    const Nd4jLong* zStride = output.stridesOf();
 
     const int rank = input.rankOf();  // both input and output have the same rank
     const int rankMinusOne = rank - 1;
@@ -195,8 +193,8 @@ void pad_(const int mode, const NDArray& input, const NDArray& paddings, NDArray
         PRAGMA_OMP_PARALLEL_FOR_ARGS(firstprivate(coords))
         for(uint i = 0; i < zLen; ++i) {
 
-            shape::index2coords(rank, zShape, i, zLen, coords.data());
-            const auto zOffset = shape::getOffset(0, zShape, zStride, coords.data(), rank);
+            shape::index2coords(i, output.getShapeInfo(), coords.data());
+            const auto zOffset = shape::getOffset(output.getShapeInfo(), coords.data());
 
             bool within = true;
             for(int j = rankMinusOne; j >= 0; --j) {
@@ -207,7 +205,7 @@ void pad_(const int mode, const NDArray& input, const NDArray& paddings, NDArray
             }
 
             if(within)
-                z[zOffset] = x[shape::getOffset(0, xShape, xStride, coords.data(), rank)];
+                z[zOffset] = x[shape::getOffset(input.getShapeInfo(), coords.data())];
             else
                 z[zOffset] = padVal;
         }
@@ -220,8 +218,8 @@ void pad_(const int mode, const NDArray& input, const NDArray& paddings, NDArray
         PRAGMA_OMP_PARALLEL_FOR_ARGS(firstprivate(coords))
         for(uint i = 0; i < zLen; ++i) {
 
-            shape::index2coords(rank, zShape, i, zLen, coords.data());
-            const auto zOffset = shape::getOffset(0, zShape, zStride, coords.data(), rank);
+            shape::index2coords(i, output.getShapeInfo(), coords.data());
+            const auto zOffset = shape::getOffset(output.getShapeInfo(), coords.data());
 
             for(int j = rankMinusOne; j >= 0; --j) {
 
@@ -231,7 +229,7 @@ void pad_(const int mode, const NDArray& input, const NDArray& paddings, NDArray
                 else if(coords[j] >= xShape[j]) coords[j] = 2 * xShape[j] - coords[j] - shift2; // means fill from right
             }
 
-            const auto xOffset = shape::getOffset(0, xShape, xStride, coords.data(), rank);
+            const auto xOffset = shape::getOffset(input.getShapeInfo(), coords.data());
             z[zOffset] = x[xOffset];
         }
     }
@@ -580,9 +578,9 @@ static void gatherND_(NDArray& input, NDArray& indices, NDArray& output) {
             xCoordStart = coords.data();
         }
 
-        shape::index2coords(zRank, output.shapeOf(), i, zLen, zCoordStart);
+        shape::index2coords(i, output.getShapeInfo(), zCoordStart);
 
-        const auto zOffset = shape::getOffset(0, output.shapeOf(), output.stridesOf(), zCoordStart, zRank);
+        const auto zOffset = shape::getOffset(output.getShapeInfo(), zCoordStart);
 
         // last y coordinate
         uint coordToRestore;
@@ -590,7 +588,7 @@ static void gatherND_(NDArray& input, NDArray& indices, NDArray& output) {
             coordToRestore = static_cast<uint>(zCoordStart[yRank - 1]);
 
         zCoordStart[yRank - 1] = 0;
-        const auto yOffset = shape::getOffset(0, indices.shapeOf(), indices.stridesOf(), zCoordStart, yRank);
+        const auto yOffset = shape::getOffset(indices.getShapeInfo(), zCoordStart);
 
         //restore z coordinate
         if(yLastDim != xRank)
@@ -600,7 +598,7 @@ static void gatherND_(NDArray& input, NDArray& indices, NDArray& output) {
         for(uint j = 0; j < yLastDim; ++j)
             xCoordStart[j] = y[yOffset + j * indices.stridesOf()[yRank - 1]];   // last stride
 
-        const auto xOffset = shape::getOffset(0, input.shapeOf(), input.stridesOf(), xCoordStart, xRank);
+        const auto xOffset = shape::getOffset(input.getShapeInfo(), xCoordStart);
 
         z[zOffset] = x[xOffset];
     }
@@ -957,26 +955,27 @@ void clipByNorm(nd4j::LaunchContext * context, NDArray& input, NDArray& output,
 
     template <typename T>
     static void clipByGlobalNorm_(std::vector<NDArray*> const& inputs, double clipNorm, nd4j::memory::Workspace* workspace, std::vector<NDArray*>& outputs, bool isInplace) {
-        NDArray globalNorm = NDArrayFactory::create<T>(0, inputs[0]->getContext()); //sqrt(sum([l2norm(t)**2 for t in t_list]))
-        PRAGMA_OMP_PARALLEL_FOR
+        T globalNorm = 0; //NDArrayFactory::create<T>(0, inputs[0]->getContext()); //sqrt(sum([l2norm(t)**2 for t in t_list]))
+//        PRAGMA_OMP_PARALLEL_FOR_SIMD_REDUCTION(sumT : globalNorm)
         for (size_t i = 0; i < inputs.size(); i++) {
             auto input = inputs[i];
             auto l2norm = input->reduceNumber(reduce::Norm2);
-            globalNorm += l2norm * l2norm;
+            globalNorm += l2norm.t<T>(0) * l2norm.t<T>(0);
         }
 
-        globalNorm.applyTransform(transform::Sqrt, nullptr, nullptr);// = nd4j::math::nd4j_sqrt(globalNorm);
-        outputs[inputs.size()]->p(0, globalNorm);
+        //globalNorm.applyTransform(transform::Sqrt, nullptr, nullptr);// = nd4j::math::nd4j_sqrt(globalNorm);
+        auto normS = nd4j::math::nd4j_sqrt<T,T>(globalNorm);
+        outputs[inputs.size()]->p(0, normS);
 
-        const T factor = clipNorm / globalNorm.e<T>(0);
+        const T factor = clipNorm / normS;
 
-        PRAGMA_OMP_PARALLEL_FOR
+//        PRAGMA_OMP_PARALLEL_FOR
         for (size_t e = 0; e < inputs.size(); e++) {
             // all-reduce
             auto input = inputs[e];
             auto output = outputs[e];
 
-            if (globalNorm.e<double>(0) <= clipNorm) {
+            if (normS <= clipNorm) {
                 output->assign(input);
             }
             else {
@@ -1171,7 +1170,7 @@ static void mirrorPad_(const NDArray& input, const NDArray& paddings, NDArray& o
         PRAGMA_OMP_PARALLEL_FOR_ARGS(firstprivate(inIdx, outIdx))
         for(int i = 0; i < outLen; ++i) {
 
-            shape::index2coords(rank, output.shapeOf(), i, outIdx.data());
+            shape::index2coords(i, output.getShapeInfo(), outIdx.data());
 
             for(int j = 0; j < rank; ++j) {
 
@@ -1190,8 +1189,8 @@ static void mirrorPad_(const NDArray& input, const NDArray& paddings, NDArray& o
                     inIdx[j] = len - outIdx[j];
             }
 
-            auto outOffset = shape::getOffset(0, output.shapeOf(), output.stridesOf(), outIdx.data(), rank);
-            auto inOffset  = shape::getOffset(0, input.shapeOf(),  input.stridesOf(),  inIdx.data(),  rank);
+            auto outOffset = shape::getOffset(output.getShapeInfo(), outIdx.data());
+            auto inOffset  = shape::getOffset(input.getShapeInfo(), inIdx.data());
             reinterpret_cast<T*>(output.buffer())[outOffset] = reinterpret_cast<T*>(input.getBuffer())[inOffset];
         }
     }
@@ -1258,7 +1257,7 @@ static void tileBP_(const NDArray& gradO /*input*/, NDArray& gradI /*output*/, c
         for(Nd4jLong i=0;  i<gradOLen; ++i) {
 
             auto fidx = shape::subArrayIndex(i, gradO.getShapeInfo(), gradI.getShapeInfo());
-            gradI.p(fidx, gradI.e<T>(fidx) + gradOBuff[shape::getIndexOffset(i, gradO.getShapeInfo(), gradOLen)]);
+            gradI.p(fidx, gradI.e<T>(fidx) + gradOBuff[shape::getIndexOffset(i, gradO.getShapeInfo())]);
         }
     }
 }
diff --git a/libnd4j/include/ops/declarable/helpers/cuda/BarnesHutTsne.cu b/libnd4j/include/ops/declarable/helpers/cuda/BarnesHutTsne.cu
index 094dacf56..fabe6800a 100644
--- a/libnd4j/include/ops/declarable/helpers/cuda/BarnesHutTsne.cu
+++ b/libnd4j/include/ops/declarable/helpers/cuda/BarnesHutTsne.cu
@@ -23,7 +23,12 @@
 namespace nd4j {
 namespace ops {
 namespace helpers {
-
+////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
+// count rows kernel - count input pRows and pCols and put result onto pRowCounts
+// pRowCounts - array of ints, with length N
+// pRows - array of ints with length N, vals from 0 to N-1
+// pCols - array of ints with length < N and vals between 0 and max(pRows)
+//
     static __global__ void countRowsKernel(int* pRowCounts, int const* pRows, int const* pCols, Nd4jLong N) {
         auto start = blockIdx.x * blockDim.x;
         auto step = blockDim.x * gridDim.x;
@@ -32,19 +37,22 @@ namespace helpers {
             int end = pRows[n + 1];//rowP->e<int>(n + 1);
             for (int i = begin; i < end; i++) {
                 bool present = false;
+                // loop between near pRows
                 for (int m = pRows[pCols[i]]; m < pRows[pCols[i] + 1]; m++)
-                    if (pCols[m] == n) {
+                    if (pCols[m] == n) { // mark index as existed with columns array
                         present = true;
                         break;
                     }
 
                 atomicAdd(&pRowCounts[n], 1);
 
-                if (!present)
+                if (!present) // increment row counter for given index
                     atomicAdd(&pRowCounts[pCols[i]], 1);
             }
         }
     }
+////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
+// row counter caller
     Nd4jLong barnes_row_count(const NDArray* rowP, const NDArray* colP, Nd4jLong N, NDArray& rowCounts) {
 
         int* pRowCounts = reinterpret_cast<int*>(rowCounts.specialBuffer());
@@ -58,12 +66,18 @@ namespace helpers {
         return numElements;
     }
 
+////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
+// extend symRowP with pRowCounts array vals
+//  pRowCounts - int array with length N
+//  symRowP - int array with length N+1
+//  N - given array length
+//
     static __global__ void fillUpsymRow(int const* pRowCounts, int* symRowP, int N) {
 
         auto start = blockIdx.x * blockDim.x + threadIdx.x;
         auto step = blockDim.x * gridDim.x;
 
-        for (int n = start; n < N + 1; n += step) {
+        for (int n = start; n < N + 1; n += step) { // to avoid race condition use shift only for given index
             symRowP[n] = 0;
             for (int i = 0; i < n; i++)
                 atomicAdd(&symRowP[n], pRowCounts[i]);
@@ -71,6 +85,17 @@ namespace helpers {
 
     }
 
+////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
+//  symmetrize routine kernel
+// pRows - rows buffer (ints)
+// pCols - column buffer (ints) with vals between 0 and max(pRows)
+// pVals - values vector (floats)
+// symRowP - ints, shifted pRows
+// symColP - ints, shifted pCols,
+// offset - ints, shitfs
+// pOutput - result matrix (floats)
+// N - pRows length
+//
     template <typename T>
     static __global__ void symmetrizeKernel(int const* pRows, int const* pCols, T const* pVals, int* symRowP, int* symColP, int* offset, T* pOutput, int N) {
         auto start = blockIdx.x * blockDim.x + threadIdx.x;
@@ -86,7 +111,6 @@ namespace helpers {
                 int start = pRows[colPI];
                 int end = pRows[colPI + 1];
 
-                //PRAGMA_OMP_PARALLEL_FOR_ARGS(schedule(guided) firstprivate(offset))
                 for (int m = start; m < end; m++) {
                     if (pCols[m] == n) {
                         present = true;
@@ -101,14 +125,10 @@ namespace helpers {
 
                 // If (colP[i], n) is not present, there is no addition involved
                 if (!present) {
-                    //int colPI = pCols[i];
-                    //if (n <= colPI) {
                     symColP[symRowP[n] + offset[n]] = colPI;
                     symColP[symRowP[pCols[i]] + offset[colPI]] = n;
                     pOutput[symRowP[n] + offset[n]] = pVals[i];
                     pOutput[symRowP[colPI] + offset[colPI]] = pVals[i];
-                    //}
-
                 }
                 // Update offsets
                 if (!present || (present && n <= colPI)) {
@@ -119,16 +139,18 @@ namespace helpers {
                 }
             }
         }
-
     }
 
+////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
+// symmetrize algorithm itself
+//
     template <typename T>
     static void barnes_symmetrize_(const NDArray* rowP, const NDArray* colP, const NDArray* valP, Nd4jLong N, NDArray* outputRows, NDArray* outputCols, NDArray* outputVals, NDArray* rowCounts) {
         int const* pRows = reinterpret_cast<int const*>(rowP->getSpecialBuffer());
         int* symRowP = reinterpret_cast<int*>(outputRows->specialBuffer());
         int* pRowCounts = reinterpret_cast<int*>(rowCounts->specialBuffer());
         auto stream = outputCols->getContext()->getCudaStream();
-
+        // fill up syRowP array
         fillUpsymRow<<<1, N, 128, *stream>>>(pRowCounts, symRowP, N);
         outputRows->syncToHost();
 //        outputRows->printBuffer("output rows");
@@ -140,15 +162,23 @@ namespace helpers {
         //std::vector<int> rowCountsV = rowCounts->getBufferAsVector<int>();
         auto offsetArr = NDArrayFactory::create<int>('c', {N});
         int* offset = reinterpret_cast<int*>(offsetArr.specialBuffer());
+        // symmetrize itself
         symmetrizeKernel<T><<<1, 1, 1024, *stream>>>(pRows, pCols, pVals, symRowP, symColP, offset, pOutput, N);
-//PRAGMA_OMP_PARALLEL_FOR_SIMD_ARGS(schedule(guided) shared(offset))
     }
+
+////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
+// symmetrize caller and adoption
+//
     void barnes_symmetrize(const NDArray* rowP, const NDArray* colP, const NDArray* valP, Nd4jLong N, NDArray* outputRows, NDArray* outputCols, NDArray* outputVals, NDArray* rowCounts) {
         BUILD_SINGLE_SELECTOR(valP->dataType(), barnes_symmetrize_, (rowP, colP, valP, N, outputRows, outputCols, outputVals, rowCounts), NUMERIC_TYPES);
 
         *outputVals /= 2.0;
     }
     BUILD_SINGLE_TEMPLATE(template void barnes_symmetrize_, (const NDArray* rowP, const NDArray* colP, const NDArray* valP, Nd4jLong N, NDArray* outputRows, NDArray* outputCols, NDArray* outputVals, NDArray* rowCounts), NUMERIC_TYPES);
+
+////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
+// edge forces implementation
+//
     template <typename T>
     static __global__ void edgeForcesKernel(int const* pRows, int const* pCols, T const* dataP, T const* vals, T* outputP, int N, int colCount, int rowSize) {
 //        std::vector<T> buffer(colCount);
@@ -172,10 +202,12 @@ namespace helpers {
                 for (int k = 0; k < colCount; k++)
                     math::atomics::nd4j_atomicAdd(&outputP[shift + k], T((dataP[shift + k] - thisSlice[k]) * res));
             }
-            //atomicAdd(&shift, colCount);
         }
-
     }
+////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
+// edge forces algorithm
+//
+
     template <typename T>
     static void barnes_edge_forces_(const NDArray* rowP, NDArray const* colP, NDArray const* valP, int N, NDArray const* data, NDArray* output) {
         NDArray::prepareSpecialUse({output}, {data, rowP, colP, valP, valP});
@@ -191,18 +223,22 @@ namespace helpers {
         edgeForcesKernel<T><<<1, 128, 1024, *stream>>>(pRows, pCols, dataP, vals, outputP, N, colCount, rowSize);
         NDArray::registerSpecialUse({output}, {rowP, colP, valP, data});
     }
-
+////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
+// edge forces caller
+//
     void barnes_edge_forces(const NDArray* rowP, NDArray const* colP, NDArray const* valP, int N, NDArray* output, NDArray const& data) {
         // Loop over all edges in the graph
         BUILD_SINGLE_SELECTOR(output->dataType(), barnes_edge_forces_, (rowP, colP, valP, N, &data, output), FLOAT_TYPES);
     }
     BUILD_SINGLE_TEMPLATE(template void barnes_edge_forces_, (const NDArray* rowP, NDArray const* colP, NDArray const* valP, int N, NDArray const* data, NDArray* output), FLOAT_TYPES);
 
+////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
+// gains - run a function T((x + 2.) * nd4j::math::nd4j_sign<T,T>(grad) != nd4j::math::nd4j_sign<T,T>(eps)) + T(x * 0.8 * nd4j::math::nd4j_sign<T,T>(grad) != nd4j::math::nd4j_sign<T,T>(eps));
+// for all members in input and put all in output
+//
     template <typename T>
     void barnes_gains_(NDArray* input, NDArray* gradX, NDArray* epsilon, NDArray* output) {
         auto gainsInternal = LAMBDA_TTT(x, grad, eps) {
-//            return T((x + 2.) * nd4j::math::nd4j_sign<T,T>(grad) != nd4j::math::nd4j_sign<T,T>(eps)) + T(x * 0.8 * nd4j::math::nd4j_sign<T,T>(grad) != nd4j::math::nd4j_sign<T,T>(eps));
-            //return T((x + 2.) * nd4j::math::nd4j_sign<T,T>(grad) == nd4j::math::nd4j_sign<T,T>(eps)) + T(x * 0.8 * nd4j::math::nd4j_sign<T,T>(grad) == nd4j::math::nd4j_sign<T,T>(eps));
             T res = nd4j::math::nd4j_sign<T,T>(grad) != nd4j::math::nd4j_sign<T,T>(eps) ? x + T(.2) : x * T(.8);
             if(res < .01) res = .01;
             return res;
@@ -211,14 +247,20 @@ namespace helpers {
         input->applyTriplewiseLambda(gradX, epsilon, gainsInternal, output);
     }
 
+////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
+// gains caller
     void barnes_gains(NDArray* input, NDArray* gradX, NDArray* epsilon, NDArray* output) {
         BUILD_SINGLE_SELECTOR(input->dataType(), barnes_gains_, (input, gradX, epsilon, output), NUMERIC_TYPES);
     }
     BUILD_SINGLE_TEMPLATE(template void barnes_gains_, (NDArray* input, NDArray* gradX, NDArray* epsilon, NDArray* output), NUMERIC_TYPES);
 
+////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
+// cell contains - check cells for given point
+//
     bool cell_contains(NDArray* corner, NDArray* width, NDArray* point, Nd4jLong dimension) {
         auto  cornerMinusWidth = *corner - *width;
         auto cornerPlusWidth = *corner + *width;
+        // executes on host side, so sync all to host memory
         cornerMinusWidth.syncToHost();
         cornerPlusWidth.syncToHost();
         for (Nd4jLong i = 0; i < dimension; i++) {
diff --git a/libnd4j/include/ops/declarable/helpers/cuda/activations.cu b/libnd4j/include/ops/declarable/helpers/cuda/activations.cu
index f402944aa..21b2eecd4 100644
--- a/libnd4j/include/ops/declarable/helpers/cuda/activations.cu
+++ b/libnd4j/include/ops/declarable/helpers/cuda/activations.cu
@@ -60,9 +60,9 @@ __global__ void preluCuda(const void *vx, const Nd4jLong *xShapeInfo,
 
 	for (int i = tid; i < xzLen; i += totalThreads) {
 
-    	shape::index2coords(xzRank, xShapeInfo + 1, i, xzLen, coords);
+    	shape::index2coords(i, xShapeInfo, coords);
 
-		const auto xzOffset = shape::getOffset(0, xShapeInfo + 1, xShapeInfo + xzRank + 1, coords, xzRank);
+		const auto xzOffset = shape::getOffset(xShapeInfo, coords);
 
 		const auto xVal = x[xzOffset];
 
@@ -72,7 +72,7 @@ __global__ void preluCuda(const void *vx, const Nd4jLong *xShapeInfo,
 				if(yShapeInfo[j + 1] == 1)
 					coords[j + 1] = 0;
 
-			z[xzOffset] = xVal * y[shape::getOffset(0, yShapeInfo + 1, yShapeInfo + yRank + 1, coords + 1, yRank)];
+			z[xzOffset] = xVal * y[shape::getOffset(yShapeInfo, coords + 1)];
 		}
 		else
 			z[xzOffset] = xVal;
@@ -139,11 +139,11 @@ __global__ linkage void preluBPCuda(const void *vIn,    const Nd4jLong *inShapeI
 
 	for (int i = tid; i < inLen; i += totalThreads) {
 
-    	shape::index2coords(inRank, inShapeInfo + 1, i, inLen, coords);
+    	shape::index2coords(i, inShapeInfo, coords);
 
-		const auto inOffset   = shape::getOffset(0, inShapeInfo   + 1, inShapeInfo   + inRank + 1, coords, inRank);
-		const auto dLdOOffset = shape::getOffset(0, dLdOShapeInfo + 1, dLdOShapeInfo + inRank + 1, coords, inRank);
-		const auto dLdIOffset = shape::getOffset(0, dLdIShapeInfo + 1, dLdIShapeInfo + inRank + 1, coords, inRank);
+		const auto inOffset   = shape::getOffset(inShapeInfo, coords);
+		const auto dLdOOffset = shape::getOffset(dLdOShapeInfo, coords);
+		const auto dLdIOffset = shape::getOffset(dLdIShapeInfo, coords);
 
 		const auto xVal = in[inOffset];
 		const auto grO  = dLdO[dLdOOffset];
@@ -154,8 +154,8 @@ __global__ linkage void preluBPCuda(const void *vIn,    const Nd4jLong *inShapeI
 				if(alphaShapeInfo[j + 1] == 1)
 					coords[j + 1] = 0;
 
-			const auto alphaOffset = shape::getOffset(0, alphaShapeInfo + 1, alphaShapeInfo + alphaRank + 1, coords + 1, alphaRank);
-			const auto dLdAOffset  = shape::getOffset(0, dLdAShapeInfo  + 1, dLdAShapeInfo  + alphaRank + 1, coords + 1, alphaRank);
+			const auto alphaOffset = shape::getOffset(alphaShapeInfo, coords + 1);
+			const auto dLdAOffset  = shape::getOffset(dLdAShapeInfo, coords + 1);
 
 			dLdI[dLdIOffset] =  grO * alpha[alphaOffset];
 
@@ -223,7 +223,7 @@ __device__ void softMaxForVectorCuda(const void *vx, const Nd4jLong *xShapeInfo,
 
 		const Nd4jLong elemIdx = i * blockDim.x + threadIdx.x;
 		if(elemIdx < len) {
-			const Nd4jLong xOffset = shape::getIndexOffset(elemIdx, xShapeInfo, len);
+			const Nd4jLong xOffset = shape::getIndexOffset(elemIdx, xShapeInfo);
 			shmem[threadIdx.x] = (threadIdx.x != 0) ? x[xOffset] : nd4j::math::nd4j_max<T>(x[xOffset], temp);	// take into account max element evaluated on previous iteration and stored in temp
 		}
 		else
@@ -249,8 +249,8 @@ __device__ void softMaxForVectorCuda(const void *vx, const Nd4jLong *xShapeInfo,
 
 		const Nd4jLong elemIdx = i * blockDim.x + threadIdx.x;
 		if(elemIdx < len) {
-			const Nd4jLong xOffset = shape::getIndexOffset(elemIdx, xShapeInfo, len);
-			const Nd4jLong zOffset = shape::getIndexOffset(elemIdx, zShapeInfo, len);
+			const Nd4jLong xOffset = shape::getIndexOffset(elemIdx, xShapeInfo);
+			const Nd4jLong zOffset = shape::getIndexOffset(elemIdx, zShapeInfo);
 			z[zOffset] = nd4j::math::nd4j_exp<T, T>(x[xOffset] - max);
 			shmem[threadIdx.x] = (threadIdx.x != 0) ? z[zOffset] : (z[zOffset] + temp); // take into account sum element evaluated on previous iteration and stored in temp
 		}
@@ -272,7 +272,7 @@ __device__ void softMaxForVectorCuda(const void *vx, const Nd4jLong *xShapeInfo,
 	for (int i = 0; i < numOfIters; ++i) {
 		const Nd4jLong elemIdx = i * blockDim.x + threadIdx.x;
 		if(elemIdx >= len) continue;
-		const Nd4jLong zOffset = shape::getIndexOffset(elemIdx, zShapeInfo, len);
+		const Nd4jLong zOffset = shape::getIndexOffset(elemIdx, zShapeInfo);
 		z[zOffset] /= shmem[0];
 	}
 }
@@ -386,7 +386,7 @@ __global__  void logSoftMaxForVectorCuda(const void *vx, const Nd4jLong *xzShape
 
 		const Nd4jLong elemIdx = i * blockDim.x + threadIdx.x;
 		if(elemIdx < len) {
-			const Nd4jLong offset = shape::getIndexOffset(elemIdx, xzShapeInfo, len);
+			const Nd4jLong offset = shape::getIndexOffset(elemIdx, xzShapeInfo);
 			shmem[threadIdx.x] = (threadIdx.x != 0) ? x[offset] : nd4j::math::nd4j_max<T>(x[offset], temp);	// take into account max element evaluated on previous iteration and stored in temp
 		}
 		else
@@ -412,7 +412,7 @@ __global__  void logSoftMaxForVectorCuda(const void *vx, const Nd4jLong *xzShape
 
 		const Nd4jLong elemIdx = i * blockDim.x + threadIdx.x;
 		if(elemIdx < len) {
-			const Nd4jLong offset = shape::getIndexOffset(elemIdx, xzShapeInfo, len);
+			const Nd4jLong offset = shape::getIndexOffset(elemIdx, xzShapeInfo);
 			z[offset] = nd4j::math::nd4j_exp<T, T>(x[offset] - max);
 			shmem[threadIdx.x] = (threadIdx.x != 0) ? z[offset] : (z[offset] + temp); // take into account sum element evaluated on previous iteration and stored in temp
 		}
@@ -434,7 +434,7 @@ __global__  void logSoftMaxForVectorCuda(const void *vx, const Nd4jLong *xzShape
 	for (int i = 0; i < numOfIters; ++i) {
 		const Nd4jLong elemIdx = i * blockDim.x + threadIdx.x;
 		if(elemIdx >= len) continue;
-		const Nd4jLong offset = shape::getIndexOffset(elemIdx, xzShapeInfo, len);
+		const Nd4jLong offset = shape::getIndexOffset(elemIdx, xzShapeInfo);
 		z[offset] = nd4j::math::nd4j_log<T,T>(z[offset] / shmem[0]);
 	}
 }
@@ -505,7 +505,7 @@ __global__ linkage void softMaxDerivForVectorCuda(const void *vx, const Nd4jLong
 
 		const Nd4jLong elemIdx = i * blockDim.x + threadIdx.x;
 		if(elemIdx < len) {
-			const Nd4jLong offset = shape::getIndexOffset(elemIdx, xzShapeInfo, len);
+			const Nd4jLong offset = shape::getIndexOffset(elemIdx, xzShapeInfo);
 			shmem[threadIdx.x] = (threadIdx.x != 0) ? x[offset] : nd4j::math::nd4j_max<T>(x[offset], temp);	// take into account max element evaluated on previous iteration and stored in temp
 		}
 		else
@@ -531,7 +531,7 @@ __global__ linkage void softMaxDerivForVectorCuda(const void *vx, const Nd4jLong
 
 		const Nd4jLong elemIdx = i * blockDim.x + threadIdx.x;
 		if(elemIdx < len) {
-			const Nd4jLong offset = shape::getIndexOffset(elemIdx, xzShapeInfo, len);
+			const Nd4jLong offset = shape::getIndexOffset(elemIdx, xzShapeInfo);
 			z[offset] = nd4j::math::nd4j_exp<T, T>(x[offset] - max);
 			shmem[threadIdx.x] = (threadIdx.x != 0) ? z[offset] : (z[offset] + temp); // take into account sum element evaluated on previous iteration and stored in temp
 		}
@@ -553,7 +553,7 @@ __global__ linkage void softMaxDerivForVectorCuda(const void *vx, const Nd4jLong
 	for (int i = 0; i < numOfIters; ++i) {
 		const Nd4jLong elemIdx = i * blockDim.x + threadIdx.x;
 		if(elemIdx >= len) continue;
-		const Nd4jLong offset = shape::getIndexOffset(elemIdx, xzShapeInfo, len);
+		const Nd4jLong offset = shape::getIndexOffset(elemIdx, xzShapeInfo);
 		z[offset] /= shmem[0];
 		z[offset] *= (1.f - z[offset]);		// derivative
 	}
diff --git a/libnd4j/include/ops/declarable/helpers/cuda/addBias.cu b/libnd4j/include/ops/declarable/helpers/cuda/addBias.cu
new file mode 100644
index 000000000..7134d764a
--- /dev/null
+++ b/libnd4j/include/ops/declarable/helpers/cuda/addBias.cu
@@ -0,0 +1,110 @@
+/*******************************************************************************
+ * Copyright (c) 2015-2018 Skymind, Inc.
+ *
+ * This program and the accompanying materials are made available under the
+ * terms of the Apache License, Version 2.0 which is available at
+ * https://www.apache.org/licenses/LICENSE-2.0.
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+ * License for the specific language governing permissions and limitations
+ * under the License.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ ******************************************************************************/
+
+//
+// @author Yurii Shyrma (iuriish@yahoo.com)
+//
+
+
+#include<ops/declarable/helpers/addBias.h>
+#include <PointersManager.h>
+
+namespace nd4j    {
+namespace ops     {
+namespace helpers {
+
+//////////////////////////////////////////////////////////////////////
+template<typename X, typename Y>
+__global__ static void addBiasCuda( const void* vx, const Nd4jLong* xShapeInfo,
+                                    const void* vy, const Nd4jLong* yShapeInfo,
+                                          void* vz, const Nd4jLong* zShapeInfo,
+                                    const bool isNCHW) {
+
+    // bias [oC]
+
+    // if(input_rank == 4)
+        // input and output have same shapes: [bS, oH, oW, oC] (NHWC) or [bS, oC, oH, oW] (NCHW)
+    // if(input_rank == 5)
+        // input and output have same shapes: [bS, oD, oH, oW, oC] (NHWC) or [bS, oD, oC, oH, oW] (NCHW)
+
+    const X* x = reinterpret_cast<const X*>(vx);
+    const Y* y = reinterpret_cast<const Y*>(vy);
+          X* z = reinterpret_cast<X*>(vz);
+
+    __shared__ int rank, channelPosition;
+    __shared__ Nd4jLong *sharedMem, len;
+    __shared__ bool xzSameOffsets, xzAreSame;
+
+    if (threadIdx.x == 0) {
+
+        extern __shared__ unsigned char shmem[];
+        sharedMem = reinterpret_cast<Nd4jLong*>(shmem);
+
+        rank = shape::rank(xShapeInfo);     // xRank == zRank
+        xzSameOffsets = shape::haveSameShapeAndStrides(xShapeInfo, zShapeInfo);
+        len = shape::length(xShapeInfo);
+        channelPosition = isNCHW ? 1 : rank - 1;        // second or last
+        xzAreSame = x == z;
+    }
+    __syncthreads();
+
+    auto coords = sharedMem + threadIdx.x * rank;
+
+    for (Nd4jLong i = blockIdx.x * blockDim.x + threadIdx.x; i < len; i += blockDim.x * gridDim.x) {
+
+        shape::index2coords(i, xShapeInfo, coords);
+
+        const auto xOffsets = shape::getOffset(xShapeInfo, coords);
+        const auto zOffsets = xzSameOffsets ? xOffsets : shape::getOffset(zShapeInfo, coords);
+        const auto yOffsets = shape::getOffset(yShapeInfo, coords + channelPosition);
+
+        if(xzAreSame)
+            z[zOffsets] += static_cast<X>(y[yOffsets]);
+        else
+            z[zOffsets] = x[xOffsets] + static_cast<X>(y[yOffsets]);
+    }
+}
+
+//////////////////////////////////////////////////////////////////////////
+template<typename X, typename Y>
+static void addBiasCudaLauncher(const int blocksPerGrid, const int threadsPerBlock, const int sharedMem, const cudaStream_t *stream,
+                                         const void* vx, const Nd4jLong* xShapeInfo,
+                                         const void* vy, const Nd4jLong* yShapeInfo,
+                                               void* vz, const Nd4jLong* zShapeInfo,
+                                         const bool isNCHW) {
+
+    addBiasCuda<X,Y><<<blocksPerGrid, threadsPerBlock, sharedMem, *stream>>>(vx, xShapeInfo, vy, yShapeInfo, vz, zShapeInfo, isNCHW);
+}
+
+//////////////////////////////////////////////////////////////////////////
+void addBias(nd4j::graph::Context& block, const NDArray& input, const NDArray& bias, NDArray& output, const bool isNCHW) {
+
+    PointersManager manager(block.launchContext(), "addBias");
+
+    const int threadsPerBlock = MAX_NUM_THREADS;
+    const int blocksPerGrid = (input.lengthOf() + threadsPerBlock - 1) / threadsPerBlock;
+    const int sharedMem = input.rankOf() * sizeof(Nd4jLong) * threadsPerBlock  + 128;
+
+    NDArray::prepareSpecialUse({&output}, {&input, &bias});
+    BUILD_DOUBLE_SELECTOR(input.dataType(), bias.dataType(), addBiasCudaLauncher, (blocksPerGrid, threadsPerBlock, sharedMem, block.launchContext()->getCudaStream(), input.getSpecialBuffer(), input.getSpecialShapeInfo(), bias.getSpecialBuffer(), bias.getSpecialShapeInfo(), output.specialBuffer(), output.specialShapeInfo(), isNCHW), FLOAT_TYPES, FLOAT_TYPES);
+    NDArray::registerSpecialUse({&output}, {&input, &bias});
+
+    manager.synchronize();
+}
+
+}
+}
+}
\ No newline at end of file
diff --git a/libnd4j/include/ops/declarable/helpers/cuda/adjust_hue.cu b/libnd4j/include/ops/declarable/helpers/cuda/adjust_hue.cu
index c27c9fb8a..5b52d1b0b 100644
--- a/libnd4j/include/ops/declarable/helpers/cuda/adjust_hue.cu
+++ b/libnd4j/include/ops/declarable/helpers/cuda/adjust_hue.cu
@@ -143,13 +143,13 @@ static void _CUDA_G adjustHueSingleNCHWKernel(void *xBuffer, Nd4jLong *xTadShape
 
 
     for (Nd4jLong e = tid; e < tuples; e += blockDim.x * gridDim.x) {
-        auto _ri = bufferR + shape::getIndexOffset(e, xTadShapeInfo, tadLength);;
-        auto _gi = bufferG + shape::getIndexOffset(e, xTadShapeInfo, tadLength);;
-        auto _bi = bufferB + shape::getIndexOffset(e, xTadShapeInfo, tadLength);;
+        auto _ri = bufferR + shape::getIndexOffset(e, xTadShapeInfo);
+        auto _gi = bufferG + shape::getIndexOffset(e, xTadShapeInfo);
+        auto _bi = bufferB + shape::getIndexOffset(e, xTadShapeInfo);
 
-        auto _ro = outputR + shape::getIndexOffset(e, xTadShapeInfo, tadLength);;
-        auto _go = outputG + shape::getIndexOffset(e, xTadShapeInfo, tadLength);;
-        auto _bo = outputB + shape::getIndexOffset(e, xTadShapeInfo, tadLength);;
+        auto _ro = outputR + shape::getIndexOffset(e, xTadShapeInfo);
+        auto _go = outputG + shape::getIndexOffset(e, xTadShapeInfo);
+        auto _bo = outputB + shape::getIndexOffset(e, xTadShapeInfo);
 
         T h, v_min, v_max;
         helpers::rgb_to_hv(_ri[0], _gi[0], _bi[0], &h, &v_min, &v_max);
diff --git a/libnd4j/include/ops/declarable/helpers/cuda/adjust_saturation.cu b/libnd4j/include/ops/declarable/helpers/cuda/adjust_saturation.cu
index a1dc4189a..b801765b2 100644
--- a/libnd4j/include/ops/declarable/helpers/cuda/adjust_saturation.cu
+++ b/libnd4j/include/ops/declarable/helpers/cuda/adjust_saturation.cu
@@ -139,13 +139,13 @@ static void _CUDA_G adjustSaturationSingleNCHWKernel(void *xBuffer, Nd4jLong *xT
     auto outputB = reinterpret_cast<T *>(zBuffer) + zOffsets[2];
 
     for (Nd4jLong e = tid; e < tuples; e += blockDim.x * gridDim.x) {
-        auto _ri = bufferR + shape::getIndexOffset(e, xTadShapeInfo, tadLength);
-        auto _gi = bufferG + shape::getIndexOffset(e, xTadShapeInfo, tadLength);
-        auto _bi = bufferB + shape::getIndexOffset(e, xTadShapeInfo, tadLength);
+        auto _ri = bufferR + shape::getIndexOffset(e, xTadShapeInfo);
+        auto _gi = bufferG + shape::getIndexOffset(e, xTadShapeInfo);
+        auto _bi = bufferB + shape::getIndexOffset(e, xTadShapeInfo);
 
-        auto _ro = outputR + shape::getIndexOffset(e, xTadShapeInfo, tadLength);
-        auto _go = outputG + shape::getIndexOffset(e, xTadShapeInfo, tadLength);
-        auto _bo = outputB + shape::getIndexOffset(e, xTadShapeInfo, tadLength);
+        auto _ro = outputR + shape::getIndexOffset(e, xTadShapeInfo);
+        auto _go = outputG + shape::getIndexOffset(e, xTadShapeInfo);
+        auto _bo = outputB + shape::getIndexOffset(e, xTadShapeInfo);
 
         T h, s, v;
         // Convert the RGB color to Hue/V-range.
diff --git a/libnd4j/include/ops/declarable/helpers/cuda/axis.cu b/libnd4j/include/ops/declarable/helpers/cuda/axis.cu
index a3b2bcd32..1236ae495 100644
--- a/libnd4j/include/ops/declarable/helpers/cuda/axis.cu
+++ b/libnd4j/include/ops/declarable/helpers/cuda/axis.cu
@@ -27,11 +27,11 @@ namespace helpers {
 
     void adjustAxis(Nd4jLong rank, NDArray* axisVector, std::vector<int>& output) {
         output.resize(axisVector->lengthOf());
-        axisVector->tickReadDevice();
-        axisVector->syncToHost();
+        axisVector->tickReadDevice(); // mark input as read on device
+        axisVector->syncToHost(); // sync to host
         for (int e = 0; e < axisVector->lengthOf(); e++) {
                 auto ca = axisVector->e<int>(e);
-                if (ca < 0)
+                if (ca < 0) // shift values on rank for negative vals
                     ca += rank;
 
                 output[e] = ca;
@@ -41,7 +41,7 @@ namespace helpers {
     void adjustAxis(Nd4jLong rank, std::vector<int> &axisVector) {
         for (int e = 0; e < axisVector.size(); e++) {
             auto a = axisVector[e];
-            if (a < 0)
+            if (a < 0) // shift vals on rank for negative vals
                 axisVector[e] = a + rank;
         }
     }
diff --git a/libnd4j/include/ops/declarable/helpers/cuda/batchnorm.cu b/libnd4j/include/ops/declarable/helpers/cuda/batchnorm.cu
index 6c3dedd20..d9188e3a8 100644
--- a/libnd4j/include/ops/declarable/helpers/cuda/batchnorm.cu
+++ b/libnd4j/include/ops/declarable/helpers/cuda/batchnorm.cu
@@ -64,25 +64,25 @@ __global__ static void batchnormCuda(const void* vx, const Nd4jLong* xShapeInfo,
 
     for (uint i = tid; i < minLen; i += totalThreads) {
 
-		const auto meanOffset     = shape::getIndexOffset(i, meanShapeInfo, minLen);
-    	const auto varianceOffset = shape::getIndexOffset(i, varianceShapeInfo, minLen);
+		const auto meanOffset     = shape::getIndexOffset(i, meanShapeInfo);
+    	const auto varianceOffset = shape::getIndexOffset(i, varianceShapeInfo);
 
     	T sigmaInvGam = 1. / nd4j::math::nd4j_sqrt<T, T>(variance[varianceOffset] + epsilon);
 
     	if(gamma != nullptr)
-    		sigmaInvGam *= gamma[shape::getIndexOffset(i, gammaShapeInfo, minLen)];
+    		sigmaInvGam *= gamma[shape::getIndexOffset(i, gammaShapeInfo)];
 
 		auto betaOffset = 0;
     	if(beta != nullptr)
-    		betaOffset = shape::getIndexOffset(i, betaShapeInfo, minLen);
+    		betaOffset = shape::getIndexOffset(i, betaShapeInfo);
 
     	const auto xTad = x + xTadOffsets[i];
     		  auto zTad = z + zTadOffsets[i];
 
     	for (uint j = 0; j < tadLen; ++j) {
 
-    		const auto xTadOffset = shape::getIndexOffset(j, xTadShapeInfo, tadLen);
-    		const auto zTadOffset = shape::getIndexOffset(j, zTadShapeInfo, tadLen);
+    		const auto xTadOffset = shape::getIndexOffset(j, xTadShapeInfo);
+    		const auto zTadOffset = shape::getIndexOffset(j, zTadShapeInfo);
 
     		zTad[zTadOffset] = (xTad[xTadOffset] - mean[meanOffset]) * sigmaInvGam;
 
@@ -130,10 +130,10 @@ __global__ static void batchnormCuda2(const void* vx, const Nd4jLong* xShapeInfo
 
     for (uint i = tid; i < xLen; i += totalThreads) {
 
-        shape::index2coords(xRank, shape::shapeOf(const_cast<Nd4jLong*>(xShapeInfo)), i, xLen, coords);
+        shape::index2coords(i, xShapeInfo, coords);
 
-        const auto xOffset = shape::getOffset(0, shape::shapeOf(const_cast<Nd4jLong*>(xShapeInfo)), shape::stride(const_cast<Nd4jLong*>(xShapeInfo)), coords, xRank);
-        const auto zOffset = shape::getOffset(0, shape::shapeOf(const_cast<Nd4jLong*>(zShapeInfo)), shape::stride(const_cast<Nd4jLong*>(zShapeInfo)), coords, xRank);
+        const auto xOffset = shape::getOffset(xShapeInfo, coords);
+        const auto zOffset = shape::getOffset(zShapeInfo, coords);
 
         if(minRank == xRank) {
             for (uint i = 0, j = 0; i < xRank; ++i) {
@@ -146,20 +146,20 @@ __global__ static void batchnormCuda2(const void* vx, const Nd4jLong* xShapeInfo
         else    // minRank = numDims = 1 in this case
             coords[0] = coords[dims[0]];
 
-        const auto meanOffset     = shape::getOffset(0, shape::shapeOf(const_cast<Nd4jLong*>(meanShapeInfo)), shape::stride(const_cast<Nd4jLong*>(meanShapeInfo)), coords, minRank);
-        const auto varianceOffset = shape::getOffset(0, shape::shapeOf(const_cast<Nd4jLong*>(varianceShapeInfo)), shape::stride(const_cast<Nd4jLong*>(varianceShapeInfo)), coords, minRank);
+        const auto meanOffset     = shape::getOffset(meanShapeInfo, coords);
+        const auto varianceOffset = shape::getOffset(varianceShapeInfo, coords);
 
         T sigmaInvGam = 1. / nd4j::math::nd4j_sqrt<T, T>(variance[varianceOffset] + epsilon);
 
         if(gamma != nullptr) {
-            const auto gammaOffset = shape::getOffset(0, shape::shapeOf(const_cast<Nd4jLong*>(gammaShapeInfo)), shape::stride(const_cast<Nd4jLong*>(gammaShapeInfo)), coords, minRank);
+            const auto gammaOffset = shape::getOffset(gammaShapeInfo, coords);
             sigmaInvGam *= gamma[gammaOffset];
         }
 
         z[zOffset] = (x[xOffset] - mean[meanOffset]) * sigmaInvGam;
 
         if(beta != nullptr) {
-            const auto betaOffset = shape::getOffset(0, shape::shapeOf(const_cast<Nd4jLong*>(betaShapeInfo)), shape::stride(const_cast<Nd4jLong*>(betaShapeInfo)), coords, minRank);
+            const auto betaOffset = shape::getOffset(betaShapeInfo, coords);
             z[zOffset] += beta[betaOffset];
         }
     }
diff --git a/libnd4j/include/ops/declarable/helpers/cuda/betaInc.cu b/libnd4j/include/ops/declarable/helpers/cuda/betaInc.cu
index 87e4948ec..90619c76c 100644
--- a/libnd4j/include/ops/declarable/helpers/cuda/betaInc.cu
+++ b/libnd4j/include/ops/declarable/helpers/cuda/betaInc.cu
@@ -15,7 +15,7 @@
  ******************************************************************************/
 
 //
-// Created by Yurii Shyrma on 11.12.2017
+// @author Yurii Shyrma (iuriish@yahoo.com)
 //
 
 #include<cmath>
@@ -117,10 +117,10 @@ __global__ void betaIncForArrayCuda(const void* va, const Nd4jLong* aShapeInfo,
 
     Nd4jLong len = shape::length(xShapeInfo);
 
-    const T  a = *(reinterpret_cast<const T*>(va) + shape::getIndexOffset(j, aShapeInfo, len));
-    const T  b = *(reinterpret_cast<const T*>(vb) + shape::getIndexOffset(j, bShapeInfo, len));
-    const T  x = *(reinterpret_cast<const T*>(vx) + shape::getIndexOffset(j, xShapeInfo, len));
-    	  T& z = *(reinterpret_cast<T*>(vz) 	 	 + shape::getIndexOffset(j, zShapeInfo, len));
+    const T  a = *(reinterpret_cast<const T*>(va) + shape::getIndexOffset(j, aShapeInfo));
+    const T  b = *(reinterpret_cast<const T*>(vb) + shape::getIndexOffset(j, bShapeInfo));
+    const T  x = *(reinterpret_cast<const T*>(vx) + shape::getIndexOffset(j, xShapeInfo));
+    	  T& z = *(reinterpret_cast<T*>(vz) 	  + shape::getIndexOffset(j, zShapeInfo));
 
     // t^{n-1} * (1 - t)^{n-1} is symmetric function with respect to x = 0.5
    	if(a == b && x == static_cast<T>(0.5)) {
diff --git a/libnd4j/include/ops/declarable/helpers/cuda/col2im.cppc b/libnd4j/include/ops/declarable/helpers/cuda/col2im.cppc
index aefb97963..63e406cc6 100644
--- a/libnd4j/include/ops/declarable/helpers/cuda/col2im.cppc
+++ b/libnd4j/include/ops/declarable/helpers/cuda/col2im.cppc
@@ -35,12 +35,12 @@ void col2im_(nd4j::LaunchContext & context, const NDArray& input,  NDArray& outp
     auto colShape  		= shape::shapeOf(colShapeBuffer);
     auto colStride 		= shape::stride(colShapeBuffer);
     auto imShape  	    = shape::shapeOf(imShapeBuffer);
-    auto imStride 	    = shape::stride(imShapeBuffer);            
+    auto imStride 	    = shape::stride(imShapeBuffer);
 
     const int bS = imShape[0];
     const int iC = imShape[1];
     const int kH = colShape[2];
-    const int kW = colShape[3];                    
+    const int kW = colShape[3];
     const int oH = colShape[4];
     const int oW = colShape[5];
     const Nd4jLong colStride0 = colStride[0];
@@ -58,31 +58,31 @@ void col2im_(nd4j::LaunchContext & context, const NDArray& input,  NDArray& outp
     const auto imEWS = shape::elementWiseStride(imShapeBuffer);
     if(imEWS == 1) {
         memset(imBuff, 0, shape::length(imShapeBuffer) * sizeof(T));
-    } 
+    }
     else if (imEWS > 1) {
 PRAGMA_OMP_PARALLEL_FOR_ARGS(schedule(static) proc_bind(close))
         for (int i = 0; i < shape::length(imShapeBuffer) * imEWS; i += imEWS)
             imBuff[i] = static_cast<T>(0.f);
-    } 
-    else {        
+    }
+    else {
         const auto len = shape::length(imShapeBuffer);
 PRAGMA_OMP_PARALLEL_FOR_ARGS(schedule(static) proc_bind(close))
-        for (int i = 0; i < len; i++)            
-            imBuff[shape::getIndexOffset(i, imShapeBuffer, len)] = static_cast<T>(0.f);
+        for (int i = 0; i < len; i++)
+            imBuff[shape::getIndexOffset(i, imShapeBuffer)] = static_cast<T>(0.f);
     }
-            
+
 	T *col, *im;
     int imRow, imCol;
 
     if (shape::order(colShapeBuffer) == 'c' &&  shape::order(imShapeBuffer) == 'c' && shape::strideDescendingCAscendingF(colShapeBuffer) && shape::strideDescendingCAscendingF(imShapeBuffer)) {
-            
+
 PRAGMA_OMP_PARALLEL_FOR_ARGS(schedule(static) proc_bind(close) private(col, im, imRow, imCol))
-    	for (int b = 0; b < bS; b++) {        
-      		for (int c = 0; c < iC; ++c) {                    
-            	for (int kRow = 0; kRow < kH; ++kRow) {                        
-                	for (int kCol = 0; kCol < kW; ++kCol) {                            
+    	for (int b = 0; b < bS; b++) {
+      		for (int c = 0; c < iC; ++c) {
+            	for (int kRow = 0; kRow < kH; ++kRow) {
+                	for (int kCol = 0; kCol < kW; ++kCol) {
                     	for (int colH = 0; colH < oH; ++colH) {
-                        	for (int colW = 0; colW < oW; ++colW) {                    
+                        	for (int colW = 0; colW < oW; ++colW) {
 
                             	imRow = (-pH + kRow * dH) + colH*sH;
                                 imCol = (-pW + kCol * dW) + colW*sW;
@@ -97,21 +97,21 @@ PRAGMA_OMP_PARALLEL_FOR_ARGS(schedule(static) proc_bind(close) private(col, im,
                     }
                 }
             }
-        }  
+        }
     }
     else {
 
 PRAGMA_OMP_PARALLEL_FOR_ARGS(schedule(static) proc_bind(close) private(im, col, imRow, imCol))
-    	for (int b = 0; b < bS; b++) {        
+    	for (int b = 0; b < bS; b++) {
         	for (int colH = 0; colH < oH; ++colH) {
             	for (int colW = 0; colW < oW; ++colW) {
-                	for (int c = 0; c < iC; ++c) {                        
-                    	for (int kRow = 0; kRow < kH; ++kRow) {                        
-                        	for (int kCol = 0; kCol < kW; ++kCol) {                            
-                        
+                	for (int c = 0; c < iC; ++c) {
+                    	for (int kRow = 0; kRow < kH; ++kRow) {
+                        	for (int kCol = 0; kCol < kW; ++kCol) {
+
                             	imRow = (-pH + kRow * dH) + colH*sH;
                                 imCol = (-pW + kCol * dW) + colW*sW;
-                                        
+
                                 col = colBuff + b*colStride0 + c*colStride1 + kRow*colStride2 + kCol*colStride3 + colH*colStride4 + colW*colStride5;
                                 im  = imBuff  + b*imStride0  + c*imStride1  + imRow*imStride2 + imCol*imStride3;
 
@@ -120,9 +120,9 @@ PRAGMA_OMP_PARALLEL_FOR_ARGS(schedule(static) proc_bind(close) private(im, col,
                             }
                         }
                     }
-                }                           
+                }
             }
-        }  
+        }
     }
 }
 
diff --git a/libnd4j/include/ops/declarable/helpers/cuda/col2im.cu b/libnd4j/include/ops/declarable/helpers/cuda/col2im.cu
index 9ab7337c2..dc1935b83 100644
--- a/libnd4j/include/ops/declarable/helpers/cuda/col2im.cu
+++ b/libnd4j/include/ops/declarable/helpers/cuda/col2im.cu
@@ -61,9 +61,9 @@ static __global__ void col2imCuda(const void* columns, const Nd4jLong* colShapeI
 
     auto coords = sharedMem + threadIdx.x * colRank;
 
-    shape::index2coords(imRank, imShapeInfo + 1, imInd, imLen, coords);
+    shape::index2coords(imInd, imShapeInfo, coords);
 
-    const auto imOffset = shape::getOffset(0, imShapeInfo + 1, imShapeInfo + imRank + 1, coords, imRank);
+    const auto imOffset = shape::getOffset(imShapeInfo, coords);
 
     const int imH = coords[2] + pH;
     const int imW = coords[3] + pW;
@@ -86,7 +86,7 @@ static __global__ void col2imCuda(const void* columns, const Nd4jLong* colShapeI
                 coords[2] /= dH;
                 coords[3] /= dW;
 
-                val += col[shape::getOffset(0, colShapeInfo + 1, colShapeInfo + colRank + 1, coords, colRank)];
+                val += col[shape::getOffset(colShapeInfo, coords)];
             }
         }
     }
diff --git a/libnd4j/include/ops/declarable/helpers/cuda/compare_elem.cu b/libnd4j/include/ops/declarable/helpers/cuda/compare_elem.cu
index 545d7c668..d2792b630 100644
--- a/libnd4j/include/ops/declarable/helpers/cuda/compare_elem.cu
+++ b/libnd4j/include/ops/declarable/helpers/cuda/compare_elem.cu
@@ -32,8 +32,8 @@ namespace nd4j {
 
                 // each thread will compare 2 elements: E and E+1
                 for (int e = tid; e < length - 1; e += blockDim.x * gridDim.x) {
-                    auto val0 = x[shape::getIndexOffset(e, xShapeInfo, length)];
-                    auto val1 = x[shape::getIndexOffset(e+1, xShapeInfo, length)];
+                    auto val0 = x[shape::getIndexOffset(e, xShapeInfo)];
+                    auto val1 = x[shape::getIndexOffset(e+1, xShapeInfo)];
 
                     bool v = false;
                     if (isStrict)
diff --git a/libnd4j/include/ops/declarable/helpers/cuda/concat.cu b/libnd4j/include/ops/declarable/helpers/cuda/concat.cu
index d372f05c8..6f9a8c6ab 100644
--- a/libnd4j/include/ops/declarable/helpers/cuda/concat.cu
+++ b/libnd4j/include/ops/declarable/helpers/cuda/concat.cu
@@ -59,9 +59,9 @@ __global__ static void concatCuda(void* pVx,  void* pxShapeInfo, void* vz, Nd4jL
 
     auto coords = sharedMem + threadIdx.x * rank;
 
-    shape::index2coords(rank, zShapeInfo + 1, tid, zLen, coords);
+    shape::index2coords(tid, zShapeInfo, coords);
 
-    const auto zOffset = shape::getOffset(0, zShapeInfo + 1, zShapeInfo + rank + 1, coords, rank);
+    const auto zOffset = shape::getOffset(zShapeInfo, coords);
 
     int inArrIdx = 0;
     Nd4jLong *xShapeInfo = reinterpret_cast<Nd4jLong**>(pxShapeInfo)[inArrIdx];
@@ -72,7 +72,7 @@ __global__ static void concatCuda(void* pVx,  void* pxShapeInfo, void* vz, Nd4jL
     }
 
     const auto* x      = reinterpret_cast<T*>(reinterpret_cast<void**>(pVx)[inArrIdx]);
-    const auto xOffset = shape::getOffset(0, xShapeInfo + 1, xShapeInfo + rank + 1, coords, rank);
+    const auto xOffset = shape::getOffset(xShapeInfo, coords);
 
     z[zOffset] = x[xOffset];
 }
diff --git a/libnd4j/include/ops/declarable/helpers/cuda/confusion.cu b/libnd4j/include/ops/declarable/helpers/cuda/confusion.cu
index 12f14b20b..3738d7770 100644
--- a/libnd4j/include/ops/declarable/helpers/cuda/confusion.cu
+++ b/libnd4j/include/ops/declarable/helpers/cuda/confusion.cu
@@ -59,7 +59,7 @@ namespace helpers {
             auto tZ = z + tadOffsets[label];
             T val = (weightsBuffer == nullptr ? (T)1.0f : w[t]);
 
-            auto idx = shape::getIndexOffset(pred, tadShape, arrLen);
+            auto idx = shape::getIndexOffset(pred, tadShape);
             tZ[idx] = val;
         }
     }
diff --git a/libnd4j/include/ops/declarable/helpers/cuda/convolutions.cu b/libnd4j/include/ops/declarable/helpers/cuda/convolutions.cu
index c08551318..273749bfd 100644
--- a/libnd4j/include/ops/declarable/helpers/cuda/convolutions.cu
+++ b/libnd4j/include/ops/declarable/helpers/cuda/convolutions.cu
@@ -21,6 +21,7 @@
 #include <ops/declarable/helpers/convolutions.h>
 #include <ops/declarable/helpers/im2col.h>
 #include <ops/declarable/helpers/col2im.h>
+#include<ops/declarable/helpers/addBias.h>
 #include <exceptions/cuda_exception.h>
 #include <NDArrayFactory.h>
 #include <MmulHelper.h>
@@ -63,7 +64,7 @@ static __global__ void vol2colCuda(const void* volume, const Nd4jLong* volShapeI
 
     auto coords = sharedMem + threadIdx.x * colRank;
 
-    shape::index2coords(colRank, colShapeInfo + 1, colInd, colLen, coords);
+    shape::index2coords(colInd, colShapeInfo, coords);
 
     // const auto colW = coords[7];
     // const auto colH = coords[6];
@@ -74,7 +75,7 @@ static __global__ void vol2colCuda(const void* volume, const Nd4jLong* volShapeI
     // const auto c    = coords[1];
     // const auto b    = coords[0];
 
-    const auto colOffset = shape::getOffset(0, colShapeInfo + 1, colShapeInfo + colRank + 1, coords, colRank);
+    const auto colOffset = shape::getOffset(colShapeInfo, coords);
 
     coords[2] = -pD + coords[2] * dD + coords[5] * sD;     // const auto volDep = (-pD + kDep * dD) + colD * sD;
     coords[3] = -pH + coords[3] * dH + coords[6] * sH;     // const auto volRow = (-pH + kRow * dH) + colH * sH;
@@ -83,7 +84,7 @@ static __global__ void vol2colCuda(const void* volume, const Nd4jLong* volShapeI
     if (static_cast<unsigned>(coords[2]) >= static_cast<unsigned>(iD) || static_cast<unsigned>(coords[3]) >= static_cast<unsigned>(iH) || static_cast<unsigned>(coords[4]) >= static_cast<unsigned>(iW))
         col[colOffset] = static_cast<T>(0.);
     else
-        col[colOffset] = vol[shape::getOffset(0, volShapeInfo + 1, volShapeInfo + volRank + 1, coords, volRank)];
+        col[colOffset] = vol[shape::getOffset(volShapeInfo, coords)];
 }
 
 //////////////////////////////////////////////////////////////////////////
@@ -149,9 +150,9 @@ static __global__ void col2volCuda(const void* columns, const Nd4jLong* colShape
 
     auto coords = sharedMem + threadIdx.x * colRank;
 
-    shape::index2coords(volRank, volShapeInfo + 1, volInd, volLen, coords);
+    shape::index2coords(volInd, volShapeInfo, coords);
 
-    const auto volOffset = shape::getOffset(0, volShapeInfo + 1, volShapeInfo + volRank + 1, coords, volRank);
+    const auto volOffset = shape::getOffset(volShapeInfo, coords);
 
     const int imD = coords[2] + pD;
     const int imH = coords[3] + pH;
@@ -181,7 +182,7 @@ static __global__ void col2volCuda(const void* columns, const Nd4jLong* colShape
                     coords[3] /= dH;
                     coords[4] /= dW;
 
-                    val += col[shape::getOffset(0, colShapeInfo + 1, colShapeInfo + colRank + 1, coords, colRank)];
+                    val += col[shape::getOffset(colShapeInfo, coords)];
                 }
             }
         }
@@ -268,8 +269,8 @@ static void conv2d_(nd4j::graph::Context& block, const NDArray* input, const NDA
 
     //----- add biases if required -----//
     if(bias)
-        output->applyBroadcast(broadcast::Add, {indIOioC}, bias);
-        // helpers::addBias(*output, *bias, isNCHW);
+        // output->applyBroadcast(broadcast::Add, {indIOioC}, bias);
+        helpers::addBias(block, *output, *bias, *output, isNCHW);
 
     if(!isNCHW)
         delete input;
@@ -283,7 +284,7 @@ void ConvolutionUtils::conv2d(nd4j::graph::Context& block, const NDArray* input,
 
 //////////////////////////////////////////////////////////////////////////
 template <typename X, typename Y>
-static void depthwiseConv2d_(const NDArray* input, const NDArray* weights, const NDArray* bias, NDArray* output, const int kH, const int kW, const int sH, const int sW, int pH, int pW, const int dH, const int dW, const int isSameMode, const int isNCHW) {
+static void depthwiseConv2d_(nd4j::graph::Context& block, const NDArray* input, const NDArray* weights, const NDArray* bias, NDArray* output, const int kH, const int kW, const int sH, const int sW, int pH, int pW, const int dH, const int dW, const int isSameMode, const int isNCHW) {
 
     // input     [bS, iH, iW, iC] (NHWC) or [bS, iC, iH, iW] (NCHW)
     // weights   [kH, kW, iC, mC] always
@@ -330,7 +331,8 @@ static void depthwiseConv2d_(const NDArray* input, const NDArray* weights, const
     MmulHelper::tensorDot(&columns, weights, &outputReshaped, modifColumns, {{2,0,1,3},{iC,kH*kW,mC}}, modifOutput);              // [iC, bS*oH*oW, kW*kH] x [iC, kH*kW, mC] = [iC, bS*oH*oW, mC]
 
     if(bias)
-        output->applyBroadcast(broadcast::Add, {indIOioC}, bias);
+        // output->applyBroadcast(broadcast::Add, {indIOioC}, bias);
+        helpers::addBias(block, *output, *bias, *output, isNCHW);
 
     if(!isNCHW)
         delete input;
@@ -338,7 +340,7 @@ static void depthwiseConv2d_(const NDArray* input, const NDArray* weights, const
 
 //////////////////////////////////////////////////////////////////////////
 void ConvolutionUtils::depthwiseConv2d(nd4j::graph::Context& block, const NDArray* input, const NDArray* weights, const NDArray* bias, NDArray* output, const int kH, const int kW, const int sH, const int sW, int pH, int pW, const int dH, const int dW, const int isSameMode, const int isNCHW) {
-    BUILD_SINGLE_SELECTOR_TWICE(input->dataType(), depthwiseConv2d_, (input, weights, bias, output, kH, kW, sH, sW, pH, pW, dH, dW, isSameMode, isNCHW), FLOAT_TYPES);
+    BUILD_SINGLE_SELECTOR_TWICE(input->dataType(), depthwiseConv2d_, (block, input, weights, bias, output, kH, kW, sH, sW, pH, pW, dH, dW, isSameMode, isNCHW), FLOAT_TYPES);
 }
 
 //////////////////////////////////////////////////////////////////////////
@@ -735,9 +737,9 @@ __global__ static void pooling3dCuda(const void* vx, const Nd4jLong* xShapeInfo,
 
     auto coords = sharedMem + threadIdx.x * rank;
 
-    shape::index2coords(rank, zShapeInfo + 1, zInd, zLen, coords);
+    shape::index2coords(zInd, zShapeInfo, coords);
 
-    const auto zOffset = shape::getOffset(0, zShapeInfo + 1, zShapeInfo + rank + 1, coords, rank);
+    const auto zOffset = shape::getOffset(zShapeInfo, coords);
 
     int dstart = coords[2] * sD - pD;
     int hstart = coords[3] * sH - pH;
@@ -768,7 +770,7 @@ __global__ static void pooling3dCuda(const void* vx, const Nd4jLong* xShapeInfo,
             for (coords[2] = dstart; coords[2] < dend; coords[2] += dD) {
                 for (coords[3] = hstart; coords[3] < hend; coords[3] += dH){
                     for (coords[4] = wstart; coords[4] < wend; coords[4] += dW) {
-                        T val = x[shape::getOffset(0, xShapeInfo + 1, xShapeInfo + rank + 1, coords, rank)];
+                        T val = x[shape::getOffset(xShapeInfo, coords)];
                         if (val > max)
                             max = val;
                     }
@@ -784,7 +786,7 @@ __global__ static void pooling3dCuda(const void* vx, const Nd4jLong* xShapeInfo,
             for (coords[2] = dstart; coords[2] < dend; coords[2] += dD)
                 for (coords[3] = hstart; coords[3] < hend; coords[3] += dH)
                     for (coords[4] = wstart; coords[4] < wend; coords[4] += dW)
-                        sum += x[shape::getOffset(0, xShapeInfo + 1, xShapeInfo + rank + 1, coords, rank)];
+                        sum += x[shape::getOffset(xShapeInfo, coords)];
 
             if (extraParam0 == 0) {         //Exclude padding
                 uint a = (dend - dstart) / dD + ((dend - dstart) % dD == 0 ? 0 : 1);
@@ -805,7 +807,7 @@ __global__ static void pooling3dCuda(const void* vx, const Nd4jLong* xShapeInfo,
             for (coords[2] = dstart; coords[2] < dend; coords[2] += dD)
                 for (coords[3] = hstart; coords[3] < hend; coords[3] += dH)
                     for (coords[4] = wstart; coords[4] < wend; coords[4] += dW)
-                        sum += nd4j::math::nd4j_pow<T,T,T>(nd4j::math::nd4j_abs<T>(x[shape::getOffset(0, xShapeInfo + 1, xShapeInfo + rank + 1, coords, rank)]), extraParam0);
+                        sum += nd4j::math::nd4j_pow<T,T,T>(nd4j::math::nd4j_abs<T>(x[shape::getOffset(xShapeInfo, coords)]), extraParam0);
 
             sum = nd4j::math::nd4j_pow<T,T,T>(sum, (T) 1.f / extraParam0);
 
@@ -885,9 +887,9 @@ __global__ static void pooling2dBPCuda(const void* vx, const Nd4jLong* xShapeInf
 
     auto coords = sharedMem + threadIdx.x * rank;
 
-    shape::index2coords(rank, yShapeInfo + 1, yInd, yLen, coords);
+    shape::index2coords(yInd, yShapeInfo, coords);
 
-    const auto yOffset = shape::getOffset(0, yShapeInfo + 1, yShapeInfo + rank + 1, coords, rank);
+    const auto yOffset = shape::getOffset(yShapeInfo, coords);
 
     int hstart = coords[2] * sH - pH;
     int wstart = coords[3] * sW - pW;
@@ -913,7 +915,7 @@ __global__ static void pooling2dBPCuda(const void* vx, const Nd4jLong* xShapeInf
             T max = -DataTypeUtils::max<T>();
             for (coords[2] = hstart; coords[2] < hend; coords[2] += dH) {
                 for (coords[3] = wstart; coords[3] < wend; coords[3] += dW){
-                    T val = x[shape::getOffset(0, xShapeInfo + 1, xShapeInfo + rank + 1, coords, rank)];
+                    T val = x[shape::getOffset(xShapeInfo, coords)];
                     if (val > max) {
                         max = val;
                         coord2 = coords[2];
@@ -923,7 +925,7 @@ __global__ static void pooling2dBPCuda(const void* vx, const Nd4jLong* xShapeInf
             }
             coords[2] = coord2;
             coords[3] = coord3;
-            auto zOffset = shape::getOffset(0, zShapeInfo + 1, zShapeInfo + rank + 1, coords, rank);
+            auto zOffset = shape::getOffset(zShapeInfo, coords);
             nd4j::math::atomics::nd4j_atomicAdd<T>(&z[zOffset], y[yOffset]);
             //z[zOffset] += y[yOffset];
         }
@@ -941,7 +943,7 @@ __global__ static void pooling2dBPCuda(const void* vx, const Nd4jLong* xShapeInf
 
             for (coords[2] = hstart; coords[2] < hend; coords[2] += dH)
                 for (coords[3] = wstart; coords[3] < wend; coords[3] += dW)
-                    nd4j::math::atomics::nd4j_atomicAdd<T>(&z[shape::getOffset(0, zShapeInfo + 1, zShapeInfo + rank + 1, coords, rank)], val);
+                    nd4j::math::atomics::nd4j_atomicAdd<T>(&z[shape::getOffset(zShapeInfo, coords)], val);
         }
         break;
 
@@ -953,14 +955,14 @@ __global__ static void pooling2dBPCuda(const void* vx, const Nd4jLong* xShapeInf
 
             for (coords[2] = hstart; coords[2] < hend; coords[2] += dH)
                 for (coords[3] = wstart; coords[3] < wend; coords[3] += dW)
-                    sum += nd4j::math::nd4j_pow<T,T,T>(nd4j::math::nd4j_abs<T>(x[shape::getOffset(0, xShapeInfo + 1, xShapeInfo + rank + 1, coords, rank)]), extraParam0);
+                    sum += nd4j::math::nd4j_pow<T,T,T>(nd4j::math::nd4j_abs<T>(x[shape::getOffset(xShapeInfo, coords)]), extraParam0);
 
             val *= nd4j::math::nd4j_pow<T,T,T>(sum, ((T)1.f - extraParam0) / extraParam0);
 
             for (coords[2] = hstart; coords[2] < hend; coords[2] += dH) {
                 for (coords[3] = wstart; coords[3] < wend; coords[3] += dW) {
-                    const auto xOffset = shape::getOffset(0, xShapeInfo + 1, xShapeInfo + rank + 1, coords, rank);
-                    const auto zOffset = shape::getOffset(0, zShapeInfo + 1, zShapeInfo + rank + 1, coords, rank);
+                    const auto xOffset = shape::getOffset(xShapeInfo, coords);
+                    const auto zOffset = shape::getOffset(zShapeInfo, coords);
                     nd4j::math::atomics::nd4j_atomicAdd<T>(&z[zOffset], val * nd4j::math::nd4j_pow<T,T,T>(nd4j::math::nd4j_abs<T>(x[xOffset]), extraParam0 - 1.f) * nd4j::math::nd4j_sgn<T,T>(x[xOffset]));
                 }
             }
@@ -1046,9 +1048,9 @@ __global__ static void pooling3dBPCuda(const void* vx, const Nd4jLong* xShapeInf
 
     auto coords = sharedMem + threadIdx.x * rank;
 
-    shape::index2coords(rank, yShapeInfo + 1, yInd, yLen, coords);
+    shape::index2coords(yInd, yShapeInfo, coords);
 
-    const auto yOffset = shape::getOffset(0, yShapeInfo + 1, yShapeInfo + rank + 1, coords, rank);
+    const auto yOffset = shape::getOffset(yShapeInfo, coords);
 
     int dstart = coords[2] * sD - pD;
     int hstart = coords[3] * sH - pH;
@@ -1080,7 +1082,7 @@ __global__ static void pooling3dBPCuda(const void* vx, const Nd4jLong* xShapeInf
             for (coords[2] = dstart; coords[2] < dend; coords[2] += dD) {
                 for (coords[3] = hstart; coords[3] < hend; coords[3] += dH){
                     for (coords[4] = wstart; coords[4] < wend; coords[4] += dW) {
-                        T val = x[shape::getOffset(0, xShapeInfo + 1, xShapeInfo + rank + 1, coords, rank)];
+                        T val = x[shape::getOffset(xShapeInfo, coords)];
                         if (val > max) {
                             max = val;
                             coord2 = coords[2];
@@ -1093,7 +1095,7 @@ __global__ static void pooling3dBPCuda(const void* vx, const Nd4jLong* xShapeInf
             coords[2] = coord2;
             coords[3] = coord3;
             coords[4] = coord4;
-            nd4j::math::atomics::nd4j_atomicAdd<T>(&z[shape::getOffset(0, zShapeInfo + 1, zShapeInfo + rank + 1, coords, rank)], y[yOffset]);
+            nd4j::math::atomics::nd4j_atomicAdd<T>(&z[shape::getOffset(zShapeInfo, coords)], y[yOffset]);
         }
         break;
 
@@ -1110,7 +1112,7 @@ __global__ static void pooling3dBPCuda(const void* vx, const Nd4jLong* xShapeInf
             for (coords[2] = dstart; coords[2] < dend; coords[2] += dD)
                 for (coords[3] = hstart; coords[3] < hend; coords[3] += dH)
                     for (coords[4] = wstart; coords[4] < wend; coords[4] += dW)
-                        nd4j::math::atomics::nd4j_atomicAdd<T>(&z[shape::getOffset(0, zShapeInfo + 1, zShapeInfo + rank + 1, coords, rank)], val);
+                        nd4j::math::atomics::nd4j_atomicAdd<T>(&z[shape::getOffset(zShapeInfo, coords)], val);
         }
         break;
 
@@ -1123,15 +1125,15 @@ __global__ static void pooling3dBPCuda(const void* vx, const Nd4jLong* xShapeInf
             for (coords[2] = dstart; coords[2] < dend; coords[2] += dD)
                 for (coords[3] = hstart; coords[3] < hend; coords[3] += dH)
                     for (coords[4] = wstart; coords[4] < wend; coords[4] += dW)
-                        sum += nd4j::math::nd4j_pow<T,T,T>(nd4j::math::nd4j_abs<T>(x[shape::getOffset(0, xShapeInfo + 1, xShapeInfo + rank + 1, coords, rank)]), extraParam0);
+                        sum += nd4j::math::nd4j_pow<T,T,T>(nd4j::math::nd4j_abs<T>(x[shape::getOffset(xShapeInfo, coords)]), extraParam0);
 
             val *= nd4j::math::nd4j_pow<T,T,T>(sum, ((T)1.f - extraParam0) / extraParam0);
 
             for (coords[2] = dstart; coords[2] < dend; coords[2] += dD) {
                 for (coords[3] = hstart; coords[3] < hend; coords[3] += dH) {
                     for (coords[4] = wstart; coords[4] < wend; coords[4] += dW) {
-                        const auto xOffset = shape::getOffset(0, xShapeInfo + 1, xShapeInfo + rank + 1, coords, rank);
-                        const auto zOffset = shape::getOffset(0, zShapeInfo + 1, zShapeInfo + rank + 1, coords, rank);
+                        const auto xOffset = shape::getOffset(xShapeInfo, coords);
+                        const auto zOffset = shape::getOffset(zShapeInfo, coords);
                         nd4j::math::atomics::nd4j_atomicAdd<T>(&z[zOffset], val * nd4j::math::nd4j_pow<T,T,T>(nd4j::math::nd4j_abs<T>(x[xOffset]), extraParam0 - 1.f) * nd4j::math::nd4j_sgn<T,T>(x[xOffset]));
                     }
                 }
@@ -1363,14 +1365,14 @@ __global__ static void upsampling2dCuda(const void* vx, const Nd4jLong* xShapeIn
 
     auto coords = sharedMem + threadIdx.x * rank;
 
-    shape::index2coords(rank, zShapeInfo + 1, zInd, zLen, coords);
+    shape::index2coords(zInd, zShapeInfo, coords);
 
-    const auto zOffset = shape::getOffset(0, zShapeInfo + 1, zShapeInfo + rank + 1, coords, rank);
+    const auto zOffset = shape::getOffset(zShapeInfo, coords);
 
     coords[dimIH]     /= factorH;
     coords[dimIH + 1] /= factorW;
 
-    const auto xOffset = shape::getOffset(0, xShapeInfo + 1, xShapeInfo + rank + 1, coords, rank);
+    const auto xOffset = shape::getOffset(xShapeInfo, coords);
 
     z[zOffset] = x[xOffset];
 }
@@ -1431,15 +1433,15 @@ __global__ static void upsampling3dCuda(const void* vx, const Nd4jLong* xShapeIn
 
     auto coords = sharedMem + threadIdx.x * rank;
 
-    shape::index2coords(rank, zShapeInfo + 1, zInd, zLen, coords);
+    shape::index2coords(zInd, zShapeInfo, coords);
 
-    const auto zOffset = shape::getOffset(0, zShapeInfo + 1, zShapeInfo + rank + 1, coords, rank);
+    const auto zOffset = shape::getOffset(zShapeInfo, coords);
 
     coords[dimID]     /= factorD;
     coords[dimID + 1] /= factorH;
     coords[dimID + 2] /= factorW;
 
-    const auto xOffset = shape::getOffset(0, xShapeInfo + 1, xShapeInfo + rank + 1, coords, rank);
+    const auto xOffset = shape::getOffset(xShapeInfo, coords);
 
     z[zOffset] = x[xOffset];
 }
@@ -1504,9 +1506,9 @@ __global__ static void upsampling2dBPCuda(const void* vx, const Nd4jLong* xShape
 
     auto coords = sharedMem + threadIdx.x * rank;
 
-    shape::index2coords(rank, zShapeInfo + 1, zInd, zLen, coords);
+    shape::index2coords(zInd, zShapeInfo, coords);
 
-    const auto zOffset = shape::getOffset(0, zShapeInfo + 1, zShapeInfo + rank + 1, coords, rank);
+    const auto zOffset = shape::getOffset(zShapeInfo, coords);
 
     z[zOffset] = 0;
 
@@ -1515,7 +1517,7 @@ __global__ static void upsampling2dBPCuda(const void* vx, const Nd4jLong* xShape
 
     for(coords[dimIH] = zCoord2; coords[dimIH] < zCoord2 + factorH; ++coords[dimIH])
         for(coords[dimIH + 1] = zCoord3; coords[dimIH + 1] < zCoord3 + factorW; ++coords[dimIH + 1])
-            z[zOffset] += x[shape::getOffset(0, xShapeInfo + 1, xShapeInfo + rank + 1, coords, rank)];
+            z[zOffset] += x[shape::getOffset(xShapeInfo, coords)];
 }
 
 //////////////////////////////////////////////////////////////////////////
@@ -1579,9 +1581,9 @@ __global__ static void upsampling3dBPCuda(const void* vx, const Nd4jLong* xShape
 
     auto coords = sharedMem + threadIdx.x * rank;
 
-    shape::index2coords(rank, zShapeInfo + 1, zInd, zLen, coords);
+    shape::index2coords(zInd, zShapeInfo, coords);
 
-    const auto zOffset = shape::getOffset(0, zShapeInfo + 1, zShapeInfo + rank + 1, coords, rank);
+    const auto zOffset = shape::getOffset(zShapeInfo, coords);
 
     z[zOffset] = 0;
 
@@ -1592,7 +1594,7 @@ __global__ static void upsampling3dBPCuda(const void* vx, const Nd4jLong* xShape
     for(coords[dimID] = zCoord2; coords[dimID] < zCoord2 + factorD; ++coords[dimID])
         for(coords[dimID + 1] = zCoord3; coords[dimID + 1] < zCoord3 + factorH; ++coords[dimID + 1])
             for(coords[dimID + 2] = zCoord4; coords[dimID + 2] < zCoord4 + factorW; ++coords[dimID + 2])
-                z[zOffset] += x[shape::getOffset(0, xShapeInfo + 1, xShapeInfo + rank + 1, coords, rank)];
+                z[zOffset] += x[shape::getOffset(xShapeInfo, coords)];
 }
 
 //////////////////////////////////////////////////////////////////////////
diff --git a/libnd4j/include/ops/declarable/helpers/cuda/cross.cu b/libnd4j/include/ops/declarable/helpers/cuda/cross.cu
index e95473739..1cd771b98 100644
--- a/libnd4j/include/ops/declarable/helpers/cuda/cross.cu
+++ b/libnd4j/include/ops/declarable/helpers/cuda/cross.cu
@@ -58,12 +58,12 @@ __global__ static void crossCuda(const void* vx, const Nd4jLong* xShapeInfo,
 
     for (uint i = tid; i < lenWithoutLastDim; i += totalThreads) {
 
-        shape::index2coords(rank - 1, shape::shapeOf(const_cast<Nd4jLong*>(xShapeInfo)), i, lenWithoutLastDim, coords);
+        shape::index2coords(i, rank - 1, xShapeInfo + 1, coords);
 
         coords[rank - 1] = 0;
 
-        auto xOffset = shape::getOffset(0, shape::shapeOf(const_cast<Nd4jLong*>(xShapeInfo)), shape::stride(const_cast<Nd4jLong*>(xShapeInfo)), coords, rank);
-        auto yOffset = shape::getOffset(0, shape::shapeOf(const_cast<Nd4jLong*>(yShapeInfo)), shape::stride(const_cast<Nd4jLong*>(yShapeInfo)), coords, rank);
+        auto xOffset = shape::getOffset(xShapeInfo, coords);
+        auto yOffset = shape::getOffset(yShapeInfo, coords);
 
         const auto x0 = x[xOffset];
         const auto y0 = y[yOffset];
@@ -80,7 +80,7 @@ __global__ static void crossCuda(const void* vx, const Nd4jLong* xShapeInfo,
 		const auto x2 = x[xOffset];
         const auto y2 = y[yOffset];
 
-        auto zOffset = shape::getOffset(0, shape::shapeOf(const_cast<Nd4jLong*>(zShapeInfo)), shape::stride(const_cast<Nd4jLong*>(zShapeInfo)), coords, rank);
+        auto zOffset = shape::getOffset(zShapeInfo, coords);
         z[zOffset] = x1 * y2 - x2 * y1;
 
         zOffset += shape::stride(const_cast<Nd4jLong*>(zShapeInfo))[rank - 1];
diff --git a/libnd4j/include/ops/declarable/helpers/cuda/diag.cu b/libnd4j/include/ops/declarable/helpers/cuda/diag.cu
index 0e861b866..265e47776 100644
--- a/libnd4j/include/ops/declarable/helpers/cuda/diag.cu
+++ b/libnd4j/include/ops/declarable/helpers/cuda/diag.cu
@@ -24,7 +24,14 @@
 namespace nd4j {
 namespace ops {
 namespace helpers {
-
+////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
+// diag functor cuda kernel
+// outputBuffer - output tensor buffer
+// outputShape - output tensor shape
+// inputBuffer - input tensor buffer - this tensor should be placed on diagonal position of output
+// inputShape - input tensor shape
+// inputLength - length for input tensor
+//
 template <typename T>
 static __global__ void diagFunctorKernel(void* outputBuffer, Nd4jLong* outputShape, void const* inputBuffer, Nd4jLong* inputShape, Nd4jLong inputLength) {
     __shared__ T *z;
@@ -41,12 +48,22 @@ static __global__ void diagFunctorKernel(void* outputBuffer, Nd4jLong* outputSha
 
     const auto tid = blockIdx.x * blockDim.x + threadIdx.x;
     const auto step = gridDim.x * blockDim.x;
-    for (int t = tid; t < inputLength; t += step) {
-        z[shape::getIndexOffset(t * (inputLength + 1), outputShape, outputLength)] = x[shape::getIndexOffset(t, inputShape, inputLength)]; //tX];
+
+    for (int t = tid; t < inputLength; t += step) { // for all vals in input, put all on diagonal position to output
+        z[shape::getIndexOffset(t * (inputLength + 1), outputShape)] = x[shape::getIndexOffset(t, inputShape)]; //tX];
     }
 
 }
 
+////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
+// diag part functor cuda kernel
+// outputBuffer - output tensor buffer - linear sequence of diagonal values
+// outputShape - output tensor shape
+// inputBuffer - input tensor buffer - this tensor should be placed on diagonal position of output
+// inputShape - input tensor shape
+// outputLength - given length of output
+// inputLength - given length for input tensor
+//
     template <typename T>
     static __global__ void diagPartFunctorKernel(void* outputBuffer, Nd4jLong* outputShape, void const* inputBuffer, Nd4jLong* inputShape, Nd4jLong outputLength, Nd4jLong inputLength) {
         __shared__ T *z;
@@ -61,10 +78,11 @@ static __global__ void diagFunctorKernel(void* outputBuffer, Nd4jLong* outputSha
 
         const auto tid = blockIdx.x * blockDim.x + threadIdx.x;
         const auto step = gridDim.x * blockDim.x;
-        Nd4jLong i = threadIdx.x * (outputLength + 1);
-        for (int t = tid; t < outputLength && i < inputLength; t += step) {
-            z[shape::getIndexOffset(t, outputShape, outputLength)] = x[shape::getIndexOffset(i, inputShape, inputLength)]; //tX];
-            i += outputLength + 1;
+        Nd4jLong i = threadIdx.x * (outputLength + 1); // pos to diagonal value
+        for (int t = tid; t < outputLength && i < inputLength; t += step) { // loop by output, but input matrix may not be square
+            // put diagonal val from input onto output
+            z[shape::getIndexOffset(t, outputShape)] = x[shape::getIndexOffset(i, inputShape)]; 
+            i += outputLength + 1; // shift to next diagonal value
         }
     }
 
@@ -81,6 +99,8 @@ static __global__ void diagFunctorKernel(void* outputBuffer, Nd4jLong* outputSha
         diagFunctorKernel<T><<<launchDims.x, launchDims.y, launchDims.z, *stream>>>(output->specialBuffer(), output->specialShapeInfo(), input->getSpecialBuffer(), input->getSpecialShapeInfo(), inputLength);
     }
 
+////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
+// diagFunctor - caller for diag functor processor
     void diagFunctor(nd4j::LaunchContext * context, const NDArray* input, NDArray* output) {
         auto xType = input->dataType();
 
@@ -89,6 +109,8 @@ static __global__ void diagFunctorKernel(void* outputBuffer, Nd4jLong* outputSha
 
     BUILD_SINGLE_TEMPLATE(template void _diagFunctor, (nd4j::LaunchContext * context, const NDArray* input, NDArray* output);, LIBND4J_TYPES);
 
+////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
+// diagPartFunctor - caller for diag part functor kernel
     template <typename T>
     void _diagPartFunctor(nd4j::LaunchContext * context, NDArray const* input, NDArray* output) {
         const int outLen = output->lengthOf();
@@ -102,7 +124,8 @@ static __global__ void diagFunctorKernel(void* outputBuffer, Nd4jLong* outputSha
         diagPartFunctorKernel<T><<<launchDims.x, launchDims.y, launchDims.z, *stream>>>(output->specialBuffer(), output->specialShapeInfo(), input->getSpecialBuffer(), input->getSpecialShapeInfo(), outLen, inLen);
     }
 
-
+////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
+// diagPartFunctor - caller for diag part functor processor
     void diagPartFunctor(nd4j::LaunchContext * context, NDArray const* input, NDArray* output) {
         auto zType = output->dataType();
         BUILD_SINGLE_SELECTOR(zType, _diagPartFunctor, (context, input, output), NUMERIC_TYPES);
diff --git a/libnd4j/include/ops/declarable/helpers/cuda/dilation2d.cu b/libnd4j/include/ops/declarable/helpers/cuda/dilation2d.cu
index de37ab276..92aa4c55a 100644
--- a/libnd4j/include/ops/declarable/helpers/cuda/dilation2d.cu
+++ b/libnd4j/include/ops/declarable/helpers/cuda/dilation2d.cu
@@ -72,7 +72,7 @@ __global__ static void dilation2dCuda(const void* vx, const Nd4jLong* xShapeInfo
     auto xzCoords = sharedMem + threadIdx.x * (xzRank + yRank);
     auto yCoords  = xzCoords + xzRank;
 
-    shape::index2coords(xzRank, zShapeInfo + 1, zInd, zLen, xzCoords);
+    shape::index2coords(zInd, zShapeInfo, xzCoords);
 
     const auto zOffset = shape::getOffset(zShapeInfo, xzCoords);
 
diff --git a/libnd4j/include/ops/declarable/helpers/cuda/dropout.cu b/libnd4j/include/ops/declarable/helpers/cuda/dropout.cu
index 5b4c27bd0..9b2a42d8f 100644
--- a/libnd4j/include/ops/declarable/helpers/cuda/dropout.cu
+++ b/libnd4j/include/ops/declarable/helpers/cuda/dropout.cu
@@ -41,7 +41,7 @@ namespace helpers {
 
             // if probability is ok - we're saving scaled value
             if (double(val) < probVal)
-                output[shape::getIndexOffset(e, outputShape, inLen)] = T(input[shape::getIndexOffset(e, inputShape, inLen)] / probVal);
+                output[shape::getIndexOffset(e, outputShape)] = T(input[shape::getIndexOffset(e, inputShape)] / probVal);
         }
     }
 
@@ -140,11 +140,11 @@ namespace helpers {
         auto step = blockDim.x * gridDim.x;
 
         for (int e = tid; e < len; e += step) {
-            const auto zOffset = shape::getIndexOffset(e, outputShape, len);
+            const auto zOffset = shape::getIndexOffset(e, outputShape);
 
             // if probability was non-zero on FF step, we'll scale grads back
             if (output[zOffset] != T(0.))
-                output[zOffset] = T(input[shape::getIndexOffset(e, gradOutShape, len)] / probValue);
+                output[zOffset] = T(input[shape::getIndexOffset(e, gradOutShape)] / probValue);
 
         }
     }
@@ -173,8 +173,8 @@ namespace helpers {
 
         for (auto e = tid; e < inLen; e += step) {
             T val = nodeRng->relativeT(e, T(0.f), T(1.f));
-            T xVal = input[shape::getIndexOffset(e, inputShape, inLen)];
-            output[shape::getIndexOffset(e, outputShape, inLen)] = (val >= T(probValue) ? T(alpha * beta + alpha1) : T(alpha * (double)xVal + alpha1));
+            T xVal = input[shape::getIndexOffset(e, inputShape)];
+            output[shape::getIndexOffset(e, outputShape)] = (val >= T(probValue) ? T(alpha * beta + alpha1) : T(alpha * (double)xVal + alpha1));
         }
     }
     template <typename T>
diff --git a/libnd4j/include/ops/declarable/helpers/cuda/dynamic.cu b/libnd4j/include/ops/declarable/helpers/cuda/dynamic.cu
index 75b541b72..c70283997 100644
--- a/libnd4j/include/ops/declarable/helpers/cuda/dynamic.cu
+++ b/libnd4j/include/ops/declarable/helpers/cuda/dynamic.cu
@@ -57,7 +57,7 @@ namespace nd4j {
                     for (Nd4jLong e = threadIdx.x; e < iLimit; e += blockDim.x) {
                         // load set of indices into shared memory
                         if (e < iLength)
-                            rawIndices[threadIdx.x] = i[shape::getIndexOffset(e, iShapeInfo, iLength)];
+                            rawIndices[threadIdx.x] = i[shape::getIndexOffset(e, iShapeInfo)];
                         __syncthreads();
 
                         // now we need to find out where our actual updates will be mapped
@@ -76,7 +76,7 @@ namespace nd4j {
                         // doing actual update
                         if (e < iLength)
                             if (trueIndices[threadIdx.x] >= 0) {
-                                z[trueIndices[threadIdx.x]] = x[shape::getIndexOffset(e, xShapeInfo, xLength)];
+                                z[trueIndices[threadIdx.x]] = x[shape::getIndexOffset(e, xShapeInfo)];
                             }
 
                         __syncthreads();
@@ -97,12 +97,12 @@ namespace nd4j {
                     int outCnt = 0;
 
                     for (Nd4jLong e = 0; e < iLength; e++) {
-                        if (indices[shape::getIndexOffset(e, iShapeInfo, iLength)] == i) {
+                        if (indices[shape::getIndexOffset(e, iShapeInfo)] == i) {
                             auto dx = x + xTadOffsets[e];
                             auto dz = z + zTadOffsets[i][outCnt++];
 
                             for (int f = threadIdx.x; f < xLength; f += blockDim.x) {
-                                dz[shape::getIndexOffset(f, zTadShapeInfos[i], xLength)] = dx[shape::getIndexOffset(f, xTadShapeInfo, xLength)];
+                                dz[shape::getIndexOffset(f, zTadShapeInfos[i])] = dx[shape::getIndexOffset(f, xTadShapeInfo)];
                             }
                         }
                     }
@@ -190,9 +190,9 @@ namespace nd4j {
                     auto iLength = shape::length(iShapeInfo);
 
                     for (int i = threadIdx.x; i < iLength; i += blockDim.x) {
-                        auto idx = indices[shape::getIndexOffset(i, iShapeInfo, iLength)];
+                        auto idx = indices[shape::getIndexOffset(i, iShapeInfo)];
                         if (idx >= 0 && idx < zLength)
-                            z[shape::getIndexOffset(idx, zShapeInfo, zLength)] = x[shape::getIndexOffset(i, xShapeInfo, iLength)];
+                            z[shape::getIndexOffset(idx, zShapeInfo)] = x[shape::getIndexOffset(i, xShapeInfo)];
                     }
                 }
             }
@@ -215,13 +215,13 @@ namespace nd4j {
                     auto xLength = shape::length(xShapeInfo);
 
                     for (int i = 0; i < iLength; i++) {
-                        auto idx = indices[shape::getIndexOffset(i, iShapeInfo, iLength)];
+                        auto idx = indices[shape::getIndexOffset(i, iShapeInfo)];
 
                         auto z = bz + zTadOffsets[idx];
                         auto x = reinterpret_cast<X*>(vx[e]) + xTadOffsets[e][i];
 
                         for (int f = threadIdx.x; f < zLength; f += blockDim.x) {
-                            z[shape::getIndexOffset(f, zTadShapeInfo, zLength)] = x[shape::getIndexOffset(f, xShapeInfo, xLength)];
+                            z[shape::getIndexOffset(f, zTadShapeInfo)] = x[shape::getIndexOffset(f, xShapeInfo)];
                         }
 
                         __syncthreads();
diff --git a/libnd4j/include/ops/declarable/helpers/cuda/extract_patches.cu b/libnd4j/include/ops/declarable/helpers/cuda/extract_patches.cu
index 9f6501cad..9470f21be 100644
--- a/libnd4j/include/ops/declarable/helpers/cuda/extract_patches.cu
+++ b/libnd4j/include/ops/declarable/helpers/cuda/extract_patches.cu
@@ -28,81 +28,31 @@
 namespace nd4j {
 namespace ops {
 namespace helpers {
-
-//    template <typename T>
-//    static __global__ void globalExtractPatchesKernel(bool theSame, int batchCount, int sizeRow, int sizeCol, int rowDim, int colDim, int outRowDim, int outColDim, int strideRow, int strideCol, int rateRow, int rateCol, int rowCast, int colCast, int lastDim, T* input, Nd4jLong* patchShape, Nd4jLong* inputOffsets, T* output, Nd4jLong* outTadShape, Nd4jLong* outputOffsets) {
-//    //globalExtractPatches_(void *vinput, Nd4jLong *xTadShape, Nd4jLong *xTadOffsets, void *voutput, Nd4jLong *zTadShape, Nd4jLong *zTadOffsets, const int numTads, const int sizeRow, const int sizeCol, const int stradeRow, const int stradeCol, const int rateRow, const int rateCol, const bool theSame, const int lastDim, const int rowDim, const int colDim) {
-//        const int warpSize = lastDim;
-//        const int tid = blockIdx.x * gridDim.x + threadIdx.x;
-//        const int warpIdx = tid / warpSize;
-//        const int warpPos = tid % warpSize;
-//        const int numWarps = 1; //(gridDim.x * blockDim.x) / warpSize;
-//        const int patchLength = shape::length(outTadShape);
+////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
+// extract patches kernel
+//      - theSame - SAME or VALID - output format
+//      - batchCount - batches - the first dimension of input
+//      - sizeRow, sizeCol - rows and cols sizes for batch
+//      - rowDim, colDim - rows and cols dimensions for input patches
+//      - outRowDim, outColDim - rows and cols dimensions for output patches
+//      - strideRow, strideCol - step between input elements with patches
+//      - rateRow, rateCol - counts for input patches
+//      - rowCast, colCast - shifts for output placement (1 or 0)
+//      - lastDim - last dimension of input/output
+//      - input - input tensor buffer
+//      - patchShape - input patch TAD shape
+//      - inputOffsets - input TAD offsets
+//      - output - output tensor buffer
+//      - outTadShape - output TAD shape
+//      - outputOffsets - output TAD offsets
 //
-//        auto xShape = shape::shapeOf(patchShape);
-//        auto xStride = shape::stride(patchShape);
-//        auto xRank = shape::rank(patchShape);
-//
-//        for (int e = 0; e < batchCount; e += numWarps) {
-//            auto patch = input + inputOffsets[e];
-//            auto matrix = output + outputOffsets[e];
-//            int iter = 0;
-//
-//            for (Nd4jLong i = 0; i < outRowDim; i++) {
-//                for (Nd4jLong j = 0; j < outColDim; j++) {
-//                    Nd4jLong pos = 0;
-//                    //for (Nd4jLong k = 0; k < outputLastDim; k++) {
-//                    auto rowStart = i * strideRow - (theSame?rowCast:0);
-//                    auto colStart = j * strideCol - (theSame?colCast:0);
-//                    auto rowEnd = rowStart + sizeRow * rateRow;
-//                    auto colEnd = colStart + sizeCol * rateCol;
-//                    if (!theSame) {
-//                        rowEnd = math::nd4j_min(int(rowStart + sizeRow * rateRow), rowDim);
-//                        colEnd = math::nd4j_min(int(colStart + sizeCol * rateCol), colDim);
-//                    }
-//                    //auto pixel = 0LL;
-//                    for (auto row = rowStart; row < rowEnd; row += rateRow)
-//                        for (auto col = colStart; col < colEnd; col += rateCol)
-//                            for (auto pixel = 0; pixel < lastDim; pixel++) {
-//                                Nd4jLong zPos[] = {i, j, pos};
-//                                Nd4jLong xPos[] = {row, col, pixel};
-//                                auto zIndex = shape::getOffset(0, shape::shapeOf(outTadShape), shape::stride(outTadShape), zPos, 3);
-//                                auto xIndex = shape::getOffset(0, shape::shapeOf(patchShape), shape::stride(patchShape), xPos, 3);
-//                                if (theSame) { // SAME case
-//                                    if (row >= 0 && col >= 0 && row < rowDim && col < colDim)
-//                                        matrix[zIndex] = patch[xIndex]; //outMatrix->p<T>(i, j, pos, patch->e<T>(row, col, pixel));
-//                                    //pos++;
-//                                }
-//                                else { // VALID case
-//                                    matrix[zIndex] = patch[xIndex]; //outMatrix->p<T>(i, j, pos++, patch->e<T>(row, col, pixel));
-//                                }
-//                                pos++;
-//                            }
-//                }
-//            }
-//            __syncthreads();
-//        }
-//    }
-
     template <typename T>
     static __global__ void globalExtractPatchesKernel(bool theSame, int batchCount, int sizeRow, int sizeCol, int rowDim, int colDim, int outRowDim, int outColDim, int strideRow, int strideCol, int rateRow, int rateCol, int rowCast, int colCast, int lastDim, T* input, Nd4jLong* patchShape, Nd4jLong* inputOffsets, T* output, Nd4jLong* outTadShape, Nd4jLong* outputOffsets) {
-        __shared__ Nd4jLong* xShapeOf;
-        __shared__ Nd4jLong* xStrideOf;
-        __shared__ Nd4jLong* zShapeOf;
-        __shared__ Nd4jLong* zStrideOf;
-
-        if (0 == threadIdx.x) {
-            xShapeOf = shape::shapeOf(patchShape);
-            xStrideOf = shape::stride(patchShape);
-            zShapeOf = shape::shapeOf(outTadShape);
-            zStrideOf = shape::stride(outTadShape);
-        }
-        __syncthreads();
 
         auto start = threadIdx.x + blockIdx.x * blockDim.x;
 
         auto step = blockDim.x * gridDim.x;
-
+        // batch  input by 3 last dims and extrapole input onto output with outColDim/outRowDim
         for (Nd4jLong batch = start; batch < batchCount; batch += step) {
             auto patch = input + inputOffsets[batch];// listOfMatricies->at(batch);
             auto outMatrix = output + outputOffsets[batch]; //listOfOutputs->at(batch);
@@ -110,7 +60,6 @@ namespace helpers {
             for (Nd4jLong i = 0; i < outRowDim; i++) {
                 for (Nd4jLong j = 0; j < outColDim; j++) {
                     Nd4jLong pos = 0;
-                    //for (Nd4jLong k = 0; k < outputLastDim; k++) {
                     auto rowStart = i * strideRow - (theSame?rowCast:0);
                     auto colStart = j * strideCol - (theSame?colCast:0);
                     auto rowEnd = rowStart + sizeRow * rateRow;
@@ -119,25 +68,29 @@ namespace helpers {
                         rowEnd = math::nd4j_min(rowStart + sizeRow * rateRow, Nd4jLong (rowDim));
                         colEnd = math::nd4j_min(colStart + sizeCol * rateCol, Nd4jLong (colDim));
                     }
-                    //auto pixel = 0LL;
-                    for (auto row = rowStart; row < rowEnd; row += rateRow)
-                        for (auto col = colStart; col < colEnd; col += rateCol)
+
+                    for (auto row = rowStart; row < rowEnd; row += rateRow) {
+                        for (auto col = colStart; col < colEnd; col += rateCol) {
                             for (auto pixel = 0; pixel < lastDim; pixel++) {
                                 Nd4jLong zPos[] = {i, j, pos};
                                 Nd4jLong xPos[] = {row, col, pixel};
-                                bool setUp = (theSame && row >= 0 && col >= 0 && row < rowDim && col < colDim) || (!theSame);
+                                bool setUp =
+                                        (theSame && row >= 0 && col >= 0 && row < rowDim && col < colDim) || (!theSame);
 
                                 if (setUp) { // VALID or SAME cases
-                                    outMatrix[shape::getOffset(0, zShapeOf, zStrideOf, zPos, 3)] = patch[shape::getOffset(0, xShapeOf, xStrideOf, xPos, 3)];
+                                    outMatrix[shape::getOffset(outTadShape, zPos)] = patch[shape::getOffset(patchShape, xPos)];
                                 }
                                 pos++;
                             }
+                        }
+                    }
                 }
             }
         }
 
     }
 
+////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
     template <typename T>
     static void _extractPatches(nd4j::LaunchContext * context, NDArray* images, NDArray* output, int sizeRow, int sizeCol, int strideRow, int strideCol, int rateRow, int rateCol, bool theSame){
         NDArray::prepareSpecialUse({output}, {images});
@@ -153,36 +106,29 @@ namespace helpers {
         Nd4jLong colDim = images->sizeAt(2);
         Nd4jLong outRowDim = output->sizeAt(1);
         Nd4jLong outColDim = output->sizeAt(2);
-        auto rowCast = 1; //(sizeRow - 1)*rateRow < outRowDim/sizeRow  ?0:1;///(ksize * lastDim > rowDim * ksizeColsEffective + lastDim?1:0);
-        auto colCast = 1; //colDim / ksizeColsEffective +2 <= sizeCol?0:1;//(ksize * lastDim > ksizeRowsEffective * colDim + lastDim?1:0);
+        auto rowCast = 1;
+        auto colCast = 1;
+        // validate shifts
         if (sizeRow * rateRow < 3)
             rowCast = 0;
         if (sizeCol * rateCol < 3)
             colCast = 0;
-        //images->tickReadDevice();
-        //if (images->isActualOnDeviceSide())
-        //images->syncToDevice();
 
         auto packX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(images->getShapeInfo(), restDims.data(), restDims.size());
         auto packZ = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(output->getShapeInfo(), restDims.data(), restDims.size());
-        int batchCount = packX.numberOfTads(); //'listOfMatricies->size(); //lengthOf() / ksize;
-        //printf("Batch Count is %d\n", batchCount);
-        //shape::printShapeInfo(packX.primaryShapeInfo());
-        //NDArray::prepareSpecialUse({output}, {images});
+        int batchCount = packX.numberOfTads();
+
         PointersManager manager(context, "helpers::extractPatches");
 
         auto stream = context->getCudaStream();
         auto imagesBuffer = reinterpret_cast<T*>(images->specialBuffer());
         auto outputBuffer = reinterpret_cast<T*>(output->specialBuffer());
-        //images->printIndexedBuffer("INPUT");
-//        globalExtractPatchesKernel<T><<<512, 512, 1024, *context->getCudaStream()>>>(theSame, batchCount, sizeRow, sizeCol,
+
         globalExtractPatchesKernel<T><<<128, 128, 1024, *stream>>>(theSame, batchCount, sizeRow, sizeCol,
                 rowDim, colDim, outRowDim, outColDim, strideRow, strideCol, rateRow, rateCol, rowCast, colCast, lastDim,
                 imagesBuffer, packX.specialShapeInfo(), packX.specialOffsets(), outputBuffer, packZ.specialShapeInfo(),
                 packZ.specialOffsets());
-        //extractPatchesKernel<T><<<batchCount, 512, 1024, *stream>>>(theSame, batchCount, sizeRow, sizeCol, rowDim, colDim, outRowDim, outColDim, stradeRow, stradeCol, rateRow, rateCol, rowCast, colCast, lastDim, imagesBuffer, packX.specialShapeInfo(), packX.platformOffsets(), outputBuffer, packZ.specialShapeInfo(), packZ.platformOffsets());
-        //output->tickWriteDevice();
-        //output->printIndexedBuffer("OUTPUT");
+
         manager.synchronize();
         NDArray::registerSpecialUse({output}, {images});
     }
@@ -195,76 +141,6 @@ namespace helpers {
 
         BUILD_SINGLE_SELECTOR(xType, _extractPatches, (context, images, output, sizeRow, sizeCol, stradeRow, stradeCol, rateRow, rateCol, theSame), LIBND4J_TYPES);
     }
-//        std::vector<int> restDims({1, 2, 3}); // the first and the last dims
-//        std::unique_ptr<ResultSet> listOfMatricies(images->allTensorsAlongDimension(restDims));
-//        std::unique_ptr<ResultSet> listOfOutputs(output->allTensorsAlongDimension(restDims));
-//        // 3D matricies - 2D matricies of vectors (if last dim is greater than 1)
-//        //int e = 0;
-//        const int ksizeRowsEffective = sizeRow + (sizeRow - 1) * (rateRow - 1);
-//        const int ksizeColsEffective = sizeCol + (sizeCol - 1) * (rateCol - 1);
-//        const int ksize = ksizeRowsEffective * ksizeColsEffective;
-//        int batchCount = listOfMatricies->size(); //lengthOf() / ksize;
-//        Nd4jLong lastDim = images->sizeAt(3);
-//        Nd4jLong outLastDim = output->sizeAt(3);
-//        Nd4jLong rowDim = images->sizeAt(1);
-//        Nd4jLong colDim = images->sizeAt(2);
-//        Nd4jLong outRowDim = output->sizeAt(1);
-//        Nd4jLong outColDim = output->sizeAt(2);
-//        auto rowCast = 1; //(sizeRow - 1)*rateRow < outRowDim/sizeRow  ?0:1;///(ksize * lastDim > rowDim * ksizeColsEffective + lastDim?1:0);
-//        auto colCast = 1; //colDim / ksizeColsEffective +2 <= sizeCol?0:1;//(ksize * lastDim > ksizeRowsEffective * colDim + lastDim?1:0);
-//        if (sizeRow * rateRow < 3)
-//            rowCast = 0;
-//        if (sizeCol * rateCol < 3)
-//            colCast = 0;
-//        //Nd4jLong outputLastDim = output->sizeAt(3);
-//       PRAGMA_OMP_PARALLEL_FOR
-//        for (Nd4jLong batch = 0; batch < batchCount; batch++) {
-//            auto patch = listOfMatricies->at(batch);
-//            auto outMatrix = listOfOutputs->at(batch);
-//            //auto patchBorder = patch->sizeAt(0);
-//            if (theSame) { // SAME case
-//                for (Nd4jLong i = 0; i < outRowDim; i++) {
-//                    for (Nd4jLong j = 0; j < outColDim; j++) {
-//                        Nd4jLong pos = 0;
-//                        //for (Nd4jLong k = 0; k < outputLastDim; k++) {
-//                        auto rowStart = i * strideRow - rowCast;
-//                        auto colStart = j * strideCol - colCast;
-//                        auto rowEnd = rowStart + sizeRow * rateRow;
-//                        auto colEnd = colStart + sizeCol * rateCol;
-//                        auto pixel = 0LL;
-//                        for (auto row = rowStart; row < rowEnd; row += rateRow)
-//                            for (auto col = colStart; col < colEnd; col += rateCol)
-//                                for (auto pixel = 0; pixel < lastDim; pixel++) {
-//                                    if (row >=0 && col >= 0 && row < rowDim && col < colDim)
-//                                    outMatrix->p<T>(i, j, pos, patch->e<T>(row, col, pixel));
-//                                    pos++;
-//                                }
-//                        //}
-//                    }
-//                }
-//
-//            } else { // VALID case
-//                for (Nd4jLong i = 0; i < outRowDim; i++) {
-//                    for (Nd4jLong j = 0; j < outColDim; j++) {
-//                        Nd4jLong pos = 0;
-//                        //for (Nd4jLong k = 0; k < outputLastDim; k++) {
-//                            auto rowStart = i * strideRow;
-//                            auto colStart = j * strideCol;
-//                            auto rowEnd = math::nd4j_min(rowStart + sizeRow * rateRow, rowDim);
-//                            auto colEnd = math::nd4j_min(colStart + sizeCol * rateCol, colDim);
-//                            auto pixel = 0LL;
-//                            for (auto row = rowStart; row < rowEnd; row += rateRow)
-//                                for (auto col = colStart; col < colEnd; col += rateCol)
-//                                    for (auto pixel = 0; pixel < lastDim; pixel++)
-//                                        outMatrix->p<T>(i,j,pos++, patch->e<T>(row, col, pixel));
-//                        //}
-//                    }
-//                }
-//            }
-//        }
-//
-//
-//
 }
 }
 }
\ No newline at end of file
diff --git a/libnd4j/include/ops/declarable/helpers/cuda/fake_quantization.cu b/libnd4j/include/ops/declarable/helpers/cuda/fake_quantization.cu
index 317edefb5..9bb331685 100644
--- a/libnd4j/include/ops/declarable/helpers/cuda/fake_quantization.cu
+++ b/libnd4j/include/ops/declarable/helpers/cuda/fake_quantization.cu
@@ -24,70 +24,68 @@
 namespace nd4j {
 namespace ops {
 namespace helpers {
-
+////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
+// fakeQuantWithMinMaxVars_
+// input - input tensor
+// min - min scalar tensor
+// max - max scalar tensor
+// numBits - (default 16bit)
+// narrowed - shrink is true
+// output - output tensor
+//
     template <typename T>
     void fakeQuantWithMinMaxVars_(NDArray* input, NDArray* min, NDArray* max, int numBits, bool narrowed, NDArray* output) {
         int lowIntBound = narrowed?1:0;
         int upperIntBound = 1 << numBits - 1;
-
+        min->syncToHost();
+        max->syncToHost();
         const float quant_min_float = static_cast<float>(lowIntBound);
         const float quant_max_float = static_cast<float>(upperIntBound);
         T scale = (max->t<T>(0) - min->t<T>(0)) / (quant_max_float - quant_min_float);
-        const T zero_point_from_min = quant_min_float - min->e<T>(0) / scale;
+        const T zero_point_from_min = quant_min_float - min->t<T>(0) / scale;
+
         const uint16_t nudged_zero_point = [zero_point_from_min, lowIntBound,
                                         quant_min_float, upperIntBound,
                                         quant_max_float] {
-        if (zero_point_from_min < quant_min_float) {
-          return static_cast<uint16_t>(lowIntBound);
-        }
-        if (zero_point_from_min > quant_max_float) {
-          return static_cast<uint16_t>(upperIntBound);
-        }
-        return static_cast<uint16_t>(roundf(zero_point_from_min));
+            if (zero_point_from_min < quant_min_float) {
+                return static_cast<uint16_t>(lowIntBound);
+            }
+            if (zero_point_from_min > quant_max_float) {
+                return static_cast<uint16_t>(upperIntBound);
+            }
+            return static_cast<uint16_t>(roundf(zero_point_from_min));
         }();
 
         auto nudged_min = (quant_min_float - nudged_zero_point) * (scale);
         auto nudged_max = (quant_max_float - nudged_zero_point) * (scale);
-        //input->applyScalar(scalar::CompareAndSet, nudged_max, clamped, nullptr); //.cwiseMin(nudged_max).cwiseMax(nudged_min);
-        //input->applyScalar(scalar::CompareAndSet, nudged_min, clamped, nullptr); //.cwiseMin(nudged_max).cwiseMax(nudged_min);
+
         auto wiseMax = LAMBDA_T(x, nudged_min) {
             if (x < nudged_min) {
                 return nudged_min;
             }
             return x;
-
         };
+
         auto wiseMin = LAMBDA_T(x, nudged_max) {
             if (x > nudged_max) {
                 return nudged_max;
             }
             return x;
         };
-        auto scaleTensor(*input); // = NDArrayFactory::create(input->ordering(), input->getShapeAsVector(), input->getWorkspace());
-        auto clamped(*input); // = NDArrayFactory::create(input->ordering(), input->getShapeAsVector(), input->getWorkspace());
+
+        auto scaleTensor(*input);
+        auto clamped(*input);
         scaleTensor.assign(scale);
         input->applyLambda(wiseMin, &clamped);
-//        const auto clamped = inputs.cwiseMin(nudged_max).cwiseMax(nudged_min);
+
         clamped.applyLambda(wiseMax, output);
-//        const auto clamped_shifted = clamped - nudged_min;
         *output -= nudged_min;
-       // auto nudgedScale = scale;
+
         (*output) /= scaleTensor;
         (*output) += T(0.5f);
         output->applyTransform(transform::Floor, nullptr, nullptr);
         (*output) *= scaleTensor;
         (*output) += nudged_min;
-        //output->printIndexedBuffer("FAKE QUANTED");
-        /*
-    const auto nudged_scale_repl = inputs.constant(nudged_scale);
-
-    const auto clamped = inputs.cwiseMin(nudged_max).cwiseMax(nudged_min);
-    const auto clamped_shifted = clamped - nudged_min;
-    *output = (clamped_shifted / nudged_scale_repl + 0.5f).floor() *
-                            nudged_scale_repl +
-                        nudged_min;
-*/
-
     }
 
     void fakeQuantWithMinMaxVars(NDArray* input, NDArray* min, NDArray* max, int numBits, bool narrowed, NDArray* output) {
diff --git a/libnd4j/include/ops/declarable/helpers/cuda/flatten.cu b/libnd4j/include/ops/declarable/helpers/cuda/flatten.cu
index 6a818a2cd..df4e25130 100644
--- a/libnd4j/include/ops/declarable/helpers/cuda/flatten.cu
+++ b/libnd4j/include/ops/declarable/helpers/cuda/flatten.cu
@@ -35,17 +35,11 @@ namespace nd4j {
 
                     auto xBuffer = reinterpret_cast<T*>(xBuffers[e]);
                     auto xShapeInfo = xShapeInfos[e];
-                    auto xShape = shape::shapeOf(xShapeInfo);
-                    auto xStride = shape::stride(xShapeInfo);
-                    auto xRank = shape::rank(xShapeInfo);
                     auto xLength = shape::length(xShapeInfo);
 
                     // each element of this input array has own place within common output array
-                    for (uint i = threadIdx.x; i < xLength; i += blockDim.x) {
-                        shape::index2coords(xRank, xShape, i, xLength, xCoord, order);
-                        auto xOffset = shape::getOffset(0, xShape, xStride, xCoord, xRank);
-                        z[i] = xBuffer[xOffset];
-                    }
+                    for (uint i = threadIdx.x; i < xLength; i += blockDim.x)
+                        z[i] = xBuffer[getIndexOffsetOrdered(i, xShapeInfo, order)];
                 }
             }
 
diff --git a/libnd4j/include/ops/declarable/helpers/cuda/gather.cu b/libnd4j/include/ops/declarable/helpers/cuda/gather.cu
index 4eb5450a3..308e58814 100644
--- a/libnd4j/include/ops/declarable/helpers/cuda/gather.cu
+++ b/libnd4j/include/ops/declarable/helpers/cuda/gather.cu
@@ -52,10 +52,9 @@ namespace helpers {
     auto step = blockDim.x * gridDim.x;
 
     for (int j = start; j < zLen; j += step) {
-        auto zIndex = shape::getIndexOffset(j, zShapeInfo, zLen);
-        auto yIndex = shape::getIndexOffset(j, yShapeInfo, yLen);
-        auto xIndex = shape::getIndexOffset(y[yIndex], xShapeInfo, xLen);
-        //printf("%lld , %lld\n", zIndex, xIndex);
+        auto zIndex = shape::getIndexOffset(j, zShapeInfo);
+        auto yIndex = shape::getIndexOffset(j, yShapeInfo);
+        auto xIndex = shape::getIndexOffset(y[yIndex], xShapeInfo);
         z[zIndex] = x[xIndex];
     }
 }
@@ -76,15 +75,14 @@ __global__ static void gatherCuda(const int numOfSubArrs,
     for (int i = blockIdx.x; i < numOfSubArrs; i += gridDim.x) {
 
         if (threadIdx.x == 0) {
-            x = reinterpret_cast<const X*>(vx) + xOffsets[y[shape::getIndexOffset(i, yShapeInfo, numOfSubArrs)]];
+            x = reinterpret_cast<const X*>(vx) + xOffsets[y[shape::getIndexOffset(i, yShapeInfo)]];
             z = reinterpret_cast<X*>(vz) + zOffsets[i];
         }
         __syncthreads();
 
         for (int j = threadIdx.x; j < len; j += blockDim.x) {
-            auto zIndex = shape::getIndexOffset(j, zShapeInfo, len);
-            auto xIndex = shape::getIndexOffset(j, xShapeInfo, len);
-            //printf("%lld , %lld\n", zIndex, xIndex);
+            auto zIndex = shape::getIndexOffset(j, zShapeInfo);
+            auto xIndex = shape::getIndexOffset(j, xShapeInfo);
             z[zIndex] = x[xIndex];
         }
         __syncthreads();
diff --git a/libnd4j/include/ops/declarable/helpers/cuda/gather_nd.cu b/libnd4j/include/ops/declarable/helpers/cuda/gather_nd.cu
index 6587b4ca7..11ba6571b 100644
--- a/libnd4j/include/ops/declarable/helpers/cuda/gather_nd.cu
+++ b/libnd4j/include/ops/declarable/helpers/cuda/gather_nd.cu
@@ -83,9 +83,9 @@ namespace nd4j {
 
                 for (Nd4jLong i = tid; i < zLen; i += totalThreads) {
 
-                    shape::index2coords(zRank, zShapeInfo + 1, i, zLen, zCoordStart);
+                    shape::index2coords(i, zShapeInfo, zCoordStart);
 
-                    const auto zOffset = shape::getOffset(0, zShapeInfo + 1, zShapeInfo + zRank + 1, zCoordStart, zRank);
+                    const auto zOffset = shape::getOffset(zShapeInfo, zCoordStart);
 
                     // last y coordinate
                     int coordToRestore;
@@ -93,7 +93,7 @@ namespace nd4j {
                         coordToRestore = static_cast<int>(zCoordStart[yRank - 1]);
 
                     zCoordStart[yRank - 1] = 0; // last y coordinate
-                    const auto yOffset = shape::getOffset(0, yShapeInfo + 1, yShapeInfo + yRank + 1, zCoordStart, yRank);
+                    const auto yOffset = shape::getOffset(yShapeInfo, zCoordStart);
 
                     //restore z coordinate
                     if(yLastDim != xRank)
@@ -103,7 +103,7 @@ namespace nd4j {
                     for(uint j = 0; j < yLastDim; ++j)
                         xCoordStart[j] = y[yOffset + j * yShapeInfo[2 * yRank]];   // last stride
 
-                    const auto xOffset = shape::getOffset(0, xShapeInfo + 1, xShapeInfo + xRank + 1, xCoordStart, xRank);
+                    const auto xOffset = shape::getOffset(xShapeInfo, xCoordStart);
 
                     z[zOffset] = x[xOffset];
                     printf("z[%lld] = x[%lld] = %f\n", zOffset, xOffset, (float) z[zOffset]);
diff --git a/libnd4j/include/ops/declarable/helpers/cuda/hamming.cu b/libnd4j/include/ops/declarable/helpers/cuda/hamming.cu
index 3bc30e373..9802ff231 100644
--- a/libnd4j/include/ops/declarable/helpers/cuda/hamming.cu
+++ b/libnd4j/include/ops/declarable/helpers/cuda/hamming.cu
@@ -43,8 +43,8 @@ namespace nd4j {
 
                 auto tid = threadIdx.x + blockIdx.x * blockDim.x;
                 for (Nd4jLong e = tid; e < length; e += blockDim.x * gridDim.x) {
-                    auto _x = static_cast<unsigned long long>(x[shape::getIndexOffset(e, xShapeInfo, length)]);
-                    auto _y = static_cast<unsigned long long>(y[shape::getIndexOffset(e, yShapeInfo, length)]);
+                    auto _x = static_cast<unsigned long long>(x[shape::getIndexOffset(e, xShapeInfo)]);
+                    auto _y = static_cast<unsigned long long>(y[shape::getIndexOffset(e, yShapeInfo)]);
 
                     // we save intermediate result into shared memory
                     shared[threadIdx.x] += __popcll(_x ^ _y);
diff --git a/libnd4j/include/ops/declarable/helpers/cuda/histogramFixedWidth.cu b/libnd4j/include/ops/declarable/helpers/cuda/histogramFixedWidth.cu
index 317f1d857..07d7bcd93 100644
--- a/libnd4j/include/ops/declarable/helpers/cuda/histogramFixedWidth.cu
+++ b/libnd4j/include/ops/declarable/helpers/cuda/histogramFixedWidth.cu
@@ -55,7 +55,7 @@ __global__ static void histogramFixedWidthCuda( const void* vx, const Nd4jLong*
 
     for (Nd4jLong i = tid; i < xLen; i += totalThreads) {
 
-        const X value = x[shape::getIndexOffset(i, xShapeInfo, xLen)];
+        const X value = x[shape::getIndexOffset(i, xShapeInfo)];
 
         Nd4jLong zIndex;
 
@@ -66,7 +66,7 @@ __global__ static void histogramFixedWidthCuda( const void* vx, const Nd4jLong*
         else
             zIndex = static_cast<Nd4jLong>((value - leftEdge) / binWidth);
 
-        nd4j::math::atomics::nd4j_atomicAdd<Z>(&z[shape::getIndexOffset(zIndex, zShapeInfo, nbins)], 1);
+        nd4j::math::atomics::nd4j_atomicAdd<Z>(&z[shape::getIndexOffset(zIndex, zShapeInfo)], 1);
     }
 }
 
@@ -101,7 +101,7 @@ void histogramFixedWidth(nd4j::LaunchContext* context, const NDArray& input, con
 //         const auto tid = blockIdx.x * gridDim.x + threadIdx.x;
 //         const auto step = gridDim.x * blockDim.x;
 //         for (int t = tid; t < bufferLength; t += step) {
-//             destination[t] = reinterpret_cast<T const*>(source)[shape::getIndexOffset(t, sourceShape, bufferLength)];
+//             destination[t] = reinterpret_cast<T const*>(source)[shape::getIndexOffset(t, sourceShape)];
 //         }
 //     }
 
@@ -110,7 +110,7 @@ void histogramFixedWidth(nd4j::LaunchContext* context, const NDArray& input, con
 //         const auto tid = blockIdx.x * gridDim.x + threadIdx.x;
 //         const auto step = gridDim.x * blockDim.x;
 //         for (int t = tid; t < bufferLength; t += step) {
-//             reinterpret_cast<T*>(destination)[shape::getIndexOffset(t, destinationShape, bufferLength)] = source[t];
+//             reinterpret_cast<T*>(destination)[shape::getIndexOffset(t, destinationShape)] = source[t];
 //         }
 //     }
 
@@ -130,7 +130,7 @@ void histogramFixedWidth(nd4j::LaunchContext* context, const NDArray& input, con
 
 //         for(auto i = tid; i < inputLength; i += step) {
 
-//             const T value = x[shape::getIndexOffset(i, inputShape, inputLength)];
+//             const T value = x[shape::getIndexOffset(i, inputShape)];
 //             Nd4jLong currInd = static_cast<Nd4jLong>((value - leftEdge) / binWidth);
 
 //             if(value < secondEdge)
diff --git a/libnd4j/include/ops/declarable/helpers/cuda/im2col.cu b/libnd4j/include/ops/declarable/helpers/cuda/im2col.cu
index f2fb9d94a..62fcd0588 100644
--- a/libnd4j/include/ops/declarable/helpers/cuda/im2col.cu
+++ b/libnd4j/include/ops/declarable/helpers/cuda/im2col.cu
@@ -64,9 +64,9 @@ __global__ static void im2colCuda(const void *image, void *columns,
 
     auto coords = sharedMem + threadIdx.x * colRank;
 
-    shape::index2coords(colRank, colShapeInfo + 1, colInd, colLen, coords);
+    shape::index2coords(colInd, colShapeInfo, coords);
 
-    const auto colOffset = shape::getOffset(0, colShapeInfo + 1, colShapeInfo + colRank + 1, coords, colRank);
+    const auto colOffset = shape::getOffset(colShapeInfo, coords);
 
     coords[2] = (-pH + coords[2] * dH) + coords[4] * sH;   // imH
     coords[3] = (-pW + coords[3] * dW) + coords[5] * sW;   // imW
@@ -74,7 +74,7 @@ __global__ static void im2colCuda(const void *image, void *columns,
     if (static_cast<unsigned>(coords[2]) >= static_cast<unsigned>(iH) || static_cast<unsigned>(coords[3]) >= static_cast<unsigned>(iW))
         col[colOffset] = zeroPadVal;
     else
-        col[colOffset] = im[shape::getOffset(0, imShapeInfo + 1, imShapeInfo + imRank + 1, coords, imRank)];
+        col[colOffset] = im[shape::getOffset(imShapeInfo, coords)];
 }
 
 
diff --git a/libnd4j/include/ops/declarable/helpers/cuda/image_resize.cu b/libnd4j/include/ops/declarable/helpers/cuda/image_resize.cu
index 431524bf3..5e57a9a90 100644
--- a/libnd4j/include/ops/declarable/helpers/cuda/image_resize.cu
+++ b/libnd4j/include/ops/declarable/helpers/cuda/image_resize.cu
@@ -32,7 +32,13 @@ namespace helpers {
         // https://en.wikipedia.org/wiki/Bilinear_interpolation)
         double interpolarValue;
     };
-
+////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
+// computeInterpolationWeights kernel
+//      outSize - output length
+//      inSize - input size
+//      scale - input scale
+//      interporationData - result
+//
     static __global__ void computeInterpolationWeights(Nd4jLong outSize,
                                               Nd4jLong inSize,
                                               double scale,
@@ -54,21 +60,26 @@ namespace helpers {
             }
         }
     }
-
+////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
+// resize image with bilinear interpolation algorithm
+//
     static void resizeImage(nd4j::LaunchContext* context, NDArray const* images, Nd4jLong batchSize, Nd4jLong inHeight, Nd4jLong inWidth, Nd4jLong outHeight,
                      Nd4jLong outWidth, Nd4jLong channels,
                      BilinearInterpolationData* xs_,
                      BilinearInterpolationData* ys_,
                      NDArray* output);
 
+////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
+// resize image with bilinear interpolation algorithm kernel
+//
     template <typename T>
     static __global__ void resizeImageKernel(T const* input, Nd4jLong const* inputShape, T* outputYptr, Nd4jLong* outputShape, Nd4jLong batchSize,
                                              Nd4jLong outWidth, Nd4jLong outHeight, Nd4jLong channels, Nd4jLong inRowSize, Nd4jLong outRowSize, Nd4jLong inBatchNumValues,
                                              BilinearInterpolationData* xs_, BilinearInterpolationData* ys_) {
 
-        if (blockIdx.x < batchSize) {
+        if (blockIdx.x < batchSize) { // blockIdx.x as batch index
             auto pX = input + blockIdx.x * inBatchNumValues;
-            //auto pZ = output_y_ptr;
+
             auto channelStart = blockIdx.z * blockDim.z + threadIdx.z;
             auto step = blockDim.z * gridDim.z;
             for (Nd4jLong y = threadIdx.x; y < outHeight; y += blockDim.x) {
@@ -80,6 +91,7 @@ namespace helpers {
                     auto xsBottom = xs_[x].bottomIndex;
                     auto xsTop = xs_[x].topIndex;
                     auto xVal = xs_[x].interpolarValue;
+                    // process interpolation for all channels
                     for (int c = channelStart; c < channels; c += step) {
                         double topLeft(ys_input_lower_ptr[xsBottom + c]);
                         double topRight(ys_input_lower_ptr[xsTop + c]);
@@ -87,13 +99,15 @@ namespace helpers {
                         double bottomRight(ys_input_upper_ptr[xsTop + c]);
                         double top = topLeft + (topRight - topLeft) * xVal;
                         double bottom = bottomLeft + (bottomRight - bottomLeft) * xVal;
-                        pZ[x * channels + c] = top + (bottom - top) * yVal;
+                        pZ[x * channels + c] = T(top + (bottom - top) * yVal);
                     }
                 }
             }
         }
     }
 
+////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
+// resize image with
     template <typename T>
     static void resizeImage_(nd4j::LaunchContext* context, NDArray const* images, Nd4jLong batchSize, Nd4jLong inHeight, Nd4jLong inWidth, Nd4jLong outHeight,
                      Nd4jLong outWidth, Nd4jLong channels,
@@ -111,7 +125,8 @@ namespace helpers {
                 outWidth, outHeight, channels, inRowSize, outRowSize, inBatchNumValues, xs_, ys_);
     }
 
-    template <typename T>
+////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
+        template <typename T>
     static int resizeBilinearFunctor_(nd4j::LaunchContext* context, NDArray const* images, int width, int height, bool center, NDArray* output) {
         const Nd4jLong batchSize = images->sizeAt(0);
         const Nd4jLong inHeight = images->sizeAt(1);
@@ -174,6 +189,10 @@ namespace helpers {
 
         return Status::OK();
     }
+
+////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
+// resize by interpolation nearest neighbor algorithm kernel
+//
     template <typename T>
     static __global__ void resizeNeighborKernel(T const* input, Nd4jLong* inputShape, T* output, Nd4jLong* outputShape,
             Nd4jLong batchSize, Nd4jLong inWidth, Nd4jLong inHeight, Nd4jLong outWidth, Nd4jLong outHeight, Nd4jLong channels, double widthScale, double heightScale, bool center) {
@@ -196,8 +215,8 @@ namespace helpers {
                     for (Nd4jLong e = start; e < channels; e += step) {
                         Nd4jLong posX[] = {b, inY, inX, e};
                         Nd4jLong posZ[] = {b, y, x, e};
-                        auto xIndex = shape::getOffset(0, shape::shapeOf(inputShape), shape::stride(inputShape), posX, 4);
-                        auto zIndex = shape::getOffset(0, shape::shapeOf(outputShape), shape::stride(outputShape), posZ, 4);
+                        auto xIndex = shape::getOffset(inputShape, posX);
+                        auto zIndex = shape::getOffset(outputShape, posZ);
                         output[zIndex] = input[xIndex];
                     }
                 }
@@ -206,6 +225,9 @@ namespace helpers {
 
     }
 
+////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
+// resizeNeighborFunctor - main algorithm by nearest neighbor
+//
     template <typename T>
     int resizeNeighborFunctor_(nd4j::LaunchContext* context, NDArray const* images, int width, int height, bool center, NDArray* output) {
         const Nd4jLong batchSize = images->sizeAt(0);
@@ -243,10 +265,11 @@ namespace helpers {
                 batchSize, inWidth, inHeight, outWidth, outHeight, channels, widthScale, heightScale, center);
         NDArray::registerSpecialUse({output}, {images});
 
-        return ND4J_STATUS_OK;
-
         return Status::OK();
     }
+////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
+// resizeImage - resize bilinear algorithm caller
+//
     void resizeImage(nd4j::LaunchContext* context, NDArray const* images, Nd4jLong batchSize, Nd4jLong inHeight,
             Nd4jLong inWidth, Nd4jLong outHeight, Nd4jLong outWidth, Nd4jLong channels, BilinearInterpolationData* xs_,
             BilinearInterpolationData* ys_, NDArray* output) {
@@ -257,21 +280,25 @@ namespace helpers {
             Nd4jLong batchSize, Nd4jLong inHeight, Nd4jLong inWidth, Nd4jLong outHeight, Nd4jLong outWidth,
             Nd4jLong channels, BilinearInterpolationData* xs_, BilinearInterpolationData* ys_, NDArray* output), LIBND4J_TYPES);
 
+////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
     int resizeBilinearFunctor(nd4j::LaunchContext* context, NDArray const* images, int width, int height, bool center, NDArray* output) {
         BUILD_SINGLE_SELECTOR(images->dataType(), return resizeBilinearFunctor_, (context, images, width, height, center, output), LIBND4J_TYPES);
     }
     BUILD_SINGLE_TEMPLATE(template int resizeBilinearFunctor_, (nd4j::LaunchContext* context, NDArray const* images, int width, int height, bool center, NDArray* output), LIBND4J_TYPES);
 
+////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
     int resizeNeighborFunctor(nd4j::LaunchContext* context, NDArray const* images, int width, int height, bool center, NDArray* output) {
         BUILD_SINGLE_SELECTOR(images->dataType(), return resizeNeighborFunctor_, (context, images, width, height, center, output), LIBND4J_TYPES);
     }
     BUILD_SINGLE_TEMPLATE(template int resizeNeighborFunctor_, (nd4j::LaunchContext* context, NDArray const* images,
             int width, int height, bool center, NDArray* output), LIBND4J_TYPES);
 
+////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
     // --------------------------------------------------------------------------------------------------------------- //
     // Crop and Resize helper implementation
     // --------------------------------------------------------------------------------------------------------------- //
-    ///////
+    // cropAndResize kernel
+    //
     template <typename T, typename Z, typename I>
     static __global__ void cropAndResizeKernel(T const *images, Nd4jLong* imagesShape, Z const* boxes, Nd4jLong* boxesShape,
             I const* indices, Nd4jLong* indexShape, I const* cropSize, Nd4jLong* cropShape, int method,
@@ -284,10 +311,10 @@ namespace helpers {
             Nd4jLong y1Pos[] = {b, 0};
             Nd4jLong y2Pos[] = {b, 2};
             Nd4jLong x2Pos[] = {b, 3};
-            Z y1 = boxes[shape::getOffset(0, shape::shapeOf(boxesShape), shape::stride(boxesShape), y1Pos, 2)];//->t<T>(b, 0)];
-            Z x1 = boxes[shape::getOffset(0, shape::shapeOf(boxesShape), shape::stride(boxesShape), x1Pos, 2)];
-            Z y2 = boxes[shape::getOffset(0, shape::shapeOf(boxesShape), shape::stride(boxesShape), y2Pos, 2)];
-            Z x2 = boxes[shape::getOffset(0, shape::shapeOf(boxesShape), shape::stride(boxesShape), x2Pos, 2)];
+            Z y1 = boxes[shape::getOffset(boxesShape, y1Pos)];//->t<T>(b, 0)];
+            Z x1 = boxes[shape::getOffset(boxesShape, x1Pos)];
+            Z y2 = boxes[shape::getOffset(boxesShape, y2Pos)];
+            Z x2 = boxes[shape::getOffset(boxesShape, x2Pos)];
 
             int bIn = indices[b];
             if (bIn >= batchSize) {
@@ -297,7 +324,6 @@ namespace helpers {
             Z heightScale = (cropHeight > 1) ? (y2 - y1) * (imageHeight - 1) / Z(cropHeight - 1) : Z(0);
             Z widthScale = (cropWidth > 1) ? (x2 - x1) * (imageWidth - 1) / Z(cropWidth - 1) : Z(0);
 
-//            PRAGMA_OMP_PARALLEL_FOR_SIMD
             for (int y = threadIdx.x; y < cropHeight; y += blockDim.x) {
                 const float inY = (cropHeight > 1)
                                   ? y1 * (imageHeight - 1) + y * heightScale
@@ -308,13 +334,14 @@ namespace helpers {
                         auto step = blockDim.z * gridDim.z;
                         for (int d = start; d < depth; d += step) {
                             Nd4jLong zPos[] = {b, y, x, d};
-                            auto zIndex = shape::getOffset(0, shape::shapeOf(outputShape), shape::stride(outputShape), zPos, 4);
+                            auto zIndex = shape::getOffset(outputShape, zPos);
                             output[zIndex] = (Z)extrapolationVal;
                             //crops->p(b, y, x, d, extrapolationVal);
                         }
                     }
                     continue;
                 }
+
                 if (method == 0 /* bilinear */) {
                     const int topYIndex = nd4j::math::p_floor(inY);
                     const int bottomYIndex = nd4j::math::p_ceil(inY);
@@ -329,7 +356,7 @@ namespace helpers {
                             auto step = blockDim.z * gridDim.z;
                             for (int d = start; d < depth; d += step) {
                                 Nd4jLong zPos[] = {b, y, x, d};
-                                auto zIndex = shape::getOffset(0, shape::shapeOf(outputShape), shape::stride(outputShape), zPos, 4);
+                                auto zIndex = shape::getOffset(outputShape, zPos);
                                 output[zIndex] = (Z)extrapolationVal;
 //                                crops->p(b, y, x, d, extrapolationVal);
                             }
@@ -346,16 +373,15 @@ namespace helpers {
                             Nd4jLong topRightPos[] = {bIn, topYIndex, right_x_index, d};
                             Nd4jLong bottomLeftPos[] = {bIn, bottomYIndex, left_x_index, d};
                             Nd4jLong bottomRightPos[] = {bIn, bottomYIndex, right_x_index, d};
-                            const T topLeft(images[shape::getOffset(0, shape::shapeOf(imagesShape), shape::stride(imagesShape), topLeftPos, 4)]); //->e<float>(bIn, topYIndex, left_x_index, d));
-                            const T topRight(images[shape::getOffset(0, shape::shapeOf(imagesShape), shape::stride(imagesShape), topRightPos, 4)]); //->e<float>(bIn, topYIndex, right_x_index, d));
-                            const T bottomLeft(images[shape::getOffset(0, shape::shapeOf(imagesShape), shape::stride(imagesShape), bottomLeftPos, 4)]);//->e<float>(bIn, bottomYIndex, left_x_index, d));
-                            const T bottomRight(images[shape::getOffset(0, shape::shapeOf(imagesShape), shape::stride(imagesShape), bottomRightPos, 4)]); //->e<float>(bIn, bottomYIndex, right_x_index, d));
+                            const T topLeft(images[shape::getOffset(imagesShape, topLeftPos)]); //->e<float>(bIn, topYIndex, left_x_index, d));
+                            const T topRight(images[shape::getOffset(imagesShape, topRightPos)]); //->e<float>(bIn, topYIndex, right_x_index, d));
+                            const T bottomLeft(images[shape::getOffset(imagesShape, bottomLeftPos)]);//->e<float>(bIn, bottomYIndex, left_x_index, d));
+                            const T bottomRight(images[shape::getOffset(imagesShape, bottomRightPos)]); //->e<float>(bIn, bottomYIndex, right_x_index, d));
                             const T top = topLeft + (topRight - topLeft) * x_lerp;
                             const T bottom = bottomLeft + (bottomRight - bottomLeft) * x_lerp;
                             Nd4jLong zPos[] = {b, y, x, d};
-                            auto zIndex = shape::getOffset(0, shape::shapeOf(outputShape), shape::stride(outputShape), zPos, 4);
+                            auto zIndex = shape::getOffset(outputShape, zPos);
                             output[zIndex] = Z(top + (bottom - top) * y_lerp);
-//                            crops->p(b, y, x, d, top + (bottom - top) * y_lerp);
                         }
                     }
                 } else {  // method is "nearest neighbor"
@@ -368,7 +394,7 @@ namespace helpers {
                             auto step = blockDim.z * gridDim.z;
                             for (int d = start; d < depth; d += step) {
                                 Nd4jLong zPos[] = {b, y, x, d};
-                                auto zIndex = shape::getOffset(0, shape::shapeOf(outputShape), shape::stride(outputShape), zPos, 4);
+                                auto zIndex = shape::getOffset(outputShape, zPos);
                                 output[zIndex] = (Z)extrapolationVal;
                             }
                             continue;
@@ -380,10 +406,9 @@ namespace helpers {
                         for (int d = start; d < depth; d += step) {
                             Nd4jLong zPos[] = {b, y, x, d};
                             Nd4jLong xPos[] = {bIn, closestYIndex, closestXIndex, d};
-                            auto zIndex = shape::getOffset(0, shape::shapeOf(outputShape), shape::stride(outputShape), zPos, 4);
-                            auto xIndex = shape::getOffset(0, shape::shapeOf(imagesShape), shape::stride(imagesShape), xPos, 4);
+                            auto zIndex = shape::getOffset(outputShape, zPos);
+                            auto xIndex = shape::getOffset(imagesShape, xPos);
                             output[zIndex] = images[xIndex];
-//                            crops->p(b, y, x, d, images->e<T>(bIn, closestYIndex, closestXIndex, d));
                         }
                     }
                 }
@@ -392,6 +417,17 @@ namespace helpers {
 
     }
 
+////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
+// cropAndResizeFunctor main algorithm
+//      context - launch context
+//      images - batch of images (4D tensor - [batch, width, height, pixels])
+//      boxes - 2D tensor with boxes for crop
+//      indices - 2D int tensor with indices of boxes to crop
+//      cropSize - 2D int tensor with crop box sizes
+//      method - (one of 0 - bilinear, 1 - nearest)
+//      extrapolationVal - double value of extrapolation
+//      crops - output (4D tensor - [batch, outWidth, outHeight, pixels])
+//
     template <typename T, typename Z, typename I>
     static void cropAndResizeFunctor_(nd4j::LaunchContext* context, NDArray const *images, NDArray const *boxes, NDArray const *indices,
                                       NDArray const *cropSize, int method, double extrapolationVal, NDArray *crops) {
@@ -416,6 +452,7 @@ namespace helpers {
         NDArray::registerSpecialUse({crops}, {images, boxes, indices, cropSize});
     }
 
+////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
     void cropAndResizeFunctor(nd4j::LaunchContext * context, NDArray const *images, NDArray const *boxes, NDArray const *indices, NDArray const *cropSize, int method, double extrapolationVal, NDArray *crops) {
         BUILD_TRIPLE_SELECTOR(images->dataType(), boxes->dataType(), indices->dataType(), cropAndResizeFunctor_,
                               (context, images, boxes, indices, cropSize, method, extrapolationVal, crops), NUMERIC_TYPES, FLOAT_TYPES, INTEGER_TYPES);
diff --git a/libnd4j/include/ops/declarable/helpers/cuda/image_suppression.cu b/libnd4j/include/ops/declarable/helpers/cuda/image_suppression.cu
index d96c1efa2..3393a61e3 100644
--- a/libnd4j/include/ops/declarable/helpers/cuda/image_suppression.cu
+++ b/libnd4j/include/ops/declarable/helpers/cuda/image_suppression.cu
@@ -26,7 +26,16 @@
 namespace nd4j {
 namespace ops {
 namespace helpers {
-
+////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
+// needToSuppressWithThreshold - predicate for suppression
+//      boxes - boxes tensor buffer
+//      boxesShape boxes tensor shape
+//      previousIndex - index for current pos value
+//      nextIndex - index for neighbor pos value
+//      threshold - threashold value to suppress
+//
+//      return value: true, if threshold is overcome, false otherwise
+//
     template <typename T>
     static __device__ bool needToSuppressWithThreshold(T* boxes, Nd4jLong* boxesShape, int previousIndex, int nextIndex, T threshold) {
         Nd4jLong previous0[] = {previousIndex, 0};
@@ -37,22 +46,26 @@ namespace helpers {
         Nd4jLong next1[] = {nextIndex, 1};
         Nd4jLong next2[] = {nextIndex, 2};
         Nd4jLong next3[] = {nextIndex, 3};
-        Nd4jLong* shapeOf = shape::shapeOf(boxesShape);
-        Nd4jLong* strideOf = shape::stride(boxesShape);
-        T minYPrev = nd4j::math::nd4j_min(boxes[shape::getOffset(0, shapeOf, strideOf, previous0, 2)], boxes[shape::getOffset(0, shapeOf, strideOf, previous2, 2)]);
-        T minXPrev = nd4j::math::nd4j_min(boxes[shape::getOffset(0, shapeOf, strideOf, previous1, 2)], boxes[shape::getOffset(0, shapeOf, strideOf, previous3, 2)]);
-        T maxYPrev = nd4j::math::nd4j_max(boxes[shape::getOffset(0, shapeOf, strideOf, previous0, 2)], boxes[shape::getOffset(0, shapeOf, strideOf, previous2, 2)]);
-        T maxXPrev = nd4j::math::nd4j_max(boxes[shape::getOffset(0, shapeOf, strideOf, previous1, 2)], boxes[shape::getOffset(0, shapeOf, strideOf, previous3, 2)]);
-        T minYNext = nd4j::math::nd4j_min(boxes[shape::getOffset(0, shapeOf, strideOf, next0, 2)],     boxes[shape::getOffset(0, shapeOf, strideOf, next2, 2)]);
-        T minXNext = nd4j::math::nd4j_min(boxes[shape::getOffset(0, shapeOf, strideOf, next1, 2)],     boxes[shape::getOffset(0, shapeOf, strideOf, next3, 2)]);
-        T maxYNext = nd4j::math::nd4j_max(boxes[shape::getOffset(0, shapeOf, strideOf, next0, 2)],     boxes[shape::getOffset(0, shapeOf, strideOf, next2, 2)]);
-        T maxXNext = nd4j::math::nd4j_max(boxes[shape::getOffset(0, shapeOf, strideOf, next1, 2)],     boxes[shape::getOffset(0, shapeOf, strideOf, next3, 2)]);
 
+        // we have rectangle with given max values. Compute vexes of rectangle first
+
+        T minYPrev = nd4j::math::nd4j_min(boxes[shape::getOffset(boxesShape, previous0)], boxes[shape::getOffset(boxesShape, previous2)]);
+        T minXPrev = nd4j::math::nd4j_min(boxes[shape::getOffset(boxesShape, previous1)], boxes[shape::getOffset(boxesShape, previous3)]);
+        T maxYPrev = nd4j::math::nd4j_max(boxes[shape::getOffset(boxesShape, previous0)], boxes[shape::getOffset(boxesShape, previous2)]);
+        T maxXPrev = nd4j::math::nd4j_max(boxes[shape::getOffset(boxesShape, previous1)], boxes[shape::getOffset(boxesShape, previous3)]);
+        T minYNext = nd4j::math::nd4j_min(boxes[shape::getOffset(boxesShape, next0)],     boxes[shape::getOffset(boxesShape, next2)]);
+        T minXNext = nd4j::math::nd4j_min(boxes[shape::getOffset(boxesShape, next1)],     boxes[shape::getOffset(boxesShape, next3)]);
+        T maxYNext = nd4j::math::nd4j_max(boxes[shape::getOffset(boxesShape, next0)],     boxes[shape::getOffset(boxesShape, next2)]);
+        T maxXNext = nd4j::math::nd4j_max(boxes[shape::getOffset(boxesShape, next1)],     boxes[shape::getOffset(boxesShape, next3)]);
+
+        // compute areas for comparation
         T areaPrev = (maxYPrev - minYPrev) * (maxXPrev - minXPrev);
         T areaNext = (maxYNext - minYNext) * (maxXNext - minXNext);
 
+        // of course, areas should be positive
         if (areaNext <= T(0.f) || areaPrev <= T(0.f)) return false;
 
+        // compute intersection of rectangles
         T minIntersectionY = nd4j::math::nd4j_max(minYPrev, minYNext);
         T minIntersectionX = nd4j::math::nd4j_max(minXPrev, minXNext);
         T maxIntersectionY = nd4j::math::nd4j_min(maxYPrev, maxYNext);
@@ -61,9 +74,15 @@ namespace helpers {
                 nd4j::math::nd4j_max(T(maxIntersectionY - minIntersectionY), T(0.0f)) *
                 nd4j::math::nd4j_max(T(maxIntersectionX - minIntersectionX), T(0.0f));
         T intersectionValue = intersectionArea / (areaPrev + areaNext - intersectionArea);
+        // final check
         return intersectionValue > threshold;
-    };
+    }
 
+////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
+// shouldSelectKernel - compute status for all selected rectangles (boxes)
+//
+// we compute boolean flag as shared uint32 and return it on final only for the first thread
+//
     template <typename T, typename I>
     static __global__ void shouldSelectKernel(T* boxesBuf, Nd4jLong* boxesShape, I* indexBuf, I* selectedIndicesData, double threshold, int numSelected, int i, bool* shouldSelect) {
         auto tid = blockIdx.x * blockDim.x + threadIdx.x;
@@ -77,15 +96,20 @@ namespace helpers {
             if (shouldSelectShared) {
                 if (needToSuppressWithThreshold(boxesBuf, boxesShape, indexBuf[i],
                                                                   indexBuf[selectedIndicesData[j]], T(threshold)))
-                    atomicCAS(&shouldSelectShared, 1, 0);
+                    atomicCAS(&shouldSelectShared, 1, 0); // exchange only when need to suppress
             }
         }
         __syncthreads();
+
+        // final move: collect result
         if (threadIdx.x == 0) {
             *shouldSelect = shouldSelectShared > 0;
         }
     }
 
+////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
+// indices - type depended, indicesLong - type defined (only 64bit integers)
+//
     template <typename I>
     static __global__ void copyIndices(void* indices,  void* indicesLong, Nd4jLong len) {
         I* indexBuf = reinterpret_cast<I*>(indices);
@@ -97,7 +121,9 @@ namespace helpers {
         for (auto i = tid; i < len; i += step)
             indexBuf[i] = (I)srcBuf[i];
     }
-
+////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
+// nonMaxSuppressionV2 algorithm - given from TF NonMaxSuppressionV2 implementation
+//
     template <typename T, typename I>
     static void nonMaxSuppressionV2_(nd4j::LaunchContext* context, NDArray* boxes, NDArray* scales, int maxSize, double threshold, NDArray* output) {
         auto stream = context->getCudaStream();
@@ -110,8 +136,7 @@ namespace helpers {
         Nd4jPointer extras[2] = {nullptr, stream};
 
         sortByValue(extras, indices->buffer(), indices->shapeInfo(), indices->specialBuffer(), indices->specialShapeInfo(), scores.buffer(), scores.shapeInfo(), scores.specialBuffer(), scores.specialShapeInfo(), true);
-        // TO DO: sort indices using scales as value row
-        //std::sort(indices.begin(), indices.end(), [scales](int i, int j) {return scales->e<T>(i) > scales->e<T>(j);});
+
         auto indexBuf = reinterpret_cast<I*>(indices->specialBuffer());
 
         NDArray selectedIndices = NDArrayFactory::create<I>('c', {output->lengthOf()});
@@ -155,6 +180,7 @@ namespace helpers {
         }
 
     }
+////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
 
     void nonMaxSuppressionV2(nd4j::LaunchContext * context, NDArray* boxes, NDArray* scales, int maxSize, double threshold, NDArray* output) {
         BUILD_DOUBLE_SELECTOR(boxes->dataType(), output->dataType(), nonMaxSuppressionV2_, (context, boxes, scales, maxSize, threshold, output), FLOAT_TYPES, INDEXING_TYPES);
diff --git a/libnd4j/include/ops/declarable/helpers/cuda/legacy_helper.cu b/libnd4j/include/ops/declarable/helpers/cuda/legacy_helper.cu
index defdfaf09..8db1f66d4 100644
--- a/libnd4j/include/ops/declarable/helpers/cuda/legacy_helper.cu
+++ b/libnd4j/include/ops/declarable/helpers/cuda/legacy_helper.cu
@@ -26,6 +26,7 @@ namespace nd4j {
 namespace ops {
 namespace helpers {
 
+////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
     template <typename T>
     linkage void cubeDerivative_(NDArray* input, NDArray* epsilon, NDArray* output) {
         auto functor = LAMBDA_TT(x, y){
@@ -35,10 +36,12 @@ namespace helpers {
         input->applyPairwiseLambda(epsilon, functor, output);
     }
 
+////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
     void cubeDerivative(nd4j::LaunchContext * context, NDArray* theFirst, NDArray* theSecond, NDArray* theOutput) {
         BUILD_SINGLE_SELECTOR(theFirst->dataType(), cubeDerivative_, (theFirst, theSecond, theOutput), FLOAT_TYPES);
     }
 
+////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
     //return (x >= X(0.f) ? y: -y);
     template <typename T>
     linkage void reduceNorm1_(NDArray* input, NDArray* epsilon, NDArray* output) {
@@ -49,10 +52,12 @@ namespace helpers {
         input->applyPairwiseLambda(epsilon, functor, output);
     }
 
+////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
     void reduceNorm1(nd4j::LaunchContext * context, NDArray* theFirst, NDArray* theSecond, NDArray* theOutput) {
         BUILD_SINGLE_SELECTOR(theFirst->dataType(), reduceNorm1_, (theFirst, theSecond, theOutput), FLOAT_TYPES);
     }
 
+////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
     ////////////////////////////////////////////////////////////////////////
     template <typename T>
     linkage void sigmCrossEntropy_(NDArray* logits, NDArray* labels, NDArray* output) {
@@ -63,10 +68,12 @@ namespace helpers {
         logits->applyPairwiseLambda(labels, functor, output);
     }
 
+////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
     void sigmCrossEntropy(nd4j::LaunchContext * context, NDArray* logits, NDArray* labels, NDArray* output) {
         BUILD_SINGLE_SELECTOR(logits->dataType(), sigmCrossEntropy_, (logits, labels, output), FLOAT_TYPES);
     }
 
+////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
     ////////////////////////////////////////////////////////////////////////
     template <typename T>
     linkage void sigmCrossEntropyGrad_(NDArray* logits, NDArray* labels, NDArray* output) {
@@ -80,14 +87,15 @@ namespace helpers {
 
         logits->applyPairwiseLambda(labels, functor, output);
     }
-
+////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
     void sigmCrossEntropyGrad(nd4j::LaunchContext * context, NDArray* logits, NDArray* labels, NDArray* output) {
         BUILD_SINGLE_SELECTOR(logits->dataType(), sigmCrossEntropyGrad_, (logits, labels, output), FLOAT_TYPES);
     }
 
+////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
     //            X f = (X) 1.0f + nd4j::math::nd4j_abs<X>(d1);
     //            return (X) d2 * ((X) 1.0f / (f * f));
-
+    //
     template <typename T>
     linkage void softSignDerivative_(NDArray* input, NDArray* epsilon, NDArray* output) {
         auto functor = LAMBDA_TT(x, y){
@@ -98,10 +106,12 @@ namespace helpers {
         input->applyPairwiseLambda(epsilon, functor, output);
     }
 
+////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
     void softSignDerivative(nd4j::LaunchContext * context, NDArray* theFirst, NDArray* theSecond, NDArray* theOutput) {
         BUILD_SINGLE_SELECTOR(theFirst->dataType(), softSignDerivative_, (theFirst, theSecond, theOutput), FLOAT_TYPES);
     }
 
+////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
     template <typename T>
     linkage void softPlusDerivative_(NDArray* input, NDArray* epsilon, NDArray* output) {
         auto functor = LAMBDA_TT(x, y){
@@ -115,10 +125,11 @@ namespace helpers {
     void softPlusDerivative(nd4j::LaunchContext * context, NDArray* theFirst, NDArray* theSecond, NDArray* theOutput) {
         BUILD_SINGLE_SELECTOR(theFirst->dataType(), softPlusDerivative_, (theFirst, theSecond, theOutput), FLOAT_TYPES);
     }
+////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
 ///
-/// \param theFirst
-/// \param theSecond
-/// \param theOutput
+/// \param input
+/// \param epsilon
+/// \param output
     template <typename T>
     linkage void sigmoidDerivative_(NDArray* input, NDArray* epsilon, NDArray* output) {
         auto functor = LAMBDA_TT(x, y){
@@ -146,6 +157,7 @@ namespace helpers {
         BUILD_SINGLE_SELECTOR(theFirst->dataType(), hardSigmoidDerivative_, (theFirst, theSecond, theOutput), FLOAT_TYPES);
     }
 
+////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
     template <typename T>
     linkage void logSumExp_(NDArray* input, NDArray* axis, NDArray* output) {
         // reduce along axis with
@@ -178,15 +190,17 @@ namespace helpers {
         output->applyTransform(transform::Log, nullptr, nullptr);
     }
 
+////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
     void logSumExp(nd4j::LaunchContext * context, NDArray* input, NDArray* axis, NDArray* output) {
         BUILD_SINGLE_SELECTOR(input->dataType(), logSumExp_, (input, axis, output), FLOAT_TYPES);
     }
 
+////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
     void logSumExp(nd4j::LaunchContext * context, NDArray* input, NDArray* subtrah, NDArray* axis, NDArray* output) {
         BUILD_SINGLE_SELECTOR(input->dataType(), logSumExp_, (input, subtrah, axis, output), FLOAT_TYPES);
     }
 
-//////////////////////////////////////////////////////////////////////////
+////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
     template <typename T>
     void weightedCrossEntropyWithLogitsFunctor_(NDArray const* targets, NDArray const* input, NDArray const* weights, NDArray* output) {
 
@@ -220,15 +234,14 @@ namespace helpers {
             const_cast<NDArray*>(input)->applyTriplewiseLambda(const_cast<NDArray*>(targets), targetTensor.get(), mainRoutineT2, output);
         }
     }
+////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
+    void weightedCrossEntropyWithLogitsFunctor(nd4j::LaunchContext * context, NDArray const* targets, NDArray const* input, NDArray const* weights, NDArray* output) {
+        NDArray::prepareSpecialUse({output}, {targets, input, weights});
 
-void weightedCrossEntropyWithLogitsFunctor(nd4j::LaunchContext * context, NDArray const* targets, NDArray const* input, NDArray const* weights, NDArray* output) {
-    NDArray::prepareSpecialUse({output}, {targets, input, weights});
-
-    BUILD_SINGLE_SELECTOR(targets->dataType(), weightedCrossEntropyWithLogitsFunctor_, (targets, input, weights, output), FLOAT_TYPES);
-
-    NDArray::registerSpecialUse({output}, {targets, input, weights});
-}
+        BUILD_SINGLE_SELECTOR(targets->dataType(), weightedCrossEntropyWithLogitsFunctor_, (targets, input, weights, output), FLOAT_TYPES);
 
+        NDArray::registerSpecialUse({output}, {targets, input, weights});
+    }
 
 }
 }
diff --git a/libnd4j/include/ops/declarable/helpers/cuda/lup.cu b/libnd4j/include/ops/declarable/helpers/cuda/lup.cu
index f11b56745..ec4fd2a97 100644
--- a/libnd4j/include/ops/declarable/helpers/cuda/lup.cu
+++ b/libnd4j/include/ops/declarable/helpers/cuda/lup.cu
@@ -31,34 +31,14 @@
 namespace nd4j {
 namespace ops {
 namespace helpers {
-//    template <typename T>
-//    static __device__ void swapRows_(T* matrix, Nd4jLong* shape, int theFirst, int theSecond, Nd4jLong N) {
-//        if (theFirst != theSecond) {
-//            auto start = threadIdx.x + blockIdx.x * blockDim.x;
-//            auto step = blockDim.x * gridDim.x;
-//            for (auto i = start; i < N; i += step) {
-//                Nd4jLong iCoord1[] = {theFirst, i};
-//                Nd4jLong iCoord2[] = {theSecond, i};
-//                auto iIndex1 = shape::getOffset(0, shape::shapeOf(shape), shape::stride(shape), iCoord1, 2);
-//                auto iIndex2 = shape::getOffset(0, shape::shapeOf(shape), shape::stride(shape), iCoord2, 2);
-//                //atomicExch(&matrix[iIndex1], matrix[iIndex2]);
-//                T e0 = matrix[iIndex1];
-//                T e1 = matrix[iIndex2];
-//                matrix[iIndex1] = e0;
-//                matrix[iIndex2] = e1;
-//            }
-//        }
-//    }
-//    BUILD_SINGLE_TEMPLATE(template void swapRows_, (NDArray* matrix, int theFirst, int theSecond), FLOAT_TYPES);
-//
-//    void swapRows(NDArray* matrix, int theFirst, int theSecond) {
-//        BUILD_SINGLE_SELECTOR(matrix->dataType(), swapRows_, (matrix, theFirst, theSecond), FLOAT_TYPES);
-//    }
+
+// ------------------------------------------------------------------------------------------------------------------ //
+//  invert the second diagonal for lower diagonal matrix
     template<typename T>
     static __global__ void
     invertKernelLow(void *invertedBuf, Nd4jLong *invertedShape, void *inputBuf, Nd4jLong *inputShape, Nd4jLong n) {
-        T *inverted = reinterpret_cast<T *>(invertedBuf);
-        T *input = reinterpret_cast<T *>(inputBuf);
+        T* inverted = reinterpret_cast<T *>(invertedBuf);
+        T* input = reinterpret_cast<T*>(inputBuf);
 
         auto start = threadIdx.x + blockIdx.x * blockDim.x;
         auto step = blockDim.x * gridDim.x;
@@ -67,15 +47,17 @@ namespace helpers {
             Nd4jLong pos[] = {i, i - 1};
             Nd4jLong posX[] = {i, i};
             Nd4jLong posY[] = {i - 1, i - 1};
-            auto xIndex = shape::getOffset(0, shape::shapeOf(inputShape), shape::stride(inputShape), pos, 2);
-            auto dxIndex = shape::getOffset(0, shape::shapeOf(inputShape), shape::stride(inputShape), posX, 2);
-            auto dyIndex = shape::getOffset(0, shape::shapeOf(inputShape), shape::stride(inputShape), posY, 2);
-            auto zIndex = shape::getOffset(0, shape::shapeOf(invertedShape), shape::stride(invertedShape), pos, 2);
+            auto xIndex = shape::getOffset(inputShape, pos);
+            auto dxIndex = shape::getOffset(inputShape, posX);
+            auto dyIndex = shape::getOffset(inputShape, posY);
+            auto zIndex = shape::getOffset(invertedShape, pos);
+            // invert lower triangular matrix
             inverted[zIndex] = -input[xIndex] / (input[dxIndex] * input[dyIndex]);
 //            math::atomics::nd4j_atomicAdd(&inverted[zIndex], - input[xIndex] * inverted[iIndex] / input[dIndex]);
         }
     }
-
+// ------------------------------------------------------------------------------------------------------------------ //
+// invert diagonal vals to upper diagonal matrix
     template<typename T>
     static __global__ void
     upvertKernel(void *invertedBuf, Nd4jLong *invertedShape, void *inputBuf, Nd4jLong *inputShape, Nd4jLong n) {
@@ -87,31 +69,25 @@ namespace helpers {
 
         for (int i = start; i < n; i += step) {
             Nd4jLong pos[] = {i, i};
-            auto xIndex = shape::getOffset(0, shape::shapeOf(inputShape), shape::stride(inputShape), pos, 2);
-            auto zIndex = shape::getOffset(0, shape::shapeOf(invertedShape), shape::stride(invertedShape), pos, 2);
+            auto xIndex = shape::getOffset(inputShape, pos);
+            auto zIndex = shape::getOffset(invertedShape, pos);
 //            math::atomics::nd4j_atomicDiv(&inverted[zIndex], input[xIndex]);
+            // invert diagonal elements
             inverted[zIndex] /= input[xIndex];
         }
     }
 
+// ------------------------------------------------------------------------------------------------------------------ //
+//  invert upper second diagonal
     template<typename T>
     static __global__ void
     upvertKernelUp(void *invertedBuf, Nd4jLong *invertedShape, void *inputBuf, Nd4jLong *inputShape, Nd4jLong n) {
 
         __shared__ T* inverted;
         __shared__ T* input;
-        __shared__ Nd4jLong* inputStride;
-        __shared__ Nd4jLong* invertedStride;
-        __shared__ Nd4jLong* invertedShapeOf;
-        __shared__ Nd4jLong* inputShapeOf;
         if (threadIdx.x == 0) {
             inverted = reinterpret_cast<T *>(invertedBuf);
             input = reinterpret_cast<T *>(inputBuf);
-            inputStride = shape::stride(inputShape);
-            invertedStride = shape::stride(invertedShape);
-            invertedShapeOf = shape::shapeOf(invertedShape);
-            inputShapeOf = shape::shapeOf(inputShape);
-
         }
         __syncthreads();
 
@@ -120,18 +96,17 @@ namespace helpers {
 
         for (int i = start; i < n - 1; i += step) {
             Nd4jLong pos[] = {i, i + 1};
-            //Nd4jLong posY[] = {i, i};
             Nd4jLong posX[] = {i + 1, i + 1};
-            auto xIndex = shape::getOffset(0, inputShapeOf, shape::stride(inputShape), pos, 2);
-//            auto yIndex = shape::getOffset(0, shape::shapeOf(inputShape), shape::stride(inputShape), posY, 2);
-//            auto yIndex = shape::getOffset(0, shape::shapeOf(inputShape), shape::stride(inputShape), pos, 2);
-            auto iIndex = shape::getOffset(0, invertedShapeOf, invertedStride, posX, 2);
-            auto zIndex = shape::getOffset(0, invertedShapeOf, invertedStride, pos, 2);
+            auto xIndex = shape::getOffset(inputShape, pos);
+            auto iIndex = shape::getOffset(invertedShape, posX);
+            auto zIndex = shape::getOffset(invertedShape, pos);
+            // invert upper matrix
             math::atomics::nd4j_atomicAdd(&inverted[zIndex], -input[xIndex] * inverted[iIndex]); // / input[yIndex]);
             //inputMatrix->t<T>(i, i + 1) * invertedMatrix->t<T>(i + 1, i + 1) / inputMatrix->t<T>(i, i)
         }
     }
 
+// ------------------------------------------------------------------------------------------------------------------ //
     template<typename T>
     static __global__ void
     invertLowKernel(void *invertedBuf, Nd4jLong *invertedShape, void *inputBuf, Nd4jLong *inputShape, Nd4jLong n) {
@@ -146,34 +121,27 @@ namespace helpers {
                     Nd4jLong posX[] = {i, k};
                     Nd4jLong posD[] = {i, i};
 
-                    auto xIndex = shape::getOffset(0, shape::shapeOf(inputShape), shape::stride(inputShape), posX, 2);
-                    auto yIndex = shape::getOffset(0, shape::shapeOf(invertedShape), shape::stride(invertedShape), posY,
-                                                   2);
-                    auto dIndex = shape::getOffset(0, shape::shapeOf(inputShape), shape::stride(inputShape), posD, 2);
-                    auto zIndex = shape::getOffset(0, shape::shapeOf(invertedShape), shape::stride(invertedShape), posZ,
-                                                   2);
+                    auto xIndex = shape::getOffset(inputShape, posX);
+                    auto yIndex = shape::getOffset(invertedShape, posY);
+                    auto dIndex = shape::getOffset(inputShape, posD);
+                    auto zIndex = shape::getOffset(invertedShape, posZ);
+                    // invert non-diagonal elements
                     math::atomics::nd4j_atomicAdd(&inverted[zIndex], -inverted[yIndex] * input[xIndex] / input[dIndex]);
                 }
         }
     }
 
+// ------------------------------------------------------------------------------------------------------------------ //
+// Invertion of upper triangular matrix non-diagonal elements when main and second diagonals already processed
     template<typename T>
     static __global__ void
     invertUpKernel(void *invertedBuf, Nd4jLong *invertedShape, void *inputBuf, Nd4jLong *inputShape, Nd4jLong n) {
         __shared__ T* inverted;
         __shared__ T* input;
-        __shared__ Nd4jLong* inputShapeOf;
-        __shared__ Nd4jLong* invertedShapeOf;
-        __shared__ Nd4jLong* invertedStrideOf;
-        __shared__ Nd4jLong* inputStrideOf;
 
         if (threadIdx.x == 0) {
             inverted = reinterpret_cast<T *>(invertedBuf);;
             input = reinterpret_cast<T *>(inputBuf);
-            inputShapeOf = shape::shapeOf(inputShape);
-            invertedShapeOf = shape::shapeOf(invertedShape);
-            inputStrideOf = shape::stride(inputShape);
-            invertedStrideOf = shape::stride(invertedShape);
         }
         __syncthreads();
 
@@ -183,18 +151,20 @@ namespace helpers {
                     Nd4jLong posZ[] = {i, j};
                     Nd4jLong posY[] = {k, j};
                     Nd4jLong posX[] = {i, k};
-//                    Nd4jLong posD[] = {i, i};
-
-                    auto xIndex = shape::getOffset(0, inputShapeOf, inputStrideOf, posX, 2);
-                    auto yIndex = shape::getOffset(0, invertedShapeOf, invertedStrideOf, posY, 2);
-                    //                  auto dIndex = shape::getOffset(0, shape::shapeOf(inputShape), shape::stride(inputShape), posD, 2);
-                    auto zIndex = shape::getOffset(0, invertedShapeOf, invertedStrideOf, posZ, 2);
-                    math::atomics::nd4j_atomicAdd(&inverted[zIndex], -inverted[yIndex] * input[xIndex]);// / input[dIndex]);
-//                    printf("(%d, %d) inverted[%lld] = %lf (-inverted[%lld] * input[%lld]\n", blockIdx.x, threadIdx.x, zIndex, inverted[zIndex], yIndex, xIndex);
+                    // inversion with Joardan Gauss transformation
+                    auto xIndex = shape::getOffset(inputShape, posX);
+                    auto yIndex = shape::getOffset(invertedShape, posY);
+                    auto zIndex = shape::getOffset(invertedShape, posZ);
+                    // invert upper non-diagonal elements
+                    math::atomics::nd4j_atomicAdd(&inverted[zIndex], -inverted[yIndex] * input[xIndex]);
                 }
         }
     }
 
+// ------------------------------------------------------------------------------------------------------------------ //
+// procedure to invert lower-triangular matrix.
+// In current case lower triangular matrix has main diagonal with general values
+//
     template<typename T>
     static void invertLowerMatrix_(LaunchContext *context, NDArray *inputMatrix, NDArray *invertedMatrix) {
         int n = inputMatrix->rows();
@@ -204,20 +174,26 @@ namespace helpers {
 
         auto stream = context->getCudaStream();
 
+        // invert lower matrix
         // invert main diagonal
         upvertKernel<T><<<1, n, 512, *stream>>>(invertedMatrix->specialBuffer(), invertedMatrix->specialShapeInfo(), inputMatrix->specialBuffer(), inputMatrix->specialShapeInfo(), n);
         // invert the second diagonal
         invertKernelLow<T><<<1, n, 512, *stream>>>(invertedMatrix->specialBuffer(), invertedMatrix->specialShapeInfo(), inputMatrix->specialBuffer(), inputMatrix->specialShapeInfo(), n);
-//        invertKernelLow<T><<<1, n, 128, *stream>>>(invertedMatrix->specialBuffer(), invertedMatrix->specialShapeInfo(), inputMatrix->specialBuffer(), inputMatrix->specialShapeInfo(), n);
+        // invert non-diagonal elements
         invertLowKernel<T><<<n, n, 512, *stream>>>(invertedMatrix->specialBuffer(), invertedMatrix->specialShapeInfo(), inputMatrix->specialBuffer(), inputMatrix->specialShapeInfo(), n);
     }
 
+// ------------------------------------------------------------------------------------------------------------------ //
+// caller for invert lower matrix routine
     void invertLowerMatrix(LaunchContext *context, NDArray *inputMatrix, NDArray *invertedMatrix) {
         NDArray::prepareSpecialUse({invertedMatrix}, {inputMatrix});
         BUILD_SINGLE_SELECTOR(inputMatrix->dataType(), invertLowerMatrix_, (context, inputMatrix, invertedMatrix), FLOAT_NATIVE);
         NDArray::registerSpecialUse({invertedMatrix}, {inputMatrix});
     }
 
+// ------------------------------------------------------------------------------------------------------------------ //
+// procedure to invert upper-triangular matrix.
+// In current case upper triangular matrix has main diagonal with all ones on it.
     template<typename T>
     static void invertUpperMatrix_(LaunchContext *context, NDArray* inputMatrix, NDArray* invertedMatrix) {
         int n = inputMatrix->rows();
@@ -227,342 +203,319 @@ namespace helpers {
             return;
         }
 
-        //upvertKernel<T><<<1, n, 128, *stream>>>(invertedMatrix->specialBuffer(), invertedMatrix->specialShapeInfo(), inputMatrix->specialBuffer(), inputMatrix->specialShapeInfo(), n);
+        // invert upper matrix
+        // invert the second diagonal
         upvertKernelUp<T><<<1, n, 512, *stream >>>(invertedMatrix->specialBuffer(), invertedMatrix->specialShapeInfo(),
                 inputMatrix->specialBuffer(), inputMatrix->specialShapeInfo(), n);
-        invertedMatrix->tickWriteDevice();
-        invertedMatrix->printIndexedBuffer("Step1 UP inversion");
+
+        // invert other elements
         invertUpKernel<T><<<n, n, 512, *stream >>>(invertedMatrix->specialBuffer(), invertedMatrix->specialShapeInfo(),inputMatrix->specialBuffer(), inputMatrix->specialShapeInfo(), n);
     }
 
+// ------------------------------------------------------------------------------------------------------------------ //
+//  invertion of upper triangular matrix - runner routine
     void invertUpperMatrix(LaunchContext *context, NDArray *inputMatrix, NDArray *invertedMatrix) {
         NDArray::prepareSpecialUse({invertedMatrix}, {inputMatrix});
         BUILD_SINGLE_SELECTOR(invertedMatrix->dataType(), invertUpperMatrix_, (context, inputMatrix, invertedMatrix), FLOAT_NATIVE);
         NDArray::prepareSpecialUse({invertedMatrix}, {inputMatrix});
     }
 
-//    template <typename T>
-//    static __global__ void lupKernel(T* compound, Nd4jLong* compoundShape, T* permutation, Nd4jLong* permutationShape, Nd4jLong rowNum) {
-//        int swapCount = 0;
-//        for(int i = blockIdx.x; i < rowNum; i += gridDim.x ) {
-//            auto pivotValue = T(0.0);
-//            auto pivot = -1;
-//
-//            for(int rowCounter = i; rowCounter < rowNum; rowCounter++ ) {
-//                Nd4jLong rowCoord[] = {rowCounter, i};
-//                auto rowPos = shape::getOffset(0, shape::shapeOf(compoundShape), shape::stride(compoundShape), rowCoord, 2);
-//                if(nd4j::math::nd4j_abs(compound[rowPos]) > pivotValue ) {
-//                    pivotValue = nd4j::math::nd4j_abs(compound[rowPos]);
-//                    pivot = rowCounter;
-//                }
-//            }
-//
-//            if( pivotValue != T(0.0) ) {
-//                swapRows_<T>(compound, compoundShape, pivot, i, rowNum);
-//                swapRows_<T>(permutation, permutationShape, pivot, i, rowNum);
-//                if (pivot != i)
-//                    swapCount++;
-//
-//                for( int j = i + 1; j < rowNum; j++ ) {
-//                    Nd4jLong posJIbuf[] = {j, i};
-//                    Nd4jLong posIIbuf[] = {i, i};
-//                    auto posJI = shape::getOffset(0, shape::shapeOf(compoundShape), shape::stride(compoundShape), posJIbuf, 2);
-//                    auto posII = shape::getOffset(0, shape::shapeOf(compoundShape), shape::stride(compoundShape), posIIbuf, 2);
-//
-//                    compound[posJI] /= compound[posII];
-//                    for( int k = i + 1; k < rowNum; k++ ) {
-//                        Nd4jLong posJKbuf[] = {j, k};
-//                        Nd4jLong posIKbuf[] = {i, k};
-//                        auto posJK = shape::getOffset(0, shape::shapeOf(compoundShape), shape::stride(compoundShape), posJKbuf, 2);
-//                        auto posIK = shape::getOffset(0, shape::shapeOf(compoundShape), shape::stride(compoundShape), posIKbuf, 2);
-//                        T arg = compound[posJI] * compound[posIK];
-//                        compound[posJK] -= arg;
-//                    }
-//                }
-//            }
-//        }
-//    }
+// ------------------------------------------------------------------------------------------------------------------ //
+    // determinant kernel - accumulation product of all values on the main diagonal
+    template<typename T>
+    static __global__ void determinantKernel(T *compound, T *result, Nd4jLong len) {
+        auto start = blockIdx.x * blockDim.x + threadIdx.x;
+        auto step = blockDim.x * gridDim.x;
+        for (auto i = start; i < len; i += step) {
+            auto pos = i * len + i; //shape::getOffset(0, shape::shapeOf(shape), shape::stride(shape), di, 2);
+            // multiply all diagonal elements
+            math::atomics::nd4j_atomicMul(&result[0], compound[pos]);
+        }
+    }
 
-//    template <typename T, typename F>
-        template<typename T>
-        static __global__ void determinantKernel(T *compound, T *result, Nd4jLong len) {
-            //F tempRes = result[0];
+// ------------------------------------------------------------------------------------------------------------------ //
+    // determinant logarithm - accumulation sum of all logarithm values on the main diagonal. All in logarithic values
+    // should be positive
+    template<typename T>
+    static __global__ void determinantLogKernel(T *compound, T *result, Nd4jLong len) {
+        auto start = blockIdx.x * blockDim.x + threadIdx.x;
+        auto step = blockDim.x * gridDim.x;
+        for (auto i = start; i < len; i += step) {
+            auto pos = i * len + i; //shape::getOffset(0, shape::shapeOf(shape), shape::stride(shape), di, 2);
+            // sum logs of all diagonal elements
+            math::atomics::nd4j_atomicAdd(result, math::nd4j_log<T,T>(math::nd4j_abs(compound[pos])));
+        }
+    }
 
-            auto start = blockIdx.x * blockDim.x + threadIdx.x;
-            auto step = blockDim.x * gridDim.x;
-            for (auto i = start; i < len; i += step) {
-                auto pos = i * len + i; //shape::getOffset(0, shape::shapeOf(shape), shape::stride(shape), di, 2);
-                math::atomics::nd4j_atomicMul(&result[0], compound[pos]);
-            }
+// ------------------------------------------------------------------------------------------------------------------ //
+    // kernel to copy matrix with given shape to compound tensor with given pos
+    // output - a N-D tensor buffer with rank not less than 2, input - 2D square n x n matrix with n = rowLen
+    template<typename T, typename F>
+    static __global__ void
+    fillMatrix(void *output, Nd4jLong *outShape, void *input, Nd4jLong *inputShape, Nd4jLong pos, Nd4jLong rowLen) {
+        __shared__ F *matrix;
+        __shared__ T *inputBuf;
+        __shared__ Nd4jLong inputLen;
+        __shared__ Nd4jLong n2;
+
+        if (threadIdx.x == 0) {
+            matrix = reinterpret_cast<F*>(output);
+            inputBuf = reinterpret_cast<T*>(input);
+            inputLen = shape::length(inputShape);
+            n2 = rowLen * rowLen;
+        }
+        __syncthreads();
+
+        auto start = blockIdx.x * blockDim.x + threadIdx.x;
+        auto step = blockDim.x * gridDim.x;
+
+        for (int k = pos + start, j = start; j < n2; k += step, j += step) {
+            auto xIndex = shape::getIndexOffset(k, inputShape);
+            matrix[j] = (F) inputBuf[xIndex];
+        }
+    }
+
+// ------------------------------------------------------------------------------------------------------------------ //
+// same as above, but without type conversion
+    template<typename T>
+    static __global__ void
+    returnMatrix(void *output, Nd4jLong *outputShape, void *input, Nd4jLong *inputShape, Nd4jLong pos, Nd4jLong rowLen) {
+        __shared__ T* matrix;
+        __shared__ T* outputBuf;
+        __shared__ Nd4jLong outputLen;
+        __shared__ Nd4jLong n2;
+
+        if (threadIdx.x == 0) {
+            matrix = reinterpret_cast<T *>(input);
+            outputBuf = reinterpret_cast<T *>(output);
+            outputLen = shape::length(inputShape);
+            n2 = rowLen * rowLen;
+        }
+        __syncthreads();
+        auto start = blockIdx.x * blockDim.x + threadIdx.x;
+        auto step = blockDim.x * gridDim.x;
+
+        for (int k = pos + start, j = start; j < n2; k += step, j += step) {
+            auto zIndex = shape::getIndexOffset(k, outputShape);
+            outputBuf[zIndex] = matrix[j];
+        }
+    }
+
+// ------------------------------------------------------------------------------------------------------------------ //
+    // fill up permutaion matrix kernel. Permutation matrix filled with zeros and ones
+    template<typename F>
+    static __global__ void fillUpPermutation(void *output, Nd4jLong *shape, int *source, int rowNum) {
+        F *permutation = reinterpret_cast<F *>(output);
+
+        auto start = blockIdx.x * blockDim.x + threadIdx.x;
+        auto step = blockDim.x * gridDim.x;
+        for (auto i = start; i < rowNum; i += step) {
+            int val = source[i] - 1;
+            Nd4jLong posF[] = {i, val};
+            auto pos = shape::getOffset(shape, posF);
+            permutation[pos] = F(1.f);
+        }
+    }
+
+// ------------------------------------------------------------------------------------------------------------------ //
+    // LUP decomposition runner - using CUBLAS SOLVER
+    // if permutation is given, then using LUP decomposition, LU decomposition otherwise
+    // L - lower triangular, U - upper triangular, P - permutation matricies
+    // PA = LU
+    //
+    // input - A matrix nxn
+    // compound - C matrix L + U - I, or main diagonal and lower - L matrix, from the 2nd diagonal - U matrix
+    template<typename T>
+    static void lup_(LaunchContext *context, NDArray *input, NDArray *compound, NDArray *permutation) {
+        auto stream = context->getCudaStream();
+        auto n = input->rows();
+        cusolverDnHandle_t cusolverH = nullptr;
+        // create solver handle
+        cusolverStatus_t status = cusolverDnCreate(&cusolverH);
+        if (CUSOLVER_STATUS_SUCCESS != status) {
+            throw cuda_exception::build("Cannot create cuSolver handle", status);
+        }
+        // set solver stream
+        status = cusolverDnSetStream(cusolverH, *stream);
+        if (CUSOLVER_STATUS_SUCCESS != status) {
+            throw cuda_exception::build("Cannot set up stream for cuda solver", status);
+        }
+        int lwork = 0;
+        int *d_info = nullptr;
+        // allocate memory for permutation vector
+        auto err = cudaMalloc((void **) &d_info, sizeof(int));
+        if (err) {
+            throw cuda_exception::build("helpers::lup_: Cannot allocate memory for solver info buffer", err);
         }
 
-        template<typename T>
-        static __global__ void determinantLogKernel(T *compound, T *result, Nd4jLong len) {
-//            F tempRes =  (F)result[0];
+        DataType dtype = input->dataType();
+        switch (dtype) { // there are two implementations with cublas for LUP decomposition - double and float
 
-            auto start = blockIdx.x * blockDim.x + threadIdx.x;
-            auto step = blockDim.x * gridDim.x;
-            for (auto i = start; i < len; i += step) {
-                auto pos = i * len + i; //shape::getOffset(0, shape::shapeOf(shape), shape::stride(shape), di, 2);
-                math::atomics::nd4j_atomicAdd(result, math::nd4j_log<T,T>(math::nd4j_abs(compound[pos])));
-            }
-//            __syncthreads();
-//
-//            if (threadIdx.x == 0) {
-//                result[0] = (T)math::nd4j_log<F,F>(math::nd4j_abs(tempRes));
-//            }
-        }
+            case DataType::DOUBLE: {
+                double *d_work = nullptr;
+                // compute internal buffer size
+                double *matrix = reinterpret_cast<double *>(input->specialBuffer());
+                status = cusolverDnDgetrf_bufferSize(
+                        cusolverH,
+                        n,
+                        n,
+                        matrix,
+                        n,
+                        &lwork);
+                if (CUSOLVER_STATUS_SUCCESS != status) {
+                    throw cuda_exception::build("helpers::lup_: Cannot create cuSolver handle", status);
+                }
 
-        template<typename T, typename F>
-        static __global__ void
-        fillMatrix(void *output, Nd4jLong *outShape, void *input, Nd4jLong *inputShape, Nd4jLong pos, Nd4jLong rowLen) {
-            __shared__
-            F *matrix;
-            __shared__
-            T *inputBuf;
-            __shared__
-            Nd4jLong inputLen;
-            __shared__
-            Nd4jLong n2;
+                err = cudaMalloc((void **) &d_work, sizeof(float) * lwork);
+                if (err) {
+                    throw cuda_exception::build("helpers::lup_: Cannot allocate memory for solver data buffer",
+                                                err);
+                }
 
-            if (threadIdx.x == 0) {
-                matrix = reinterpret_cast<F *>(output);
-                inputBuf = reinterpret_cast<T *>(input);
-                inputLen = shape::length(inputShape);
-                n2 = rowLen * rowLen;
-            }
-            __syncthreads();
-            auto start = blockIdx.x * blockDim.x + threadIdx.x;
-            auto step = blockDim.x * gridDim.x;
-
-            for (int k = pos + start, j = start; j < n2; k += step, j += step) {
-                auto xIndex = shape::getIndexOffset(k, inputShape, inputLen);
-                matrix[j] = (F) inputBuf[xIndex];
-            }
-        }
-
-        template<typename T>
-        static __global__ void
-        returnMatrix(void *output, Nd4jLong *outputShape, void *input, Nd4jLong *inputShape, Nd4jLong pos,
-                     Nd4jLong rowLen) {
-            __shared__            T *matrix;
-            __shared__            T *outputBuf;
-            __shared__            Nd4jLong outputLen;
-            __shared__            Nd4jLong n2;
-
-            if (threadIdx.x == 0) {
-                matrix = reinterpret_cast<T *>(input);
-                outputBuf = reinterpret_cast<T *>(output);
-                outputLen = shape::length(inputShape);
-                n2 = rowLen * rowLen;
-            }
-            __syncthreads();
-            auto start = blockIdx.x * blockDim.x + threadIdx.x;
-            auto step = blockDim.x * gridDim.x;
-
-            for (int k = pos + start, j = start; j < n2; k += step, j += step) {
-                auto zIndex = shape::getIndexOffset(k, outputShape, outputLen);
-                outputBuf[zIndex] = (T) matrix[j];
-            }
-        }
-
-        template<typename F>
-        static __global__ void fillUpPermutation(void *output, Nd4jLong *shape, int *source, int rowNum) {
-            F *permutation = reinterpret_cast<F *>(output);
-
-            auto start = blockIdx.x * blockDim.x + threadIdx.x;
-            auto step = blockDim.x * gridDim.x;
-            for (auto i = start; i < rowNum; i += step) {
-                int val = source[i] - 1;
-                Nd4jLong posF[] = {i, val};
-                auto pos = shape::getOffset(0, shape::shapeOf(shape), shape::stride(shape), posF, 2);
-                permutation[pos] = F(1.f);
-            }
-        }
-
-        template<typename T>
-        static void lup_(LaunchContext *context, NDArray *input, NDArray *compound, NDArray *permutation) {
-            auto stream = context->getCudaStream();
-            auto n = input->rows();
-            cusolverDnHandle_t cusolverH = nullptr;
-            cusolverStatus_t status = cusolverDnCreate(&cusolverH);
-            if (CUSOLVER_STATUS_SUCCESS != status) {
-                throw cuda_exception::build("Cannot create cuSolver handle", status);
-            }
-            status = cusolverDnSetStream(cusolverH, *stream);
-            if (CUSOLVER_STATUS_SUCCESS != status) {
-                throw cuda_exception::build("Cannot set up stream for cuda solver", status);
-            }
-            int lwork = 0;
-            int *d_info = nullptr;
-
-            auto err = cudaMalloc((void **) &d_info, sizeof(int));
-            if (err) {
-                throw cuda_exception::build("helpers::lup_: Cannot allocate memory for solver info buffer", err);
-            }
-
-            DataType dtype = input->dataType();
-            switch (dtype) {
-
-                case DataType::DOUBLE: {
-                    double *d_work = nullptr;
-                    err = cudaMalloc((void **) &d_work, sizeof(float) * lwork);
-                    if (err) {
-                        throw cuda_exception::build("helpers::lup_: Cannot allocate memory for solver data buffer",
-                                                    err);
-                    }
-                    double *matrix = reinterpret_cast<double *>(input->specialBuffer());
-                    status = cusolverDnDgetrf_bufferSize(
+                if (permutation == nullptr)
+                    status = cusolverDnDgetrf(
                             cusolverH,
                             n,
                             n,
                             matrix,
                             n,
-                            &lwork);
-                    if (CUSOLVER_STATUS_SUCCESS != status) {
-                        throw cuda_exception::build("helpers::lup_: Cannot create cuSolver handle", status);
-                    }
-                    if (permutation == nullptr)
-                        status = cusolverDnDgetrf(
-                                cusolverH,
-                                n,
-                                n,
-                                matrix,
-                                n,
-                                d_work,
-                                nullptr,
-                                d_info);
-                    else {
-                        NDArray permutVector('c', {n}, nd4j::DataType::INT32, context);
-                        int *permutationBuf = reinterpret_cast<int *>(permutVector.specialBuffer());
-                        status = cusolverDnDgetrf(
-                                cusolverH,
-                                n,
-                                n,
-                                matrix,
-                                n,
-                                d_work,
-                                permutationBuf,
-                                d_info);
-                        fillUpPermutation<double> << < n, n, 1024, *stream >> >
-                                                                  (permutation->specialBuffer(), permutation->specialShapeInfo(), permutationBuf, n);
-                        permutation->tickWriteDevice();
-                    }
-                    err = cudaFree(d_work);
-                    if (err) {
-                        throw cuda_exception::build("helpers::lup_: Cannot deallocate memory for solver data buffer",
-                                                    err);
-                    }
-                }
-                    break;
-                case DataType::FLOAT32: {
-                    float *matrix = reinterpret_cast<float *>(input->specialBuffer());
-                    float *d_work = nullptr;
-                    err = cudaMalloc((void **) &d_work, sizeof(float) * lwork);
-                    if (err) {
-                        throw cuda_exception::build("helpers::lup_: Cannot allocate memory for solver data buffer",
-                                                    err);
-                    }
-
-                    status = cusolverDnSgetrf_bufferSize(
+                            d_work,
+                            nullptr,
+                            d_info);
+                else {
+                    NDArray permutVector('c', {n}, nd4j::DataType::INT32, context);
+                    int *permutationBuf = reinterpret_cast<int *>(permutVector.specialBuffer());
+                    status = cusolverDnDgetrf(
                             cusolverH,
                             n,
                             n,
                             matrix,
                             n,
-                            &lwork);
-                    if (CUSOLVER_STATUS_SUCCESS != status) {
-                        throw cuda_exception::build("helpers::lup_: Cannot create cuSolver handle", status);
-                    }
-
-                    if (permutation == nullptr)
-                        status = cusolverDnSgetrf(
-                                cusolverH,
-                                n,
-                                n,
-                                matrix,
-                                n,
-                                d_work,
-                                nullptr,
-                                d_info);
-                    else {
-                        NDArray permutVector('c', {n}, nd4j::DataType::INT32, context);
-                        int *permutationBuf = reinterpret_cast<int *>(permutVector.specialBuffer());
-                        status = cusolverDnSgetrf(
-                                cusolverH,
-                                n,
-                                n,
-                                matrix,
-                                n,
-                                d_work,
-                                permutationBuf,
-                                d_info);
-                        fillUpPermutation<T> <<< n, n, 128, *stream >> >
-                                                             (permutation->specialBuffer(), permutation->specialShapeInfo(), permutationBuf, n);
-                        permutation->tickWriteDevice();
-                    }
-                    err = cudaFree(d_work);
-                    if (err) {
-                        throw cuda_exception::build("helpers::lup_: Cannot deallocate memory for solver data buffer",
-                                                    err);
-                    }
-
+                            d_work,
+                            permutationBuf,
+                            d_info);
+                    fillUpPermutation<double> << < n, n, 1024, *stream >> >
+                                                              (permutation->specialBuffer(), permutation->specialShapeInfo(), permutationBuf, n);
+                    permutation->tickWriteDevice();
+                }
+                err = cudaFree(d_work);
+                if (err) {
+                    throw cuda_exception::build("helpers::lup_: Cannot deallocate memory for solver data buffer",
+                                                err);
                 }
             }
-            if (CUSOLVER_STATUS_SUCCESS != status) {
-                throw cuda_exception::build("helpers::lup_: Cannot make LU decomposition", status);
+                break;
+            case DataType::FLOAT32: {
+                float *matrix = reinterpret_cast<float*>(input->specialBuffer());
+                float *d_work = nullptr;
+
+                status = cusolverDnSgetrf_bufferSize(
+                        cusolverH,
+                        n,
+                        n,
+                        matrix,
+                        n,
+                        &lwork);
+                if (CUSOLVER_STATUS_SUCCESS != status) {
+                    throw cuda_exception::build("helpers::lup_: Cannot create cuSolver handle", status);
+                }
+
+                err = cudaMalloc((void **) &d_work, sizeof(float) * lwork);
+                if (err) {
+                    throw cuda_exception::build("helpers::lup_: Cannot allocate memory for solver data buffer",
+                                                err);
+                }
+
+                if (permutation == nullptr)
+                    status = cusolverDnSgetrf(
+                            cusolverH,
+                            n,
+                            n,
+                            matrix,
+                            n,
+                            d_work,
+                            nullptr,
+                            d_info);
+                else {
+                    NDArray permutVector('c', {n}, nd4j::DataType::INT32, context);
+                    int *permutationBuf = reinterpret_cast<int *>(permutVector.specialBuffer());
+                    status = cusolverDnSgetrf(
+                            cusolverH,
+                            n,
+                            n,
+                            matrix,
+                            n,
+                            d_work,
+                            permutationBuf,
+                            d_info);
+                    fillUpPermutation<T> <<< n, n, 128, *stream >> >
+                                                         (permutation->specialBuffer(), permutation->specialShapeInfo(), permutationBuf, n);
+                    permutation->tickWriteDevice();
+                }
+                err = cudaFree(d_work);
+                if (err) {
+                    throw cuda_exception::build("helpers::lup_: Cannot deallocate memory for solver data buffer",
+                                                err);
+                }
+
             }
-            err = cudaFree(d_info);
-            if (err) {
-                throw cuda_exception::build("helpers::lup_: Cannot deallocate memory for solver info buffer", err);
-            }
-            cusolverDnDestroy(cusolverH);
+        }
+        if (CUSOLVER_STATUS_SUCCESS != status) {
+            throw cuda_exception::build("helpers::lup_: Cannot make LU decomposition", status);
+        }
+        err = cudaFree(d_info);
+        if (err) {
+            throw cuda_exception::build("helpers::lup_: Cannot deallocate memory for solver info buffer", err);
+        }
+        cusolverDnDestroy(cusolverH);
 //        NDArray::registerSpecialUse({input}, {input});
-            input->tickWriteDevice();
-        }
+        input->tickWriteDevice();
+    }
+// ------------------------------------------------------------------------------------------------------------------ //
 
-        BUILD_SINGLE_TEMPLATE(template void lup_,(LaunchContext * context, NDArray * input, NDArray * output, NDArray * permutation), FLOAT_NATIVE);
+    BUILD_SINGLE_TEMPLATE(template void lup_,(LaunchContext * context, NDArray * input, NDArray * output, NDArray * permutation), FLOAT_NATIVE);
 
-        template<typename T>
-        static int determinant_(nd4j::LaunchContext *context, NDArray *input, NDArray *output) {
-            Nd4jLong n = input->sizeAt(-1);
-            Nd4jLong n2 = n * n;
-            std::vector<int> dims();
-            auto packX = ConstantTadHelper::getInstance()->tadForDimensions(input->getShapeInfo(), {input->rankOf() - 2, input->rankOf() - 1});
-            //auto packZ = ConstantTadHelper::getInstance()->tadForDimensions(output->shapeInfo(), {output->rankOf() - 1});
+// ------------------------------------------------------------------------------------------------------------------ //
+    template<typename T>
+    static int determinant_(nd4j::LaunchContext *context, NDArray *input, NDArray *output) {
+        Nd4jLong n = input->sizeAt(-1);
+        Nd4jLong n2 = n * n;
+        std::vector<int> dims();
+        auto packX = ConstantTadHelper::getInstance()->tadForDimensions(input->getShapeInfo(), {input->rankOf() - 2, input->rankOf() - 1});
+        //auto packZ = ConstantTadHelper::getInstance()->tadForDimensions(output->shapeInfo(), {output->rankOf() - 1});
 //        DataType dtype = input->dataType();
 //        if (dtype != DataType::DOUBLE)
 //            dtype = DataType::FLOAT32;
-            auto matrix = NDArrayFactory::create(input->ordering(), {n, n}, DataTypeUtils::fromT<T>(), context); //, block.getWorkspace());
-            auto det = NDArrayFactory::create<T>(1);
-            auto stream = context->getCudaStream();
-            NDArray::prepareSpecialUse({output}, {input});
-            dim3 launchDims(256, 256, 1024);
-            output->assign(1.f);
-            for (int e = 0; e < output->lengthOf(); e++) {
-                Nd4jLong pos = e * n2;
+        auto matrix = NDArrayFactory::create(input->ordering(), {n, n}, DataTypeUtils::fromT<T>(), context); //, block.getWorkspace());
+        auto det = NDArrayFactory::create<T>(1);
+        auto stream = context->getCudaStream();
+        NDArray::prepareSpecialUse({output}, {input});
+        dim3 launchDims(256, 256, 1024);
+        output->assign(1.f);
+        for (int e = 0; e < output->lengthOf(); e++) {
+            Nd4jLong pos = e * n2;
 //            if (matrix.dataType() == input->dataType())
-                fillMatrix<T, T><<<launchDims.x, launchDims.y, launchDims.z, *stream>>>(matrix.specialBuffer(), matrix.specialShapeInfo(), input->specialBuffer(), input->specialShapeInfo(), pos, n);
+            fillMatrix<T, T><<<launchDims.x, launchDims.y, launchDims.z, *stream>>>(matrix.specialBuffer(), matrix.specialShapeInfo(), input->specialBuffer(), input->specialShapeInfo(), pos, n);
 //            else
 //                fillMatrix<T, float><<<launchDims.x, launchDims.y, launchDims.z, *stream>>>(matrix.specialBuffer(), matrix.specialShapeInfo(), input->specialBuffer(), input->specialShapeInfo(), pos, n);
 
 //            if (matrix.dataType() == input->dataType())
-                lup_<T>(context, &matrix, nullptr, nullptr);
+            lup_<T>(context, &matrix, nullptr, nullptr);
 //            else
 //                lup_<float>(context, &matrix, nullptr, nullptr);
-                auto offset = shape::getIndexOffset(e, output->shapeInfo(), output->lengthOf());
-                auto inputBuf = reinterpret_cast<T *>(matrix.specialBuffer());
-                auto outputBuf = reinterpret_cast<T *>(output->specialBuffer()) + offset;
+            auto offset = shape::getIndexOffset(e, output->shapeInfo());
+            auto inputBuf = reinterpret_cast<T *>(matrix.specialBuffer());
+            auto outputBuf = reinterpret_cast<T *>(output->specialBuffer()) + offset;
 //            if (matrix.dataType() == input->dataType())
-                determinantKernel<T> << < launchDims.x, launchDims.y, launchDims.z, *stream >> >
-                                                                                    (inputBuf, outputBuf, n);
+            determinantKernel<T> << < launchDims.x, launchDims.y, launchDims.z, *stream >> >
+                                                                                (inputBuf, outputBuf, n);
 //            else
 //                determinantKernel<T, float><<<launchDims.x, launchDims.y, launchDims.z, *stream >>> (inputBuf, outputBuf, n);
-            }
-            NDArray::registerSpecialUse({output}, {input});
-
-            return Status::OK();
         }
+        NDArray::registerSpecialUse({output}, {input});
+
+        return Status::OK();
+    }
 
         int determinant(nd4j::LaunchContext *context, NDArray *input, NDArray *output) {
             NDArray::prepareSpecialUse({output}, {input});
@@ -598,7 +551,7 @@ namespace helpers {
                 lup_<T>(context, &matrix, nullptr, nullptr);
 //            else
 //                lup_<float>(context, &matrix, nullptr, nullptr);
-                auto offset = shape::getIndexOffset(e, output->shapeInfo(), output->lengthOf());
+                auto offset = shape::getIndexOffset(e, output->shapeInfo());
                 auto inputBuf = reinterpret_cast<T *>(matrix.specialBuffer());
                 auto outputBuf = reinterpret_cast<T *>(output->specialBuffer()) + offset;
 //            if (matrix.dataType() == input->dataType())
@@ -624,34 +577,11 @@ namespace helpers {
         fillLowerUpperKernel(void *lowerBuf, Nd4jLong *lowerShape, void *upperBuf, Nd4jLong *upperShape,
                              void *matrixBuf, Nd4jLong *matrixShape, Nd4jLong n) {
 
-            __shared__
-            Nd4jLong *xShapeOf;
-            __shared__
-            Nd4jLong *yShapeOf;
-            __shared__
-            Nd4jLong *zShapeOf;
-            __shared__
-            Nd4jLong *xStrideOf;
-            __shared__
-            Nd4jLong *yStrideOf;
-            __shared__
-            Nd4jLong *zStrideOf;
-            __shared__
-            T *lowerMatrix;
-            __shared__
-            T *upperMatrix;
-            __shared__
-            T *matrix;
+            __shared__ T *lowerMatrix;
+            __shared__ T *upperMatrix;
+            __shared__ T *matrix;
 
             if (threadIdx.x == 0) {
-                xShapeOf = shape::shapeOf(lowerShape);
-                xStrideOf = shape::stride(lowerShape);
-
-                yShapeOf = shape::shapeOf(upperShape);
-                yStrideOf = shape::stride(upperShape);
-
-                zShapeOf = shape::shapeOf(matrixShape);
-                zStrideOf = shape::stride(matrixShape);
                 lowerMatrix = reinterpret_cast<T *>(lowerBuf);
                 upperMatrix = reinterpret_cast<T *>(upperBuf);
                 matrix = reinterpret_cast<T *>(matrixBuf);
@@ -662,10 +592,10 @@ namespace helpers {
                 for (int j = threadIdx.x; j < n; j += blockDim.x) {
                     Nd4jLong posX[] = {k, j};
                     Nd4jLong posD[] = {j, j};
-                    auto xPos = shape::getOffset(0, xShapeOf, xStrideOf, posX, 2);
-                    auto yPos = shape::getOffset(0, yShapeOf, yStrideOf, posX, 2);
-                    auto iPos = shape::getOffset(0, zShapeOf, zStrideOf, posX, 2);
-                    auto dPos = shape::getOffset(0, zShapeOf, zStrideOf, posD, 2);
+                    auto xPos = shape::getOffset(lowerShape, posX);
+                    auto yPos = shape::getOffset(upperShape, posX);
+                    auto iPos = shape::getOffset(matrixShape, posX);
+                    auto dPos = shape::getOffset(matrixShape, posD);
                     if (k >= j)
                         lowerMatrix[xPos] = matrix[iPos];//(k, j);
                     else
@@ -878,18 +808,14 @@ namespace helpers {
             T *output = outputBuf;
             T *input = inputBuf;
 
-            Nd4jLong *shapeOf = shape::shapeOf(tadShape);
-            Nd4jLong *strideOf = shape::stride(tadShape);
-
             for (auto i = blockIdx.x; i < batchNum; i += gridDim.x) {
                 T *current = input + tadOffsets[i];
 
-                auto zIndex = shape::getIndexOffset(i, outputShape, batchNum);
+                auto zIndex = shape::getIndexOffset(i, outputShape);
                 for (auto e = threadIdx.x; e < n; e += blockDim.x) {
                     Nd4jLong diag[] = {e, e};
-                    auto xIndex = shape::getOffset(0, shapeOf, strideOf, diag, 2);
-                    math::atomics::nd4j_atomicAdd(&output[zIndex],
-                                                  math::nd4j_log<T, T>(current[xIndex] * current[xIndex]));
+                    auto xIndex = shape::getOffset(tadShape, diag);
+                    math::atomics::nd4j_atomicAdd(&output[zIndex],math::nd4j_log<T, T>(current[xIndex] * current[xIndex]));
                 }
             }
         }
diff --git a/libnd4j/include/ops/declarable/helpers/cuda/matrixSetDiag.cu b/libnd4j/include/ops/declarable/helpers/cuda/matrixSetDiag.cu
index 01baaffb4..a3c754cf5 100644
--- a/libnd4j/include/ops/declarable/helpers/cuda/matrixSetDiag.cu
+++ b/libnd4j/include/ops/declarable/helpers/cuda/matrixSetDiag.cu
@@ -61,14 +61,14 @@ __global__ static void matrixSetDiagCuda(const void* vx, const Nd4jLong* xShapeI
 
     for (Nd4jLong i = tid; i < xLen; i += gridDim.x * blockDim.x) {
 
-        shape::index2coords(xRank, xShapeInfo + 1, i, xLen, coords);
+        shape::index2coords(i, xShapeInfo, coords);
 
-        const auto xOffset = shape::getOffset(0, xShapeInfo + 1, xShapeInfo + xRank + 1, coords, xRank);
-        const auto zOffset = areSameOffsets ? xOffset : shape::getOffset(0, zShapeInfo + 1, zShapeInfo + xRank + 1, coords, xRank);
+        const auto xOffset = shape::getOffset(xShapeInfo, coords);
+        const auto zOffset = areSameOffsets ? xOffset : shape::getOffset(zShapeInfo, coords);
 
         // condition to be on diagonal of innermost matrix
         if(coords[xRank - 2] == coords[xRank - 1])
-            z[zOffset] = y[shape::getOffset(0, yShapeInfo + 1, yShapeInfo + xRank, coords, xRank - 1)];
+            z[zOffset] = y[shape::getOffset(yShapeInfo, coords)];
         else
             z[zOffset] = zeroPad ? static_cast<T>(0) : x[xOffset];
     }
diff --git a/libnd4j/include/ops/declarable/helpers/cuda/matrix_band.cu b/libnd4j/include/ops/declarable/helpers/cuda/matrix_band.cu
index 41b71f5d7..3b7ea9551 100644
--- a/libnd4j/include/ops/declarable/helpers/cuda/matrix_band.cu
+++ b/libnd4j/include/ops/declarable/helpers/cuda/matrix_band.cu
@@ -26,7 +26,22 @@
 namespace nd4j {
 namespace ops {
 namespace helpers {
-
+////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
+// matrix band kernel
+//
+// inputBuffer - buffer of input tensor
+// inputShape - shape of input tensor
+// outputBuffer - buffer of output tensor
+// outputShape - shape of output tensor
+// lowerBand - lower band of matrix
+// upperBand - upper band of matrix
+// tadOnlyInputShapeInfo - TAD shape for input
+// tadInputOffsets - TAD offsets for input
+// tadOnlyOutputShapeInfo - TAD output shape
+// tadOutputOffsets - TAD output offsets
+// numTads - number of subarrays
+// inputLength - input subarray length
+//
     template <typename T>
     static __global__ void matrixBandKernel(void* inputBuffer, Nd4jLong* inputShape,
             void* outputBuffer, Nd4jLong* outputShape, Nd4jLong lowerBand, Nd4jLong upperBand, Nd4jLong* tadOnlyInputShapeInfo,  Nd4jLong* tadInputOffsets,
@@ -40,11 +55,9 @@ namespace helpers {
             for (Nd4jLong i = blockIdx.y; i < rows; i += gridDim.y) {
                 for (Nd4jLong j = threadIdx.x; j < cols; j += totalThreads) {
                     Nd4jLong coords[2] = {i, j};
-                    Nd4jLong tadOffsetOut = shape::getOffset(0, shape::shapeOf(tadOnlyOutputShapeInfo),
-                                                             shape::stride(tadOnlyOutputShapeInfo), coords, 2);
-                    Nd4jLong tadOffsetIn = shape::getOffset(0, shape::shapeOf(tadOnlyInputShapeInfo),
-                                                            shape::stride(tadOnlyInputShapeInfo), coords, 2);
-                    //shape::getIndexOffset(j, tadOnlyOutputShapeInfo, inputLength)
+                    Nd4jLong tadOffsetOut = shape::getOffset(tadOnlyOutputShapeInfo, coords);
+                    Nd4jLong tadOffsetIn = shape::getOffset(tadOnlyInputShapeInfo, coords);
+
                     if (i >= j) { // check lower diagonals
                         if (lowerBand > 0) {
                             if ((i - j) > lowerBand)
@@ -61,16 +74,14 @@ namespace helpers {
                                 *(reinterpret_cast<T *>(outputBuffer) + xOffset + tadOffsetOut) = *(
                                         reinterpret_cast<T const *>(inputBuffer) + yOffset + tadOffsetIn);
                     }
-//                if ((i >= j) && (i - j) <= lowerBand && (j - i) <= upperBand) // with in band
-//                    *(reinterpret_cast<T*>(outputBuffer) + xOffset + tadOffsetOut) = *(reinterpret_cast<T const*>(inputBuffer) + yOffset + tadOffsetIn);
-                    //else
-                    //    *(reinterpret_cast<T*>(outputBuffer) + xOffset + tadOffsetOut) = T(0);
                 }
             }
         }
 
     }
-
+////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
+// matrixBandPart_ - main algorithm caller
+//
     template <typename T>
     void matrixBandPart_(nd4j::LaunchContext * context, NDArray* input, NDArray* output, Nd4jLong lowerBand, Nd4jLong upperBand) {
         dim3 launchDims(256, 512, 8192);
@@ -84,17 +95,14 @@ namespace helpers {
 
         const Nd4jLong numTads = packX.numberOfTads();
 
-        if (!input->isActualOnDeviceSide())
-            input->syncToDevice();
-
-        if (!input->isActualOnDeviceSide())
-            input->syncToDevice();
-
+        NDArray::prepareSpecialUse({output}, {input});
         matrixBandKernel<T><<<launchDims.x, launchDims.y, launchDims.z, *stream>>>(input->getSpecialBuffer(),
                 input->getSpecialShapeInfo(), output->getSpecialBuffer(), output->getSpecialShapeInfo(),
                 lowerBand, upperBand, packX.specialShapeInfo(), packX.specialOffsets(), packZ.specialShapeInfo(), packZ.specialOffsets(), numTads, input->lengthOf());
+        NDArray::registerSpecialUse({output}, {input});
     }
 
+    ////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
     void matrixBandPart(nd4j::LaunchContext * context, NDArray* input, NDArray* output, Nd4jLong lowerBand, Nd4jLong upperBand) {
         BUILD_SINGLE_SELECTOR(input->dataType(), matrixBandPart_, (context, input, output, lowerBand, upperBand), FLOAT_TYPES);
     }
diff --git a/libnd4j/include/ops/declarable/helpers/cuda/matrix_diag_part.cu b/libnd4j/include/ops/declarable/helpers/cuda/matrix_diag_part.cu
index a83067f01..ca24b3466 100644
--- a/libnd4j/include/ops/declarable/helpers/cuda/matrix_diag_part.cu
+++ b/libnd4j/include/ops/declarable/helpers/cuda/matrix_diag_part.cu
@@ -31,6 +31,8 @@ namespace nd4j {
 namespace ops {
 namespace helpers {
 
+////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
+// put diagonals from input batched matricies to output batched vectors
     template <typename T>
     static __global__ void matrixDiagPartKernel(void const* inputBuffer, void* outputBuffer, Nd4jLong numTads, Nd4jLong inputLength,
                                             Nd4jLong* tadOnlyInputShapeInfo,  Nd4jLong *tadInputOffsets,
@@ -41,9 +43,8 @@ namespace helpers {
             auto xOffset = tadOutputOffsets[i];
             for (Nd4jLong j = threadIdx.x; j < inputLength; j += totalThreads) {
                 Nd4jLong coords[2] = {j, j};
-                Nd4jLong tadOffset = shape::getOffset(0, shape::shapeOf(tadOnlyInputShapeInfo), shape::stride(tadOnlyInputShapeInfo), coords, 2);
-                //shape::getIndexOffset(j, tadOnlyOutputShapeInfo, inputLength)
-                *(reinterpret_cast<T*>(outputBuffer) + xOffset + shape::getIndexOffset(j, tadOnlyOutputShapeInfo, inputLength)) = *(reinterpret_cast<T const*>(inputBuffer) + yOffset + tadOffset);
+                Nd4jLong tadOffset = shape::getOffset(tadOnlyInputShapeInfo, coords);
+                *(reinterpret_cast<T*>(outputBuffer) + xOffset + shape::getIndexOffset(j, tadOnlyOutputShapeInfo)) = *(reinterpret_cast<T const*>(inputBuffer) + yOffset + tadOffset);
             }
         }
     }
@@ -51,6 +52,7 @@ namespace helpers {
 //////////////////////////////////////////////////////////////////////////
 // Returns a batched matrix tensor with new batched diagonal values.
 // for detailed explanations please take a look on web page: https://www.tensorflow.org/api_docs/python/tf/matrix_set_diag
+//
     template <typename T>
     int _matrixDiagPart(nd4j::LaunchContext * context, const NDArray* input, NDArray* output) {
         auto stream = context->getCudaStream();
@@ -86,6 +88,9 @@ namespace helpers {
         return Status::OK();
     }
 
+////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
+// caller for _matrixDiagPart
+//
     int matrixDiagPart(nd4j::LaunchContext * context, const NDArray* input, NDArray* output) {
         BUILD_SINGLE_SELECTOR(input->dataType(), return _matrixDiagPart, (context, input, output), LIBND4J_TYPES);
     }
diff --git a/libnd4j/include/ops/declarable/helpers/cuda/max_pooling.cu b/libnd4j/include/ops/declarable/helpers/cuda/max_pooling.cu
index d5af6328a..aa129ee8e 100644
--- a/libnd4j/include/ops/declarable/helpers/cuda/max_pooling.cu
+++ b/libnd4j/include/ops/declarable/helpers/cuda/max_pooling.cu
@@ -27,12 +27,12 @@ namespace ops {
 namespace helpers {
 
     template <typename Z>
-    static _CUDA_G void indicesFiller(void *vz, Nd4jLong *zShapeInfo, Nd4jLong zLength, Nd4jLong part, Nd4jLong bSize) {
+    static _CUDA_G void indicesFiller(void *vz, Nd4jLong *zShapeInfo, Nd4jLong part, Nd4jLong bSize) {
         auto z = reinterpret_cast<Z*>(vz);
 
         for (int b = blockIdx.x; b < bSize; b += gridDim.x) {
             for (Nd4jLong e = threadIdx.x; e < part; e += blockDim.x) {
-                z[shape::getIndexOffset(e + b * part, zShapeInfo, zLength)] = static_cast<Z>(e);
+                z[shape::getIndexOffset(e + b * part, zShapeInfo)] = static_cast<Z>(e);
             }
         }
     }
@@ -74,7 +74,7 @@ namespace helpers {
             auto total = input->lengthOf();
             auto part = total / bSize;
 
-            indicesFiller<Y><<<256, 256, 1024, *block.launchContext()->getCudaStream()>>>(indices->specialBuffer(), indices->specialShapeInfo(), indices->lengthOf(), part, bSize);
+            indicesFiller<Y><<<256, 256, 1024, *block.launchContext()->getCudaStream()>>>(indices->specialBuffer(), indices->specialShapeInfo(), part, bSize);
 
             /*
             for (int k = 0; k < total; )
diff --git a/libnd4j/include/ops/declarable/helpers/cuda/merge.cu b/libnd4j/include/ops/declarable/helpers/cuda/merge.cu
index 27c8fc630..14fda24ec 100644
--- a/libnd4j/include/ops/declarable/helpers/cuda/merge.cu
+++ b/libnd4j/include/ops/declarable/helpers/cuda/merge.cu
@@ -47,7 +47,7 @@ namespace nd4j {
                     for (int i = 0; i < numArrays; i++) {
                         auto x = reinterpret_cast<T*>(inArrs[i]);
                         auto xShape = reinterpret_cast<Nd4jLong *>(inShapes[i]);
-                        auto val = x[shape::getIndexOffset(e, xShape, length)];;
+                        auto val = x[shape::getIndexOffset(e, xShape)];;
                         if (mVal < val) {
                             mIdx = static_cast<Z>(i);
                             mVal = val;
@@ -55,7 +55,7 @@ namespace nd4j {
                     }
                     __syncthreads();
 
-                    output[shape::getIndexOffset(e, outputShape, length)] = mIdx;
+                    output[shape::getIndexOffset(e, outputShape)] = mIdx;
                 }
             }
 
@@ -105,13 +105,13 @@ namespace nd4j {
                     for (int i = 0; i < numArrays; i++) {
                         auto x = reinterpret_cast<T*>(inArrs[i]);
                         auto xShape = reinterpret_cast<Nd4jLong *>(inShapes[i]);
-                        auto val = x[shape::getIndexOffset(e, xShape, length)];;
+                        auto val = x[shape::getIndexOffset(e, xShape)];;
                         if (mVal < val)
                             mVal = val;
                     }
                     __syncthreads();
 
-                    output[shape::getIndexOffset(e, outputShape, length)] = mVal;
+                    output[shape::getIndexOffset(e, outputShape)] = mVal;
                 }
             }
 
@@ -160,10 +160,10 @@ namespace nd4j {
                         auto x = reinterpret_cast<T*>(inArrs[i]);
                         auto xShape = reinterpret_cast<Nd4jLong *>(inShapes[i]);
 
-                        sum += x[shape::getIndexOffset(e, xShape, length)];
+                        sum += x[shape::getIndexOffset(e, xShape)];
                     }
 
-                    output[shape::getIndexOffset(e, outputShape, length)] = sum / numArrays;
+                    output[shape::getIndexOffset(e, outputShape)] = sum / numArrays;
                 }
             }
 
@@ -213,10 +213,10 @@ namespace nd4j {
                         auto x = reinterpret_cast<T*>(inArrs[i]);
                         auto xShape = reinterpret_cast<Nd4jLong *>(inShapes[i]);
 
-                        sum += x[shape::getIndexOffset(e, xShape, length)];
+                        sum += x[shape::getIndexOffset(e, xShape)];
                     }
 
-                    output[shape::getIndexOffset(e, outputShape, length)] = sum;
+                    output[shape::getIndexOffset(e, outputShape)] = sum;
                 }
             }
 
diff --git a/libnd4j/include/ops/declarable/helpers/cuda/meshgrid.cu b/libnd4j/include/ops/declarable/helpers/cuda/meshgrid.cu
index ea4a1e146..399447c9a 100644
--- a/libnd4j/include/ops/declarable/helpers/cuda/meshgrid.cu
+++ b/libnd4j/include/ops/declarable/helpers/cuda/meshgrid.cu
@@ -55,8 +55,8 @@ namespace helpers {
             }
         } else {
             for (int i = threadIdx.x; i < length; i += blockDim.x) {
-                auto xOffset = shape::getIndexOffset(i, xShapeInfo, length);
-                auto zOffset = shape::getIndexOffset(i, zShapeInfo, length);
+                auto xOffset = shape::getIndexOffset(i, xShapeInfo);
+                auto zOffset = shape::getIndexOffset(i, zShapeInfo);
 
                 z[zOffset] = x[xOffset];
             }
diff --git a/libnd4j/include/ops/declarable/helpers/cuda/nth_element.cu b/libnd4j/include/ops/declarable/helpers/cuda/nth_element.cu
index 3b80f3df9..bafbde2e2 100644
--- a/libnd4j/include/ops/declarable/helpers/cuda/nth_element.cu
+++ b/libnd4j/include/ops/declarable/helpers/cuda/nth_element.cu
@@ -31,22 +31,21 @@ namespace helpers {
 
     template <typename T>
     static __global__ void fillUpElementKernel(void* outputBuffer, Nd4jLong* outputShapeInfo, void* inputBuffer, Nd4jLong* inputShapeInfo, Nd4jLong* pTadShape, Nd4jLong* pTadOffsets, Nd4jLong n) {
-        __shared__ Nd4jLong bufferLength, arrLen;
+        __shared__ Nd4jLong bufferLength;
 
         auto z = reinterpret_cast<T*>(outputBuffer);
         auto x = reinterpret_cast<T*>(inputBuffer);
 
-        if (threadIdx.x == 0) {
-            arrLen = shape::length(pTadShape);
+        if (threadIdx.x == 0)
             bufferLength = shape::length(outputShapeInfo);
-        }
+
         __syncthreads();
 
         const auto tid = blockIdx.x * blockDim.x + threadIdx.x;
         const auto step = gridDim.x * blockDim.x;
         for (int t = tid; t < bufferLength; t += step) {
             auto tX = x + pTadOffsets[t];
-            z[shape::getIndexOffset(t, outputShapeInfo, bufferLength)] = tX[shape::getIndexOffset(n, pTadShape, arrLen)]; //tX];
+            z[shape::getIndexOffset(t, outputShapeInfo)] = tX[shape::getIndexOffset(n, pTadShape)]; //tX];
         }
     }
 
@@ -58,7 +57,7 @@ namespace helpers {
         Nd4jPointer params[2];
         params[0] = context;
         params[1] = context->getCudaStream();
-
+        // Nth element in sorted sequence : basic algorithm sort and retrieve nth element in sorted
         if (input->isVector()) {
             sort(params, nullptr, sortedVals.shapeInfo(), sortedVals.specialBuffer(), sortedVals.specialShapeInfo(), reverse);
 
@@ -72,9 +71,7 @@ namespace helpers {
             auto pTadShape = packX.specialShapeInfo();
             auto pTadShapeH = packX.primaryShapeInfo();
             auto pTadOffsets = packX.specialOffsets();
-//            auto pLastDimData = (int*) manager.replicatePointer(lastDims.data(), lastDims.size() * sizeof(int));
             sortTad(params, sortedVals.buffer(), sortedVals.shapeInfo(), sortedVals.specialBuffer(), sortedVals.specialShapeInfo(), lastDims.data(), lastDims.size(), pTadShape, pTadOffsets, reverse);
-//            manager.synchronize();
             sortedVals.tickWriteDevice();
             sortedVals.syncToHost();
             auto stream = context->getCudaStream();
diff --git a/libnd4j/include/ops/declarable/helpers/cuda/one_hot.cu b/libnd4j/include/ops/declarable/helpers/cuda/one_hot.cu
index 53b983d09..c0d1d95dc 100644
--- a/libnd4j/include/ops/declarable/helpers/cuda/one_hot.cu
+++ b/libnd4j/include/ops/declarable/helpers/cuda/one_hot.cu
@@ -61,14 +61,14 @@ __global__ static void onehotCuda(const void *vx, const Nd4jLong *xShapeInfo, vo
 
     for (Nd4jLong i = tid; i < zLen; i += totalThreads) {
 
-        shape::index2coords(zRank, shape::shapeOf(const_cast<Nd4jLong*>(zShapeInfo)), i, zLen, coord);
-        const auto zOffset = shape::getOffset(0, shape::shapeOf(const_cast<Nd4jLong*>(zShapeInfo)), shape::stride(const_cast<Nd4jLong*>(zShapeInfo)), coord, zRank);
+        shape::index2coords(i, zShapeInfo, coord);
+        const auto zOffset = shape::getOffset(zShapeInfo, coord);
         const auto depthCoord = coord[axis];
 
         for (uint j = axis; j < zRank - 1; ++j)
           coord[j] = coord[j + 1];
 
-        const auto xOffset = shape::getOffset(0, shape::shapeOf(const_cast<Nd4jLong*>(xShapeInfo)), shape::stride(const_cast<Nd4jLong*>(xShapeInfo)), coord, xRank);
+        const auto xOffset = shape::getOffset(xShapeInfo, coord);
         const Nd4jLong idx = x[xOffset];
         z[zOffset] = depthCoord == idx ? on : off;
     }
diff --git a/libnd4j/include/ops/declarable/helpers/cuda/pad.cu b/libnd4j/include/ops/declarable/helpers/cuda/pad.cu
index e19ddcb1b..aede6243a 100644
--- a/libnd4j/include/ops/declarable/helpers/cuda/pad.cu
+++ b/libnd4j/include/ops/declarable/helpers/cuda/pad.cu
@@ -48,7 +48,7 @@ namespace nd4j {
                 auto z = reinterpret_cast<X*>(vz);
 
                 __shared__ int rank, rankMinusOne;
-                __shared__ Nd4jLong zLen, yLen, totalThreads, *coords, *xShape, *zShape, *xStride, *zStride, shift1, shift2, yStride0;
+                __shared__ Nd4jLong zLen, totalThreads, *coords, *xShape, *zShape, shift1, shift2, yStride0;
 
                 if (threadIdx.x == 0) {
                     extern __shared__ unsigned char shmem[];
@@ -56,12 +56,9 @@ namespace nd4j {
                     zLen     = shape::length(zShapeInfo);
                     xShape   = shape::shapeOf(const_cast<Nd4jLong*>(xShapeInfo));
                     zShape   = shape::shapeOf(const_cast<Nd4jLong*>(zShapeInfo));
-                    xStride  = shape::stride(const_cast<Nd4jLong*>(xShapeInfo));
-                    zStride  = shape::stride(const_cast<Nd4jLong*>(zShapeInfo));
                     yStride0 = shape::stride(const_cast<Nd4jLong*>(yShapeInfo))[0];
                     rank     = shape::rank(xShapeInfo);
                     zLen     = shape::length(zShapeInfo);
-                    yLen     = 2 * rank;
                     rankMinusOne = rank - 1;
                     totalThreads = gridDim.x * blockDim.x;
                     shift1 = mode == 1 ? 0 : 1;         // REFLECT : SYMMETRIC
@@ -78,19 +75,19 @@ namespace nd4j {
 
                     for (Nd4jLong i = tid; i < zLen; i += totalThreads) {
 
-                        shape::index2coords(rank, zShape, i, zLen, xzCoord);
-                        const auto zOffset = shape::getOffset(0, zShape, zStride, xzCoord, rank);
+                        shape::index2coords(i, zShapeInfo, xzCoord);
+                        const auto zOffset = shape::getOffset(zShapeInfo, xzCoord);
 
                         bool within = true;
                         for(int j = rankMinusOne; j >= 0; --j) {
                             if(xShape[j] == zShape[j]) continue;
-                            const auto left = y[shape::getIndexOffset(yStride0 * j, yShapeInfo, yLen)];
+                            const auto left = y[shape::getIndexOffset(yStride0 * j, yShapeInfo)];
                             if(xzCoord[j] < left || xzCoord[j] >= left + xShape[j]) {within = false; break;}
                             else                                                    {xzCoord[j] = xzCoord[j] - left;}
                         }
 
                         if(within)
-                            z[zOffset] = x[shape::getOffset(0, xShape, xStride, xzCoord, rank)];
+                            z[zOffset] = x[shape::getOffset(xShapeInfo, xzCoord)];
                         else
                             z[zOffset] = padVal;
                     }
@@ -99,18 +96,18 @@ namespace nd4j {
 
                     for (Nd4jLong i = tid; i < zLen; i += totalThreads) {
 
-                        shape::index2coords(rank, zShape, i, zLen, xzCoord);
-                        const auto zOffset = shape::getOffset(0, zShape, zStride, xzCoord, rank);
+                        shape::index2coords(i, zShapeInfo, xzCoord);
+                        const auto zOffset = shape::getOffset(zShapeInfo, xzCoord);
 
                         for(int j = rankMinusOne; j >= 0; --j) {
 
                             if(xShape[j] == zShape[j]) continue;
-                            xzCoord[j] = xzCoord[j] - y[shape::getIndexOffset(yStride0 * j, yShapeInfo, yLen)];    // are ready to fill middle (within input dimension range)
+                            xzCoord[j] = xzCoord[j] - y[shape::getIndexOffset(yStride0 * j, yShapeInfo)];    // are ready to fill middle (within input dimension range)
                             if(xzCoord[j] < 0)               xzCoord[j] = -xzCoord[j] - shift1;                // means fill from left
                             else if(xzCoord[j] >= xShape[j]) xzCoord[j] = 2 * xShape[j] - xzCoord[j] - shift2; // means fill from right
                         }
 
-                        const auto xOffset = shape::getOffset(0, xShape, xStride, xzCoord, rank);
+                        const auto xOffset = shape::getOffset(xShapeInfo, xzCoord);
                         z[zOffset] = x[xOffset];
                     }
                 }
@@ -164,14 +161,14 @@ namespace nd4j {
                 auto step = blockDim.x * gridDim.x;
 
                 for(int i = start; i < zLen; i+= step) {
-                    auto zIndex = shape::getIndexOffset(i, zShape, zLen);
-                    auto xIndex = shape::getIndexOffset(len - i, xShape, xLen);
+                    auto zIndex = shape::getIndexOffset(i, zShape);
+                    auto xIndex = shape::getIndexOffset(len - i, xShape);
 
                     if (i < leftSide)                                   // left side
-                        xIndex = shape::getIndexOffset(leftSideCorrected - i, xShape, xLen);
+                        xIndex = shape::getIndexOffset(leftSideCorrected - i, xShape);
 
                     else if(i >= leftSide && i < leftSide + xLen)       // middle
-                        xIndex = shape::getIndexOffset(i - leftSide, xShape, xLen);
+                        xIndex = shape::getIndexOffset(i - leftSide, xShape);
 
 //            else                                                // right side
 //                z[i] = x[len - i];
@@ -187,8 +184,6 @@ namespace nd4j {
                 __shared__ I const* pads;
                 __shared__ F* z;
                 __shared__ Nd4jLong zRank, rank;
-                __shared__ Nd4jLong* xShapeOf, *xStrideOf, *padsShapeOf, *padsStrideOf;
-                __shared__ Nd4jLong* zShapeOf, *zStrideOf;
                 __shared__ Nd4jLong* xIdx;
                 if (threadIdx.x == 0) {
                     extern __shared__ unsigned char shmem[];
@@ -198,13 +193,6 @@ namespace nd4j {
                     x = reinterpret_cast<F const*>(vx);//
                     pads = reinterpret_cast<I const*>(paddings);
                     z = reinterpret_cast<F*>(vz);
-                    xShapeOf = shape::shapeOf(xShape);
-                    xStrideOf = shape::stride(xShape);
-                    zShapeOf = shape::shapeOf(zShape);
-                    zRank = shape::rank(zShape);
-                    zStrideOf = shape::stride(zShape);
-                    padsShapeOf = shape::shapeOf(paddingShape);
-                    padsStrideOf = shape::stride(paddingShape);
                 }
                 __syncthreads();
                 auto start = threadIdx.x + blockIdx.x * blockDim.x;
@@ -214,14 +202,14 @@ namespace nd4j {
                     auto xzCoord = xIdx + threadIdx.x * rank;
                     //auto zxCoord = xIdx + (threadIdx.x + threadIdx.x % 2 + 1) * rank;
 
-                    shape::index2coords(rank, zShapeOf, i, xzCoord);
-                    auto outOffset = shape::getOffset(0, zShapeOf, zStrideOf, xzCoord, rank);
+                    shape::index2coords(i, zShape, xzCoord);
+                    auto outOffset = shape::getOffset(zShape, xzCoord);
 //                auto intStep = blockDim.y * gridDim.y;
                     for(int j = 0; j < rank; j++) {
 
                         const Nd4jLong inLen         = shape::sizeAt(xShape, j);
                         Nd4jLong coords[2] = {j, 0};
-                        auto padOffset = shape::getOffset(0, padsShapeOf, padsStrideOf, coords, 2); // padding already has rank 2
+                        auto padOffset = shape::getOffset(paddingShape, coords); // padding already has rank 2
                         const auto leftSide          = pads[padOffset];
                         const auto leftSideCorrected = leftSide - reflBorder;
                         const Nd4jLong len           = 2 * (inLen - 1) + leftSide + reflBorder;
@@ -238,7 +226,7 @@ namespace nd4j {
                             xzCoord[j] = xzCoord[j] - len;
                     }
 
-                    auto inOffset  = shape::getOffset(0, xShapeOf, xStrideOf,  xzCoord,  rank);
+                    auto inOffset  = shape::getOffset(xShape, xzCoord);
                     z[outOffset] = x[inOffset];
                 }
             }
diff --git a/libnd4j/include/ops/declarable/helpers/cuda/percentile.cu b/libnd4j/include/ops/declarable/helpers/cuda/percentile.cu
index 7b325eb3e..ccfbbf943 100644
--- a/libnd4j/include/ops/declarable/helpers/cuda/percentile.cu
+++ b/libnd4j/include/ops/declarable/helpers/cuda/percentile.cu
@@ -43,8 +43,8 @@ namespace helpers {
                         for (int tid = threadIdx.x; tid < tadLength; tid += blockDim.x) {
                             auto top = 2 * tid + 1;
                             if (top < tadLength) {
-                                auto t0 = shape::getIndexOffset(top - 1, xTadShapeInfo, tadLength);
-                                auto t1 = shape::getIndexOffset(top, xTadShapeInfo, tadLength);
+                                auto t0 = shape::getIndexOffset(top - 1, xTadShapeInfo);
+                                auto t1 = shape::getIndexOffset(top, xTadShapeInfo);
 
                                 if (x[t0] > x[t1]) {
                                     //swap values
@@ -58,8 +58,8 @@ namespace helpers {
                         for (int tid = threadIdx.x; tid < tadLength; tid += blockDim.x) {
                             auto top = 2 * tid + 2;
                             if (top < tadLength) {
-                                auto t0 = shape::getIndexOffset(top - 1, xTadShapeInfo, tadLength);
-                                auto t1 = shape::getIndexOffset(top, xTadShapeInfo, tadLength);
+                                auto t0 = shape::getIndexOffset(top - 1, xTadShapeInfo);
+                                auto t1 = shape::getIndexOffset(top, xTadShapeInfo);
 
                                 if (x[t0] > x[t1]) {
                                     //swap values
@@ -76,7 +76,7 @@ namespace helpers {
 
             // saving final value
             if (threadIdx.x == 0)
-                z[shape::getIndexOffset(t, zShapeInfo, zLength)] = x[shape::getIndexOffset(position, xTadShapeInfo, tadLength)];
+                z[shape::getIndexOffset(t, zShapeInfo)] = x[shape::getIndexOffset(position, xTadShapeInfo)];
             __syncthreads();
         }
     }
diff --git a/libnd4j/include/ops/declarable/helpers/cuda/polyGamma.cu b/libnd4j/include/ops/declarable/helpers/cuda/polyGamma.cu
index bddaf65e3..01b9464fa 100644
--- a/libnd4j/include/ops/declarable/helpers/cuda/polyGamma.cu
+++ b/libnd4j/include/ops/declarable/helpers/cuda/polyGamma.cu
@@ -34,31 +34,31 @@ __global__ static void polyGammaCuda(const void *vn, const Nd4jLong *nShapeInfo,
 
     const auto n = reinterpret_cast<const T*>(vn);
     const auto x = reinterpret_cast<const T*>(vx);
-          auto z = reinterpret_cast<T*>(vz);    
+          auto z = reinterpret_cast<T*>(vz);
 
     __shared__ Nd4jLong len;
-    
-    if (threadIdx.x == 0)         
-        len = shape::length(nShapeInfo);    
+
+    if (threadIdx.x == 0)
+        len = shape::length(nShapeInfo);
     __syncthreads();
 
     const auto tid = blockIdx.x * blockDim.x + threadIdx.x;
     const auto totalThreads = gridDim.x * blockDim.x;
 
     for (int i = tid; i < len; i += totalThreads) {
-        
-        const auto nOffset = shape::getIndexOffset(i, nShapeInfo, len);    
-        const auto xOffset = shape::getIndexOffset(i, xShapeInfo, len);        
-        const auto zOffset = shape::getIndexOffset(i, zShapeInfo, len);
 
-        const T nVal = n[nOffset]; 
-        
+        const auto nOffset = shape::getIndexOffset(i, nShapeInfo);
+        const auto xOffset = shape::getIndexOffset(i, xShapeInfo);
+        const auto zOffset = shape::getIndexOffset(i, zShapeInfo);
+
+        const T nVal = n[nOffset];
+
         int sign = (static_cast<int>(nVal) + 1) % 2  ?  -1 : 1;
 
         T factorial = 1;
         if(nVal != 0 && nVal != 1)
         	for(int i = 2; i <= nVal; ++i)
-				factorial *= i;	
+				factorial *= i;
 
         z[zOffset] = sign * factorial * zetaScalar<T>(nVal + 1, x[xOffset]);
     }
@@ -75,10 +75,10 @@ static void polyGammaCudaLauncher(const int blocksPerGrid, const int threadsPerB
 void polyGamma(nd4j::LaunchContext * context, const NDArray& n, const NDArray& x, NDArray& z) {
 
     NDArray::prepareSpecialUse({&z}, {&n, &x});
-        
+
     int threadsPerBlock = MAX_NUM_THREADS;
     int blocksPerGrid = (z.lengthOf() + threadsPerBlock - 1) / threadsPerBlock;
-    
+
     BUILD_SINGLE_SELECTOR(n.dataType(), polyGammaCudaLauncher, (blocksPerGrid, threadsPerBlock, context->getCudaStream(), n.getSpecialBuffer(), n.getSpecialShapeInfo(), x.getSpecialBuffer(), x.getSpecialShapeInfo(), z.getSpecialBuffer(), z.getSpecialShapeInfo()), FLOAT_TYPES);
 
     NDArray::registerSpecialUse({&z}, {&n, &x});
diff --git a/libnd4j/include/ops/declarable/helpers/cuda/prefix.cu b/libnd4j/include/ops/declarable/helpers/cuda/prefix.cu
index b1412343b..52dd8b815 100644
--- a/libnd4j/include/ops/declarable/helpers/cuda/prefix.cu
+++ b/libnd4j/include/ops/declarable/helpers/cuda/prefix.cu
@@ -68,12 +68,12 @@ __global__ static void prefixPerBlockCuda(scalar::Ops op,
         }
 
         if(leftArrInd < tadLen)
-            shared[sharedInd] = xLeft = xTad[shape::getIndexOffset(leftArrInd, xTadShapeInfo, tadLen)];
+            shared[sharedInd] = xLeft = xTad[shape::getIndexOffset(leftArrInd, xTadShapeInfo)];
         // else
         //     shared[sharedInd] = (op == scalar::Add) ? 0 : 1;
 
         if(rightArrInd < tadLen)
-            shared[sharedInd + 1] = xRight = xTad[shape::getIndexOffset(rightArrInd, xTadShapeInfo, tadLen)];
+            shared[sharedInd + 1] = xRight = xTad[shape::getIndexOffset(rightArrInd, xTadShapeInfo)];
         // else
         //     shared[sharedInd + 1] = (op == scalar::Add) ? 0 : 1;
 
@@ -117,7 +117,7 @@ __global__ static void prefixPerBlockCuda(scalar::Ops op,
                 result = (op == scalar::Add) ? result + xLeft : result * xLeft;
             if(i > 0)
                 result = (op == scalar::Add) ? result + lastElemInChunk : result * lastElemInChunk;
-            zTad[shape::getIndexOffset(leftArrInd, zTadShapeInfo, tadLen)] = result;
+            zTad[shape::getIndexOffset(leftArrInd, zTadShapeInfo)] = result;
         }
 
         if(rightArrInd < tadLen) {
@@ -128,7 +128,7 @@ __global__ static void prefixPerBlockCuda(scalar::Ops op,
                 result = (op == scalar::Add) ? result + lastElemInChunk : result * lastElemInChunk;
             if(i < numTadChunks - 1 && threadIdx.x == blockDim.x - 1)    // last element in chunk
                 lastElemInChunk = !exclusive ? result : (op == scalar::Add) ? result + xRight : result * xRight;
-            zTad[shape::getIndexOffset(rightArrInd, zTadShapeInfo, tadLen)] = result;
+            zTad[shape::getIndexOffset(rightArrInd, zTadShapeInfo)] = result;
         }
     }
 }
diff --git a/libnd4j/include/ops/declarable/helpers/cuda/reverse.cu b/libnd4j/include/ops/declarable/helpers/cuda/reverse.cu
index e86cd382a..aceebf7a0 100644
--- a/libnd4j/include/ops/declarable/helpers/cuda/reverse.cu
+++ b/libnd4j/include/ops/declarable/helpers/cuda/reverse.cu
@@ -34,14 +34,12 @@ namespace helpers {
     static __global__ void reverseArrayKernel(void* input, Nd4jLong *inputShape, void* output, Nd4jLong *outputShape, Nd4jLong numOfElemsToReverse) {
         const auto tid = blockIdx.x * blockDim.x + threadIdx.x;
         const auto step = gridDim.x * blockDim.x;
-        __shared__ Nd4jLong length;
         __shared__ int linearStatus;
         __shared__ T* inputArr;
         __shared__ T* outputArr;
         __shared__ char inputOrder, outputOrder;
 
         if (threadIdx.x == 0) {
-            length = shape::length(inputShape);
             linearStatus = (shape::elementWiseStride(inputShape) == shape::elementWiseStride(outputShape)) && (inputOrder == outputOrder)? shape::elementWiseStride(inputShape):0;
 
             char inputOrder = shape::order(inputShape);
@@ -56,31 +54,28 @@ namespace helpers {
 
         for (Nd4jLong e = tid; e < limit; e += step) {
             // we're calculating offsets within input array
-            auto fOffset = shape::getIndexOffset(e, inputShape, length);
-            auto lOffset = shape::getIndexOffset(numOfElemsToReverse - e - 1, inputShape, length);
+            auto fOffset = shape::getIndexOffset(e, inputShape);
+            auto lOffset = shape::getIndexOffset(numOfElemsToReverse - e - 1, inputShape);
 
             // now we're storing input values
             auto v1 = inputArr[fOffset];
             auto v2 = inputArr[lOffset];
 
             // now we're calculating offsets within output array
-            auto zfOffset = shape::getIndexOffset(e, outputShape, length);
-            auto zlOffset = shape::getIndexOffset(numOfElemsToReverse - e - 1, outputShape, length);
+            auto zfOffset = shape::getIndexOffset(e, outputShape);
+            auto zlOffset = shape::getIndexOffset(numOfElemsToReverse - e - 1, outputShape);
 
             // and saving values to output arrays
             outputArr[zfOffset] = v2;
             outputArr[zlOffset] = v1;
-
-            //printf("TID: %i; E: %lld; z[%lld], z[%lld] = x[%lld], x[%lld];\n", tid, e, zfOffset, zlOffset, lOffset, fOffset);
         }
 
         // in case of odd array we'll have to move middle value
         if (odd && tid == 0) {
-            auto xOffset = shape::getIndexOffset(limit, inputShape, length);
-            auto zOffset = shape::getIndexOffset(limit, outputShape, length);
+            auto xOffset = shape::getIndexOffset(limit, inputShape);
+            auto zOffset = shape::getIndexOffset(limit, outputShape);
 
             outputArr[zOffset] = inputArr[xOffset];
-            //printf("TID: %i; E: %lld; z[%lld] = x[%lld];\n", tid, limit, zOffset, xOffset);
         }
     }
 
diff --git a/libnd4j/include/ops/declarable/helpers/cuda/roll.cu b/libnd4j/include/ops/declarable/helpers/cuda/roll.cu
index 216c6b7a0..d843feeff 100644
--- a/libnd4j/include/ops/declarable/helpers/cuda/roll.cu
+++ b/libnd4j/include/ops/declarable/helpers/cuda/roll.cu
@@ -53,11 +53,11 @@ namespace helpers {
             for (int i = tid; i < actualShift; i += blockDim.x * gridDim.x) {
                 int sourceIndex = fullLength - actualShift + i;
 
-                auto xOffsetA = shape::getIndexOffset(i, xShapeInfo, fullLength);
-                auto xOffsetB = shape::getIndexOffset(sourceIndex, xShapeInfo, fullLength);
+                auto xOffsetA = shape::getIndexOffset(i, xShapeInfo);
+                auto xOffsetB = shape::getIndexOffset(sourceIndex, xShapeInfo);
 
-                auto zOffsetA = shape::getIndexOffset(i, zShapeInfo, fullLength);
-                auto zOffsetB = shape::getIndexOffset(sourceIndex, zShapeInfo, fullLength);
+                auto zOffsetA = shape::getIndexOffset(i, zShapeInfo);
+                auto zOffsetB = shape::getIndexOffset(sourceIndex, zShapeInfo);
 
                 auto eA = x[xOffsetA];
                 auto eB = x[xOffsetB];
@@ -107,11 +107,11 @@ namespace helpers {
                     int destinationIndex = fullLength - (count + 1) * actualShift + i;
                     int sourceIndex = fullLength - count * actualShift + i;
 
-                    auto xOffsetA = shape::getIndexOffset(destinationIndex, xShapeInfo, fullLength);
-                    auto xOffsetB = shape::getIndexOffset(sourceIndex, xShapeInfo, fullLength);
+                    auto xOffsetA = shape::getIndexOffset(destinationIndex, xShapeInfo);
+                    auto xOffsetB = shape::getIndexOffset(sourceIndex, xShapeInfo);
 
-                    auto zOffsetA = shape::getIndexOffset(destinationIndex, zShapeInfo, fullLength);
-                    auto zOffsetB = shape::getIndexOffset(sourceIndex, zShapeInfo, fullLength);
+                    auto zOffsetA = shape::getIndexOffset(destinationIndex, zShapeInfo);
+                    auto zOffsetB = shape::getIndexOffset(sourceIndex, zShapeInfo);
 
                     auto eA = x[xOffsetA];
                     auto eB = x[xOffsetB];
@@ -154,11 +154,11 @@ namespace helpers {
                 int remainIdx = i + actualShift;
                 int sourceIndex = remainIdx + remainShift;
 
-                auto xOffsetA = shape::getIndexOffset(remainIdx, xShapeInfo, fullLength);
-                auto xOffsetB = shape::getIndexOffset(sourceIndex, xShapeInfo, fullLength);
+                auto xOffsetA = shape::getIndexOffset(remainIdx, xShapeInfo);
+                auto xOffsetB = shape::getIndexOffset(sourceIndex, xShapeInfo);
 
-                auto zOffsetA = shape::getIndexOffset(remainIdx, zShapeInfo, fullLength);
-                auto zOffsetB = shape::getIndexOffset(sourceIndex, zShapeInfo, fullLength);
+                auto zOffsetA = shape::getIndexOffset(remainIdx, zShapeInfo);
+                auto zOffsetB = shape::getIndexOffset(sourceIndex, zShapeInfo);
 
                 auto eA = x[xOffsetA];
                 auto eB = x[xOffsetB];
@@ -190,7 +190,7 @@ namespace helpers {
             }
         } else {
             for (int e = threadIdx.x; e < tadLength; e += blockDim.x) {
-                auto zOffset = shape::getIndexOffset(e, zShapeInfo, tadLength);
+                auto zOffset = shape::getIndexOffset(e, zShapeInfo);
 
                 auto eA = x[zOffset];
                 auto eB = z[zOffset];
diff --git a/libnd4j/include/ops/declarable/helpers/cuda/s_t_b.cu b/libnd4j/include/ops/declarable/helpers/cuda/s_t_b.cu
index 0ac0a1882..82f421fdd 100644
--- a/libnd4j/include/ops/declarable/helpers/cuda/s_t_b.cu
+++ b/libnd4j/include/ops/declarable/helpers/cuda/s_t_b.cu
@@ -62,14 +62,14 @@ __global__ static void batchToSpaceCuda(const void* vx, const Nd4jLong* xShapeIn
     if(i >= zLen)
         return;
 
-    shape::index2coords(rank, zShapeInfo + 1, i, zLen, coords);
+    shape::index2coords(i, zShapeInfo, coords);
 
-    const auto zOffset = shape::getOffset(0, zShapeInfo + 1, zShapeInfo + rank + 1, coords, rank);
+    const auto zOffset = shape::getOffset(zShapeInfo, coords);
 
     coords[1] += cropBottom;
     coords[2] += cropLeft;
 
-    const auto xOffset = shape::getOffset(0, xShapeInfo + 1, xShapeInfo + rank + 1, coords, rank);
+    const auto xOffset = shape::getOffset(xShapeInfo, coords);
 
     z[zOffset] = x[xOffset];
 
@@ -156,9 +156,9 @@ __global__ static void batchToSpaceNDCuda(const void* vx, const Nd4jLong* xShape
 
     for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < zLen; i += gridDim.x * blockDim.x) {
 
-        shape::index2coords(rank, zShapeInfo + 1, i, zLen, coords);
+        shape::index2coords(i, zShapeInfo, coords);
 
-        const auto zOffset = shape::getOffset(0, zShapeInfo + 1, zShapeInfo + rank + 1, coords, rank);
+        const auto zOffset = shape::getOffset(zShapeInfo, coords);
 
         // evaluate spatial coordinates for x
         for(uint j = 1; j <= numOfSpatialDims; ++j) {
@@ -166,7 +166,7 @@ __global__ static void batchToSpaceNDCuda(const void* vx, const Nd4jLong* xShape
             coords[j] += y[yOffset];                        // add crop left
         }
 
-        const auto xOffset = shape::getOffset(0, xShapeInfo + 1, xShapeInfo + rank + 1, coords, rank);
+        const auto xOffset = shape::getOffset(xShapeInfo, coords);
 
         z[zOffset] = x[xOffset];
     }
@@ -283,16 +283,16 @@ __global__ static void spaceToBatchCuda(const void* vx, const Nd4jLong* xShapeIn
     if(i >= zLen)
         return;
 
-    shape::index2coords(rank, zShapeInfo + 1, i, zLen, coords);
+    shape::index2coords(i, zShapeInfo, coords);
 
-    const auto zOffset = shape::getOffset(0, zShapeInfo + 1, zShapeInfo + rank + 1, coords, rank);
+    const auto zOffset = shape::getOffset(zShapeInfo, coords);
 
     if(coords[1] >= padBottom && coords[1] < zShapeInfo[2] - padTop && coords[2] >= padLeft && coords[2] < zShapeInfo[3] - padRight) {
 
         coords[1] -= padBottom;
         coords[2] -= padLeft;
 
-        const auto xOffset = shape::getOffset(0, xShapeInfo + 1, xShapeInfo + rank + 1, coords, rank);
+        const auto xOffset = shape::getOffset(xShapeInfo, coords);
 
         z[zOffset] = x[xOffset];
     }
@@ -383,9 +383,9 @@ __global__ static void spaceToBatchNDCuda(const void* vx, const Nd4jLong* xShape
 
     for (int i = blockDim.x * blockIdx.x + threadIdx.x; i < zLen; i += totalThreads) {
 
-        shape::index2coords(rank, zShapeInfo + 1, i, zLen, coords);
+        shape::index2coords(i, zShapeInfo, coords);
 
-        const auto zOffset = shape::getOffset(0, zShapeInfo + 1, zShapeInfo + rank + 1, coords, rank);
+        const auto zOffset = shape::getOffset(zShapeInfo, coords);
 
         bool within = true;
 
@@ -405,7 +405,7 @@ __global__ static void spaceToBatchNDCuda(const void* vx, const Nd4jLong* xShape
         }
 
         if(within)
-            z[zOffset] = x[shape::getOffset(0, xShapeInfo + 1, xShapeInfo + rank + 1, coords, rank)];
+            z[zOffset] = x[shape::getOffset(xShapeInfo, coords)];
         else
             z[zOffset] = 0.f;
     }
diff --git a/libnd4j/include/ops/declarable/helpers/cuda/scatter.cu b/libnd4j/include/ops/declarable/helpers/cuda/scatter.cu
index 54d350f47..501b9bca4 100644
--- a/libnd4j/include/ops/declarable/helpers/cuda/scatter.cu
+++ b/libnd4j/include/ops/declarable/helpers/cuda/scatter.cu
@@ -57,8 +57,8 @@ namespace helpers {
 
             //             for (Nd4jLong i = threadIdx.x; i < arrLenX; i += blockDim.x) {
 
-            //                 const auto xOffset = shape::getIndexOffset(i, xShapeInfo, arrLenX);
-            //                 const auto yOffset = shape::getIndexOffset(i, yShapeInfo, arrLenY);
+            //                 const auto xOffset = shape::getIndexOffset(i, xShapeInfo);
+            //                 const auto yOffset = shape::getIndexOffset(i, yShapeInfo);
 
             //                 switch (opCode) {
             //                     case pairwise::Add:
@@ -99,8 +99,8 @@ namespace helpers {
             //             __syncthreads();
 
             //             for (Nd4jLong i = threadIdx.x; i < arrLenX; i += blockDim.x) {
-            //                 const auto xOffset = shape::getIndexOffset(i, xShapeInfo, arrLenX);
-            //                 const auto yOffset = shape::getIndexOffset(i, yShapeInfo, arrLenY);
+            //                 const auto xOffset = shape::getIndexOffset(i, xShapeInfo);
+            //                 const auto yOffset = shape::getIndexOffset(i, yShapeInfo);
 
             //                 switch (opCode) {
             //                     case pairwise::Add:
@@ -188,7 +188,7 @@ __global__ static void scatterLockCuda(const int opCode,
 
     for (int e = 0; e < xLen; e++) {
 
-        const Nd4jLong zIndex = x[shape::getIndexOffset(e, xShapeInfo, xLen)];
+        const Nd4jLong zIndex = x[shape::getIndexOffset(e, xShapeInfo)];
         const bool isOwner = zIndex < gridDim.x ? blockIdx.x == zIndex : blockIdx.x == zIndex % gridDim.x;
 
         if (!isOwner)
@@ -199,8 +199,8 @@ __global__ static void scatterLockCuda(const int opCode,
             if(threadIdx.x != 0)
                 continue;
 
-            const auto yOffset = shape::getIndexOffset(e,      yTadShapeInfo, yTadLen);
-            const auto zOffset = shape::getIndexOffset(zIndex, zTadShapeInfo, zTadLen);
+            const auto yOffset = shape::getIndexOffset(e,      yTadShapeInfo);
+            const auto zOffset = shape::getIndexOffset(zIndex, zTadShapeInfo);
 
             switch (opCode) {
                 case pairwise::Add:
@@ -241,8 +241,8 @@ __global__ static void scatterLockCuda(const int opCode,
 
             for (Nd4jLong i = threadIdx.x; i < zTadLen; i += blockDim.x) {
 
-                const auto yOffset = shape::getIndexOffset(i, yTadShapeInfo, zTadLen);
-                const auto zOffset = shape::getIndexOffset(i, zTadShapeInfo, zTadLen);
+                const auto yOffset = shape::getIndexOffset(i, yTadShapeInfo);
+                const auto zOffset = shape::getIndexOffset(i, zTadShapeInfo);
 
                 switch (opCode) {
                     case pairwise::Add:
@@ -326,19 +326,19 @@ __global__ static void scatterCuda(const int opCode,
 
     for (Nd4jLong i = tid; i < yLen; i += totalThreads) {
 
-        shape::index2coords(yRank, shape::shapeOf(const_cast<Nd4jLong*>(yShapeInfo)), i, yLen, yCoord);
+        shape::index2coords(i, yShapeInfo, yCoord);
 
         for (uint j = 0; j < xRank; ++j)
             xCoord[j] = yCoord[j];
 
-        const auto xOffset = shape::getOffset(0, shape::shapeOf(const_cast<Nd4jLong*>(xShapeInfo)), shape::stride(const_cast<Nd4jLong*>(xShapeInfo)), xCoord, xRank);
+        const auto xOffset = shape::getOffset(xShapeInfo, xCoord);
         zCoord[0] = x[xOffset];
 
         for (uint j = 0; j < yRank - xRank; ++j)
             zCoord[j + 1] = yCoord[xRank + j];
 
-        const auto yOffset = shape::getOffset(0, shape::shapeOf(const_cast<Nd4jLong*>(yShapeInfo)), shape::stride(const_cast<Nd4jLong*>(yShapeInfo)), yCoord, yRank);
-        const auto zOffset = shape::getOffset(0, shape::shapeOf(const_cast<Nd4jLong*>(zShapeInfo)), shape::stride(const_cast<Nd4jLong*>(zShapeInfo)), zCoord, zRank);
+        const auto yOffset = shape::getOffset(yShapeInfo, yCoord);
+        const auto zOffset = shape::getOffset(zShapeInfo, zCoord);
 
         switch (opCode) {
             case pairwise::Add:
@@ -471,9 +471,9 @@ __global__ static void scatterNDLockCuda(const int opCode,
         const X* xTad = x + xOffsets[i];
 
         for (uint k = 0; k < xLastDim; ++k)
-            zTadCoordsPerThread[k] = xTad[shape::getIndexOffset(k, xTadShapeInfo, xLastDim)];
+            zTadCoordsPerThread[k] = xTad[shape::getIndexOffset(k, xTadShapeInfo)];
 
-        const auto zTadIndex = shape::coords2index(xLastDim, shape::shapeOf(const_cast<Nd4jLong*>(zShapeInfo)), zTadCoordsPerThread);
+        const auto zTadIndex = shape::coords2index(xLastDim, zShapeInfo + 1, zTadCoordsPerThread);
 
         const bool isOwner = zTadIndex < gridDim.x ? blockIdx.x == zTadIndex : blockIdx.x == zTadIndex % gridDim.x;
 
@@ -485,8 +485,8 @@ __global__ static void scatterNDLockCuda(const int opCode,
             if(threadIdx.x != 0)
                 continue;
 
-            const auto yOffset = shape::getIndexOffset(i,         yTadShapeInfo, yTadLen);
-            const auto zOffset = shape::getIndexOffset(zTadIndex, zTadShapeInfo, yTadLen);
+            const auto yOffset = shape::getIndexOffset(i,         yTadShapeInfo);
+            const auto zOffset = shape::getIndexOffset(zTadIndex, zTadShapeInfo);
 
             switch (opCode) {
                 case pairwise::Add:
@@ -526,8 +526,8 @@ __global__ static void scatterNDLockCuda(const int opCode,
 
             for (Nd4jLong j = threadIdx.x; j < yTadLen; j += blockDim.x) {
 
-                const auto yOffset = shape::getIndexOffset(j, yTadShapeInfo, yTadLen);
-                const auto zOffset = shape::getIndexOffset(j, zTadShapeInfo, yTadLen);
+                const auto yOffset = shape::getIndexOffset(j, yTadShapeInfo);
+                const auto zOffset = shape::getIndexOffset(j, zTadShapeInfo);
 
                 switch (opCode) {
                     case pairwise::Add:
@@ -618,22 +618,22 @@ __global__ static void scatterNDCuda(const int opCode,
 
     for (Nd4jLong i = tid; i < yLen; i += totalThreads) {
 
-        shape::index2coords(yRank, shape::shapeOf(const_cast<Nd4jLong*>(yShapeInfo)), i, yLen, yCoord);
+        shape::index2coords(i, yShapeInfo, yCoord);
 
         for (uint j = 0; j < xRank - 1; ++j)
             xCoord[j] = yCoord[j];
 
         for (uint j = 0; j < xLastDim; ++j) {
             xCoord[xRank - 1] = j;
-            const auto xOffset = shape::getOffset(0, shape::shapeOf(const_cast<Nd4jLong*>(xShapeInfo)), shape::stride(const_cast<Nd4jLong*>(xShapeInfo)), xCoord, xRank);
+            const auto xOffset = shape::getOffset(xShapeInfo, xCoord);
             zCoord[j] = x[xOffset];
         }
 
         for (uint j = xLastDim; j < zRank; ++j)
             zCoord[j] = yCoord[yRank - zRank + j];
 
-        const auto yOffset = shape::getOffset(0, shape::shapeOf(const_cast<Nd4jLong*>(yShapeInfo)), shape::stride(const_cast<Nd4jLong*>(yShapeInfo)), yCoord, yRank);
-        const auto zOffset = shape::getOffset(0, shape::shapeOf(const_cast<Nd4jLong*>(zShapeInfo)), shape::stride(const_cast<Nd4jLong*>(zShapeInfo)), zCoord, zRank);
+        const auto yOffset = shape::getOffset(yShapeInfo, yCoord);
+        const auto zOffset = shape::getOffset(zShapeInfo, zCoord);
 
         switch (opCode) {
             case pairwise::Add:
@@ -760,18 +760,18 @@ __global__ void scatterForLossCuda(const void *vx, const Nd4jLong *xShapeInfo,
 
     auto coords = sharedMem + threadIdx.x * (xRank + 1);
 
-    shape::index2coords(xRank, xShapeInfo + 1, xInd, xLen, coords);
+    shape::index2coords(xInd, xShapeInfo, coords);
 
     // y last coordinate
-    coords[xRank] = x[shape::getOffset(0, xShapeInfo + 1, xShapeInfo + xRank + 1, coords, xRank)];
+    coords[xRank] = x[shape::getOffset(xShapeInfo, coords)];
 
-    const auto yOffset = shape::getOffset(0, yShapeInfo + 1, yShapeInfo + xRank + 2, coords, xRank + 1);
+    const auto yOffset = shape::getOffset(yShapeInfo, coords);
 
     if(z == nullptr) { // gradient calculation
         y[yOffset] -= 1.f;
     }
     else {
-        z[shape::getOffset(0, zShapeInfo + 1, zShapeInfo + xRank + 1, coords, xRank)] = y[yOffset];
+        z[shape::getOffset(zShapeInfo, coords)] = y[yOffset];
     }
 }
 
diff --git a/libnd4j/include/ops/declarable/helpers/cuda/scatter_simple.cu b/libnd4j/include/ops/declarable/helpers/cuda/scatter_simple.cu
index f1eda6b01..37a465144 100644
--- a/libnd4j/include/ops/declarable/helpers/cuda/scatter_simple.cu
+++ b/libnd4j/include/ops/declarable/helpers/cuda/scatter_simple.cu
@@ -40,9 +40,9 @@ namespace nd4j {
                 auto tid = threadIdx.x + blockIdx.x * blockDim.x;
                 for (int i = tid; i < iLength; i += blockDim.x * gridDim.x) {
                     auto x = reinterpret_cast<X*>(vx) + xTadOffsets[i];
-                    auto idx = indices[shape::getIndexOffset(i, iShapeInfo, iLength)];
+                    auto idx = indices[shape::getIndexOffset(i, iShapeInfo)];
 
-                    x[shape::getIndexOffset(idx, xTadShape, xLength)] = u[shape::getIndexOffset(i, uShapeInfo, uLength)];
+                    x[shape::getIndexOffset(idx, xTadShape)] = u[shape::getIndexOffset(i, uShapeInfo)];
                 }
             }
 
diff --git a/libnd4j/include/ops/declarable/helpers/cuda/scatter_update.cu b/libnd4j/include/ops/declarable/helpers/cuda/scatter_update.cu
index d8b3575ff..1ad55a111 100644
--- a/libnd4j/include/ops/declarable/helpers/cuda/scatter_update.cu
+++ b/libnd4j/include/ops/declarable/helpers/cuda/scatter_update.cu
@@ -63,8 +63,8 @@ namespace nd4j {
 
                     for (Nd4jLong i = threadIdx.x; i < arrLenX; i += blockDim.x) {
 
-                        const auto xOffset = shape::getIndexOffset(i, xShapeInfo, arrLenX);
-                        const auto yOffset = shape::getIndexOffset(i, yShapeInfo, arrLenY);
+                        const auto xOffset = shape::getIndexOffset(i, xShapeInfo);
+                        const auto yOffset = shape::getIndexOffset(i, yShapeInfo);
 
                         switch (opCode) {
                             case 0:
diff --git a/libnd4j/include/ops/declarable/helpers/cuda/segment_max.cu b/libnd4j/include/ops/declarable/helpers/cuda/segment_max.cu
index 8830f37e7..cab6e50e7 100644
--- a/libnd4j/include/ops/declarable/helpers/cuda/segment_max.cu
+++ b/libnd4j/include/ops/declarable/helpers/cuda/segment_max.cu
@@ -58,10 +58,10 @@ namespace nd4j {
                     zLen = shape::length(outputShape);
 
                     if (segment < numOfClasses) {
-                        zIndex = shape::getIndexOffset(segment, outputShape, zLen);
+                        zIndex = shape::getIndexOffset(segment, outputShape);
                         start = starts[segment];
                         finish = start + lengths[segment];
-                        z[zIndex] = x[shape::getIndexOffset(start, inputShape, xLen)];
+                        z[zIndex] = x[shape::getIndexOffset(start, inputShape)];
                         val[segment] = z[zIndex];
                     }
 
@@ -69,7 +69,7 @@ namespace nd4j {
                 __syncthreads();
 
                 for (auto e = start + threadIdx.x + 1; e < finish; e += blockDim.x) {
-                    auto xIndex = shape::getIndexOffset(e, inputShape, xLen);
+                    auto xIndex = shape::getIndexOffset(e, inputShape);
                     nd4j::math::atomics::nd4j_atomicMax(&z[zIndex], x[xIndex]);
                 }
             }
@@ -94,19 +94,19 @@ namespace nd4j {
                     xLen = shape::length(inputShape);
                     zLen = shape::length(outputShape);
 
-                    zIndex = shape::getIndexOffset(segment, outputShape, zLen);
+                    zIndex = shape::getIndexOffset(segment, outputShape);
                     //start = starts[segment];
                     //finish = start + lengths[segment];
                     if (lengths[segment] > 0)
-                        z[zIndex] = x[shape::getIndexOffset(starts[segment], inputShape, xLen)];
+                        z[zIndex] = x[shape::getIndexOffset(starts[segment], inputShape)];
                     else
                         z[zIndex] = -DataTypeUtils::max<T>();
                 }
                 __syncthreads();
                 if (lengths[segment] > 0)
                     for (auto e = threadIdx.x + 1; e < xLen; e += blockDim.x) {
-                        auto xIndex = shape::getIndexOffset(e, inputShape, xLen);
-                        auto yIndex = shape::getIndexOffset(e, indicesShape, xLen);
+                        auto xIndex = shape::getIndexOffset(e, inputShape);
+                        auto yIndex = shape::getIndexOffset(e, indicesShape);
                         if (y[yIndex] == segment) {
                             nd4j::math::atomics::nd4j_atomicMax(&z[zIndex], x[xIndex]);
                         }
@@ -140,16 +140,16 @@ namespace nd4j {
                     auto x = reinterpret_cast<T *>(inputBuf) + inputTadOffsets[idx];
                     if (blockIdx.x == start) {
                         for (auto e = threadIdx.x; e < len; e += blockDim.x) {
-                            auto xIndex = shape::getIndexOffset(e, inputTads, len);
-                            auto zIndex = shape::getIndexOffset(e, outputTads, len);
+                            auto xIndex = shape::getIndexOffset(e, inputTads);
+                            auto zIndex = shape::getIndexOffset(e, outputTads);
                             nd4j::math::atomics::nd4j_atomicMax(&z[zIndex], x[xIndex]);
                             //z[zIndex] = x[xIndex];
                         }
                     }
                     else {
                         for (auto e = threadIdx.x; e < len; e += blockDim.x) {
-                            auto xIndex = shape::getIndexOffset(e, inputTads, len);
-                            auto zIndex = shape::getIndexOffset(e, outputTads, len);
+                            auto xIndex = shape::getIndexOffset(e, inputTads);
+                            auto zIndex = shape::getIndexOffset(e, outputTads);
                             if (lengths[segment])
                                 nd4j::math::atomics::nd4j_atomicMax(&z[zIndex], x[xIndex]);
                         }
@@ -276,12 +276,12 @@ namespace nd4j {
 
                 for (auto e = start; e < xLen; e += step) {
 
-                    auto zOffset = shape::getIndexOffset(e, outputShape, xLen);
-                    auto xOffset = shape::getIndexOffset(e, inputShape, xLen);
-                    auto yOffset = shape::getIndexOffset(e, indicesShape, xLen);
+                    auto zOffset = shape::getIndexOffset(e, outputShape);
+                    auto xOffset = shape::getIndexOffset(e, inputShape);
+                    auto yOffset = shape::getIndexOffset(e, indicesShape);
                     auto classIndex = y[yOffset];
-                    auto gradOffsetI = shape::getIndexOffset(classIndex, forwardShape, gradLen);
-                    auto gradOffsetO = shape::getIndexOffset(classIndex, epsShape, gradLen);
+                    auto gradOffsetI = shape::getIndexOffset(classIndex, forwardShape);
+                    auto gradOffsetO = shape::getIndexOffset(classIndex, epsShape);
 
                     if (nd4j::math::nd4j_abs(gradIn[gradOffsetI] - x[xOffset]) <= T(1.e-6)) {
                         z[zOffset] = gradOut[gradOffsetO];
@@ -318,7 +318,7 @@ namespace nd4j {
                 __syncthreads();
 
                 for (auto i = blockIdx.x; i < yLen; i += gridDim.x) {
-                    auto yIndex = shape::getIndexOffset(i, indicesShape, yLen);
+                    auto yIndex = shape::getIndexOffset(i, indicesShape);
                     auto segment = y[yIndex];
                     T* current = x + inputOffsets[i];
                     T* currentOut = z + outOffsets[i];
diff --git a/libnd4j/include/ops/declarable/helpers/cuda/segment_mean.cu b/libnd4j/include/ops/declarable/helpers/cuda/segment_mean.cu
index 19869f646..dc958f79c 100644
--- a/libnd4j/include/ops/declarable/helpers/cuda/segment_mean.cu
+++ b/libnd4j/include/ops/declarable/helpers/cuda/segment_mean.cu
@@ -53,11 +53,11 @@ namespace helpers {
 
             //[zIndex] =
             if (segment < numOfClasses) {
-                zIndex = shape::getIndexOffset(segment, outputShape, zLen);
+                zIndex = shape::getIndexOffset(segment, outputShape);
                 start = starts[segment];
                 finish = start + lengths[segment];
                 //val[segment] = ;
-                z[zIndex] = T(x[shape::getIndexOffset(start, inputShape, xLen)] / lengths[segment]);
+                z[zIndex] = T(x[shape::getIndexOffset(start, inputShape)] / lengths[segment]);
 //                val[segment] = z[zIndex];
             }
 
@@ -65,7 +65,7 @@ namespace helpers {
         __syncthreads();
 
         for (auto e = start + threadIdx.x + 1; e < finish; e += blockDim.x) {
-            auto xIndex = shape::getIndexOffset(e, inputShape, xLen);
+            auto xIndex = shape::getIndexOffset(e, inputShape);
             if (lengths[segment])
                 nd4j::math::atomics::nd4j_atomicAdd(&z[zIndex], T(x[xIndex] / lengths[segment]));
         }
@@ -91,11 +91,11 @@ namespace helpers {
             zLen = shape::length(outputShape);
 
 //            if (segment < numOfClasses) {
-            zIndex = shape::getIndexOffset(segment, outputShape, zLen);
+            zIndex = shape::getIndexOffset(segment, outputShape);
             //start = starts[segment];
             //finish = start + lengths[segment];
             if (lengths[segment] > 0)
-                z[zIndex] = T(x[shape::getIndexOffset(starts[segment], inputShape, xLen)] / T(lengths[segment]));
+                z[zIndex] = T(x[shape::getIndexOffset(starts[segment], inputShape)] / T(lengths[segment]));
             else
                 z[zIndex] = 0; //DataTypeUtils::max<T>();
 //                val[segment] = z[zIndex];
@@ -105,8 +105,8 @@ namespace helpers {
         __syncthreads();
         if (lengths[segment] > 0)
             for (auto e = threadIdx.x; e < xLen; e += blockDim.x) {
-                auto xIndex = shape::getIndexOffset(e, inputShape, xLen);
-                auto yIndex = shape::getIndexOffset(e, indicesShape, xLen);
+                auto xIndex = shape::getIndexOffset(e, inputShape);
+                auto yIndex = shape::getIndexOffset(e, indicesShape);
                 if (y[yIndex] == segment && e != starts[segment]) {
                     nd4j::math::atomics::nd4j_atomicAdd(&z[zIndex], T(x[xIndex]/T(lengths[segment])));
                 }
@@ -137,15 +137,15 @@ namespace helpers {
             auto x = reinterpret_cast<T *>(inputBuf) + inputTadOffsets[idx];
             if (blockIdx.x == start) {
                 for (auto e = threadIdx.x; e < len; e += blockDim.x) {
-                    auto xIndex = shape::getIndexOffset(e, inputTads, len);
-                    auto zIndex = shape::getIndexOffset(e, outputTads, len);
+                    auto xIndex = shape::getIndexOffset(e, inputTads);
+                    auto zIndex = shape::getIndexOffset(e, outputTads);
                     nd4j::math::atomics::nd4j_atomicAdd(&z[zIndex], T(x[xIndex]/lengths[segment]));
                 }
             }
             else {
                 for (auto e = threadIdx.x; e < len; e += blockDim.x) {
-                    auto xIndex = shape::getIndexOffset(e, inputTads, len);
-                    auto zIndex = shape::getIndexOffset(e, outputTads, len);
+                    auto xIndex = shape::getIndexOffset(e, inputTads);
+                    auto zIndex = shape::getIndexOffset(e, outputTads);
                     if (lengths[segment])
                         nd4j::math::atomics::nd4j_atomicAdd(&z[zIndex], T(x[xIndex]/lengths[segment]));
                 }
@@ -261,11 +261,11 @@ namespace helpers {
 
         for (auto e = start; e < xLen; e += step) {
 
-            auto zOffset = shape::getIndexOffset(e, outputShape, xLen);
-            auto xOffset = shape::getIndexOffset(e, inputShape, xLen);
-            auto yOffset = shape::getIndexOffset(e, indicesShape, xLen);
+            auto zOffset = shape::getIndexOffset(e, outputShape);
+            auto xOffset = shape::getIndexOffset(e, inputShape);
+            auto yOffset = shape::getIndexOffset(e, indicesShape);
             auto classIndex = y[yOffset];
-            auto gradOffsetO = shape::getIndexOffset(classIndex, epsShape, gradLen);
+            auto gradOffsetO = shape::getIndexOffset(classIndex, epsShape);
 
             z[zOffset] = T(gradOut[gradOffsetO] / float(lengths[classIndex]));
         }
@@ -294,14 +294,14 @@ namespace helpers {
         __syncthreads();
 
         for (auto i = blockIdx.x; i < yLen; i += gridDim.x) {
-//            auto yIndex = shape::getIndexOffset(i, indicesShape, yLen);
+//            auto yIndex = shape::getIndexOffset(i, indicesShape);
             auto segment = y[i]; //yIndex];
             T* currentOut = z + outOffsets[i];
             T* outGrad = gradOut + gradOutOffsets[segment];
 
             for (auto e = threadIdx.x; e < currentLen; e += blockDim.x) {
-                auto zIndex = shape::getIndexOffset(e, outTad, currentLen);
-                auto gradIndex = shape::getIndexOffset(e, gradOutTad, gradLen);
+                auto zIndex = shape::getIndexOffset(e, outTad);
+                auto gradIndex = shape::getIndexOffset(e, gradOutTad);
                 if (lengths[segment] > 0)
                     currentOut[zIndex] = T(outGrad[gradIndex] / float(lengths[segment]));
             }
diff --git a/libnd4j/include/ops/declarable/helpers/cuda/segment_min.cu b/libnd4j/include/ops/declarable/helpers/cuda/segment_min.cu
index e5ea2eb91..506cfaa41 100644
--- a/libnd4j/include/ops/declarable/helpers/cuda/segment_min.cu
+++ b/libnd4j/include/ops/declarable/helpers/cuda/segment_min.cu
@@ -56,10 +56,10 @@ namespace helpers {
             zLen = shape::length(outputShape);
 
             if (segment < numOfClasses) {
-                zIndex = shape::getIndexOffset(segment, outputShape, zLen);
+                zIndex = shape::getIndexOffset(segment, outputShape);
                 start = starts[segment];
                 finish = start + lengths[segment];
-                z[zIndex] = x[shape::getIndexOffset(start, inputShape, xLen)];
+                z[zIndex] = x[shape::getIndexOffset(start, inputShape)];
                 val[segment] = z[zIndex];
             }
 
@@ -67,7 +67,7 @@ namespace helpers {
         __syncthreads();
 
         for (auto e = start + threadIdx.x + 1; e < finish; e += blockDim.x) {
-            auto xIndex = shape::getIndexOffset(e, inputShape, xLen);
+            auto xIndex = shape::getIndexOffset(e, inputShape);
             nd4j::math::atomics::nd4j_atomicMin(&z[zIndex], x[xIndex]);
         }
 
@@ -98,9 +98,9 @@ namespace helpers {
             xLen = shape::length(inputShape);
             zLen = shape::length(outputShape);
 
-            zIndex = shape::getIndexOffset(segment, outputShape, zLen);
+            zIndex = shape::getIndexOffset(segment, outputShape);
             if (lengths[segment] > 0)
-                z[zIndex] = x[shape::getIndexOffset(starts[segment], inputShape, xLen)];
+                z[zIndex] = x[shape::getIndexOffset(starts[segment], inputShape)];
             else
                 z[zIndex] = DataTypeUtils::max<T>();
 
@@ -108,8 +108,8 @@ namespace helpers {
         __syncthreads();
         if (lengths[segment] > 0)
             for (auto e = threadIdx.x + 1; e < xLen; e += blockDim.x) {
-                auto xIndex = shape::getIndexOffset(e, inputShape, xLen);
-                auto yIndex = shape::getIndexOffset(e, indicesShape, xLen);
+                auto xIndex = shape::getIndexOffset(e, inputShape);
+                auto yIndex = shape::getIndexOffset(e, indicesShape);
                 if (y[yIndex] == segment) {
                     nd4j::math::atomics::nd4j_atomicMin(&z[zIndex], x[xIndex]);
                 }
@@ -140,15 +140,15 @@ namespace helpers {
             auto x = reinterpret_cast<T *>(inputBuf) + inputTadOffsets[idx];
             if (blockIdx.x == start) {
                 for (auto e = threadIdx.x; e < len; e += blockDim.x) {
-                    auto xIndex = shape::getIndexOffset(e, inputTads, len);
-                    auto zIndex = shape::getIndexOffset(e, outputTads, len);
+                    auto xIndex = shape::getIndexOffset(e, inputTads);
+                    auto zIndex = shape::getIndexOffset(e, outputTads);
                     nd4j::math::atomics::nd4j_atomicMin(&z[zIndex], x[xIndex]);
                 }
             }
             else {
                 for (auto e = threadIdx.x; e < len; e += blockDim.x) {
-                    auto xIndex = shape::getIndexOffset(e, inputTads, len);
-                    auto zIndex = shape::getIndexOffset(e, outputTads, len);
+                    auto xIndex = shape::getIndexOffset(e, inputTads);
+                    auto zIndex = shape::getIndexOffset(e, outputTads);
 //                    if (lengths[indices[idx]])
                         nd4j::math::atomics::nd4j_atomicMin(&z[zIndex], x[xIndex]);
                 }
@@ -269,12 +269,12 @@ namespace helpers {
 
         for (auto e = start; e < xLen; e += step) {
 
-            auto zOffset = shape::getIndexOffset(e, outputShape, xLen);
-            auto xOffset = shape::getIndexOffset(e, inputShape, xLen);
-            auto yOffset = shape::getIndexOffset(e, indicesShape, xLen);
+            auto zOffset = shape::getIndexOffset(e, outputShape);
+            auto xOffset = shape::getIndexOffset(e, inputShape);
+            auto yOffset = shape::getIndexOffset(e, indicesShape);
             auto classIndex = y[yOffset];
-            auto gradOffsetI = shape::getIndexOffset(classIndex, forwardShape, gradLen);
-            auto gradOffsetO = shape::getIndexOffset(classIndex, epsShape, gradLen);
+            auto gradOffsetI = shape::getIndexOffset(classIndex, forwardShape);
+            auto gradOffsetO = shape::getIndexOffset(classIndex, epsShape);
 
             if (nd4j::math::nd4j_abs(gradIn[gradOffsetI] - x[xOffset]) <= T(1.e-6)) {
                 z[zOffset] = gradOut[gradOffsetO];
@@ -311,7 +311,7 @@ namespace helpers {
         __syncthreads();
 
         for (auto i = blockIdx.x; i < yLen; i += gridDim.x) {
-            auto yIndex = shape::getIndexOffset(i, indicesShape, yLen);
+            auto yIndex = shape::getIndexOffset(i, indicesShape);
             auto segment = y[yIndex];
             T* current = x + inputOffsets[i];
             T* currentOut = z + outOffsets[i];
diff --git a/libnd4j/include/ops/declarable/helpers/cuda/segment_prod.cu b/libnd4j/include/ops/declarable/helpers/cuda/segment_prod.cu
index 5709a63ea..7814defe1 100644
--- a/libnd4j/include/ops/declarable/helpers/cuda/segment_prod.cu
+++ b/libnd4j/include/ops/declarable/helpers/cuda/segment_prod.cu
@@ -53,11 +53,11 @@ namespace helpers {
             zLen = shape::length(outputShape);
 
             if (segment < numOfClasses) {
-                zIndex = shape::getIndexOffset(segment, outputShape, zLen);
+                zIndex = shape::getIndexOffset(segment, outputShape);
                 start = starts[segment];
                 finish = start + lengths[segment];
                 //val[segment] = ;
-                z[zIndex] = x[shape::getIndexOffset(start, inputShape, xLen)];
+                z[zIndex] = x[shape::getIndexOffset(start, inputShape)];
                 val[segment] = z[zIndex];
             }
 
@@ -67,7 +67,7 @@ namespace helpers {
 //         auto step = blockDim.x * gridDim.x;
 
         for (auto e = start + threadIdx.x + 1; e < finish; e += blockDim.x) {
-            auto xIndex = shape::getIndexOffset(e, inputShape, xLen);
+            auto xIndex = shape::getIndexOffset(e, inputShape);
             nd4j::math::atomics::nd4j_atomicMul(&val[segment], x[xIndex]);
         }
         __syncthreads();
@@ -98,11 +98,11 @@ namespace helpers {
             zLen = shape::length(outputShape);
 
 //            if (segment < numOfClasses) {
-            zIndex = shape::getIndexOffset(segment, outputShape, zLen);
+            zIndex = shape::getIndexOffset(segment, outputShape);
             //start = starts[segment];
             //finish = start + lengths[segment];
             if (lengths[segment] > 0)
-                z[zIndex] = x[shape::getIndexOffset(starts[segment], inputShape, xLen)];
+                z[zIndex] = x[shape::getIndexOffset(starts[segment], inputShape)];
             else
                 z[zIndex] = 0; //DataTypeUtils::max<T>();
 //                val[segment] = z[zIndex];
@@ -112,8 +112,8 @@ namespace helpers {
         __syncthreads();
         if (lengths[segment] > 0)
             for (auto e = threadIdx.x; e < xLen; e += blockDim.x) {
-                auto xIndex = shape::getIndexOffset(e, inputShape, xLen);
-                auto yIndex = shape::getIndexOffset(e, indicesShape, xLen);
+                auto xIndex = shape::getIndexOffset(e, inputShape);
+                auto yIndex = shape::getIndexOffset(e, indicesShape);
                 if (y[yIndex] == segment && e != starts[segment]) {
                     nd4j::math::atomics::nd4j_atomicMul(&z[zIndex], x[xIndex]);
                 }
@@ -144,15 +144,15 @@ namespace helpers {
             auto x = reinterpret_cast<T *>(inputBuf) + inputTadOffsets[idx];
             if (blockIdx.x == start) {
                 for (auto e = threadIdx.x; e < len; e += blockDim.x) {
-                    auto xIndex = shape::getIndexOffset(e, inputTads, len);
-                    auto zIndex = shape::getIndexOffset(e, outputTads, len);
+                    auto xIndex = shape::getIndexOffset(e, inputTads);
+                    auto zIndex = shape::getIndexOffset(e, outputTads);
                     nd4j::math::atomics::nd4j_atomicMul(&z[zIndex], x[xIndex]);
                 }
             }
             else {
                 for (auto e = threadIdx.x; e < len; e += blockDim.x) {
-                    auto xIndex = shape::getIndexOffset(e, inputTads, len);
-                    auto zIndex = shape::getIndexOffset(e, outputTads, len);
+                    auto xIndex = shape::getIndexOffset(e, inputTads);
+                    auto zIndex = shape::getIndexOffset(e, outputTads);
                     if (lengths[segment] > 0)
                         nd4j::math::atomics::nd4j_atomicMul(&z[zIndex], x[xIndex]);
                 }
@@ -268,12 +268,12 @@ namespace helpers {
 
         for (auto e = start; e < xLen; e += step) {
 
-            auto zOffset = shape::getIndexOffset(e, outputShape, xLen);
-            auto xOffset = shape::getIndexOffset(e, inputShape, xLen);
-            auto yOffset = shape::getIndexOffset(e, indicesShape, xLen);
+            auto zOffset = shape::getIndexOffset(e, outputShape);
+            auto xOffset = shape::getIndexOffset(e, inputShape);
+            auto yOffset = shape::getIndexOffset(e, indicesShape);
             auto classIndex = y[yOffset];
-            auto gradOffsetI = shape::getIndexOffset(classIndex, forwardShape, gradLen);
-            auto gradOffsetO = shape::getIndexOffset(classIndex, epsShape, gradLen);
+            auto gradOffsetI = shape::getIndexOffset(classIndex, forwardShape);
+            auto gradOffsetO = shape::getIndexOffset(classIndex, epsShape);
 
             z[zOffset] = gradOut[gradOffsetO]  * gradIn[gradOffsetI] / x[xOffset];
         }
@@ -307,7 +307,7 @@ namespace helpers {
         __syncthreads();
 
         for (auto i = blockIdx.x; i < yLen; i += gridDim.x) {
-            auto yIndex = shape::getIndexOffset(i, indicesShape, yLen);
+            auto yIndex = shape::getIndexOffset(i, indicesShape);
             auto segment = y[yIndex];
             T* current = x + inputOffsets[i];
             T* currentOut = z + outOffsets[i];
diff --git a/libnd4j/include/ops/declarable/helpers/cuda/segment_sqrtn.cu b/libnd4j/include/ops/declarable/helpers/cuda/segment_sqrtn.cu
index 229d41cc9..f4237ac44 100644
--- a/libnd4j/include/ops/declarable/helpers/cuda/segment_sqrtn.cu
+++ b/libnd4j/include/ops/declarable/helpers/cuda/segment_sqrtn.cu
@@ -51,11 +51,11 @@ namespace helpers {
             zLen = shape::length(outputShape);
 
 //            if (segment < numOfClasses) {
-            zIndex = shape::getIndexOffset(segment, outputShape, zLen);
+            zIndex = shape::getIndexOffset(segment, outputShape);
             //start = starts[segment];
             //finish = start + lengths[segment];
             if (lengths[segment] > 0)
-                z[zIndex] = x[shape::getIndexOffset(starts[segment], inputShape, xLen)] / nd4j::math::nd4j_sqrt<int, T>(lengths[segment]);
+                z[zIndex] = x[shape::getIndexOffset(starts[segment], inputShape)] / nd4j::math::nd4j_sqrt<int, T>(lengths[segment]);
             else
                 z[zIndex] = 0; //DataTypeUtils::max<T>();
 //                val[segment] = z[zIndex];
@@ -65,8 +65,8 @@ namespace helpers {
         __syncthreads();
         if (lengths[segment] > 0)
             for (auto e = threadIdx.x + 1; e < xLen; e += blockDim.x) {
-                auto xIndex = shape::getIndexOffset(e, inputShape, xLen);
-                auto yIndex = shape::getIndexOffset(e, indicesShape, xLen);
+                auto xIndex = shape::getIndexOffset(e, inputShape);
+                auto yIndex = shape::getIndexOffset(e, indicesShape);
                 if (y[yIndex] == segment && e != starts[segment]) {
                     nd4j::math::atomics::nd4j_atomicAdd(&z[zIndex], x[xIndex] / nd4j::math::nd4j_sqrt<int, T>(lengths[segment]));
                 }
@@ -97,15 +97,15 @@ namespace helpers {
             auto x = reinterpret_cast<T *>(inputBuf) + inputTadOffsets[idx];
             if (blockIdx.x == start) {
                 for (auto e = threadIdx.x; e < len; e += blockDim.x) {
-                    auto xIndex = shape::getIndexOffset(e, inputTads, len);
-                    auto zIndex = shape::getIndexOffset(e, outputTads, len);
+                    auto xIndex = shape::getIndexOffset(e, inputTads);
+                    auto zIndex = shape::getIndexOffset(e, outputTads);
                     z[zIndex] = x[xIndex] / nd4j::math::nd4j_sqrt<int, T>(lengths[segment]);
                 }
             }
             else {
                 for (auto e = threadIdx.x; e < len; e += blockDim.x) {
-                    auto xIndex = shape::getIndexOffset(e, inputTads, len);
-                    auto zIndex = shape::getIndexOffset(e, outputTads, len);
+                    auto xIndex = shape::getIndexOffset(e, inputTads);
+                    auto zIndex = shape::getIndexOffset(e, outputTads);
                     nd4j::math::atomics::nd4j_atomicAdd(&z[zIndex], x[xIndex] / nd4j::math::nd4j_sqrt<int, T>(lengths[segment]));
                 }
             }
@@ -177,11 +177,11 @@ namespace helpers {
 
         for (auto e = start; e < xLen; e += step) {
 
-            auto zOffset = shape::getIndexOffset(e, outputShape, xLen);
-            auto xOffset = shape::getIndexOffset(e, inputShape, xLen);
-            auto yOffset = shape::getIndexOffset(e, indicesShape, xLen);
+            auto zOffset = shape::getIndexOffset(e, outputShape);
+            auto xOffset = shape::getIndexOffset(e, inputShape);
+            auto yOffset = shape::getIndexOffset(e, indicesShape);
             auto classIndex = y[yOffset];
-            auto gradOffsetO = shape::getIndexOffset(classIndex, epsShape, gradLen);
+            auto gradOffsetO = shape::getIndexOffset(classIndex, epsShape);
 
             z[zOffset] = T(gradOut[gradOffsetO] / math::nd4j_sqrt<int, float>(lengths[classIndex]));
         }
@@ -211,14 +211,14 @@ namespace helpers {
         __syncthreads();
 
         for (auto i = blockIdx.x; i < yLen; i += gridDim.x) {
-//            auto yIndex = shape::getIndexOffset(i, indicesShape, yLen);
+//            auto yIndex = shape::getIndexOffset(i, indicesShape);
             auto segment = y[i]; //yIndex];
             T* currentOut = z + outOffsets[i];
             T* outGrad = gradOut + gradOutOffsets[segment];
 
             for (auto e = threadIdx.x; e < currentLen; e += blockDim.x) {
-                auto zIndex = shape::getIndexOffset(e, outTad, currentLen);
-                auto gradIndex = shape::getIndexOffset(e, gradOutTad, gradLen);
+                auto zIndex = shape::getIndexOffset(e, outTad);
+                auto gradIndex = shape::getIndexOffset(e, gradOutTad);
                 if (lengths[segment] > 0)
                     currentOut[zIndex] = T(outGrad[gradIndex] / math::nd4j_sqrt<int, float>(lengths[segment]));
             }
diff --git a/libnd4j/include/ops/declarable/helpers/cuda/segment_sum.cu b/libnd4j/include/ops/declarable/helpers/cuda/segment_sum.cu
index 4b8976f4e..cf4ddd942 100644
--- a/libnd4j/include/ops/declarable/helpers/cuda/segment_sum.cu
+++ b/libnd4j/include/ops/declarable/helpers/cuda/segment_sum.cu
@@ -58,18 +58,18 @@ namespace helpers {
 
 
             if (segment < numOfClasses) {
-                zIndex = shape::getIndexOffset(segment, outputShape, zLen);
+                zIndex = shape::getIndexOffset(segment, outputShape);
                 start = starts[segment];
                 finish = start + lengths[segment];
                 //val[segment] = ;
-                z[zIndex] = x[shape::getIndexOffset(start, inputShape, xLen)];
+                z[zIndex] = x[shape::getIndexOffset(start, inputShape)];
             }
 
         }
         __syncthreads();
 
         for (auto e = start + threadIdx.x + 1; e < finish; e += blockDim.x) {
-            auto xIndex = shape::getIndexOffset(e, inputShape, xLen);
+            auto xIndex = shape::getIndexOffset(e, inputShape);
             nd4j::math::atomics::nd4j_atomicAdd(&z[zIndex], x[xIndex]);
         }
     }
@@ -99,9 +99,9 @@ namespace helpers {
             xLen = shape::length(inputShape);
             zLen = shape::length(outputShape);
 
-            zIndex = shape::getIndexOffset(segment, outputShape, zLen);
+            zIndex = shape::getIndexOffset(segment, outputShape);
             if (lengths[segment] > 0)
-                z[zIndex] = x[shape::getIndexOffset(starts[segment], inputShape, xLen)];
+                z[zIndex] = x[shape::getIndexOffset(starts[segment], inputShape)];
             else
                 z[zIndex] = 0; //DataTypeUtils::max<T>();
         }
@@ -109,8 +109,8 @@ namespace helpers {
 
         if (lengths[segment] > 0)
             for (auto e = threadIdx.x; e < xLen; e += blockDim.x) {
-                auto xIndex = shape::getIndexOffset(e, inputShape, xLen);
-                auto yIndex = shape::getIndexOffset(e, indicesShape, xLen);
+                auto xIndex = shape::getIndexOffset(e, inputShape);
+                auto yIndex = shape::getIndexOffset(e, indicesShape);
                 if (y[yIndex] == segment && e != starts[segment]) {
                     nd4j::math::atomics::nd4j_atomicAdd(&z[zIndex], x[xIndex]);
                 }
@@ -141,15 +141,15 @@ namespace helpers {
             auto x = reinterpret_cast<T *>(inputBuf) + inputTadOffsets[idx];
             if (blockIdx.x == start) {
                 for (auto e = threadIdx.x; e < len; e += blockDim.x) {
-                    auto xIndex = shape::getIndexOffset(e, inputTads, len);
-                    auto zIndex = shape::getIndexOffset(e, outputTads, len);
+                    auto xIndex = shape::getIndexOffset(e, inputTads);
+                    auto zIndex = shape::getIndexOffset(e, outputTads);
                     nd4j::math::atomics::nd4j_atomicAdd(&z[zIndex], x[xIndex]);
                 }
             }
             else {
                 for (auto e = threadIdx.x; e < len; e += blockDim.x) {
-                    auto xIndex = shape::getIndexOffset(e, inputTads, len);
-                    auto zIndex = shape::getIndexOffset(e, outputTads, len);
+                    auto xIndex = shape::getIndexOffset(e, inputTads);
+                    auto zIndex = shape::getIndexOffset(e, outputTads);
                     if (lengths[indices[idx]])
                         nd4j::math::atomics::nd4j_atomicAdd(&z[zIndex], x[xIndex]);
                 }
@@ -269,11 +269,11 @@ namespace helpers {
 
         for (auto e = start; e < xLen; e += step) {
 
-            auto zOffset = shape::getIndexOffset(e, outputShape, xLen);
-            auto xOffset = shape::getIndexOffset(e, inputShape, xLen);
-            auto yOffset = shape::getIndexOffset(e, indicesShape, xLen);
+            auto zOffset = shape::getIndexOffset(e, outputShape);
+            auto xOffset = shape::getIndexOffset(e, inputShape);
+            auto yOffset = shape::getIndexOffset(e, indicesShape);
             auto classIndex = y[yOffset];
-            auto gradOffsetO = shape::getIndexOffset(classIndex, epsShape, gradLen);
+            auto gradOffsetO = shape::getIndexOffset(classIndex, epsShape);
 
             z[zOffset] = gradOut[gradOffsetO];
         }
@@ -302,7 +302,7 @@ namespace helpers {
         __syncthreads();
 
         for (auto i = blockIdx.x; i < yLen; i += gridDim.x) {
-            auto yIndex = shape::getIndexOffset(i, indicesShape, yLen);
+            auto yIndex = shape::getIndexOffset(i, indicesShape);
             auto segment = y[yIndex];
             T* currentOut = z + outOffsets[i];
             T* outGrad = gradOut + gradOutOffsets[segment];
diff --git a/libnd4j/include/ops/declarable/helpers/cuda/sequence_mask.cu b/libnd4j/include/ops/declarable/helpers/cuda/sequence_mask.cu
index 7318dbaea..c07db1b95 100644
--- a/libnd4j/include/ops/declarable/helpers/cuda/sequence_mask.cu
+++ b/libnd4j/include/ops/declarable/helpers/cuda/sequence_mask.cu
@@ -40,8 +40,8 @@ namespace helpers {
 
         for (auto i = blockIdx.x; i < maxIndex; i += gridDim.x)
             for(auto k = threadIdx.x; k < inputLen; k += blockDim.x)
-                if (i < input[shape::getIndexOffset(k, inputShape, inputLen)])
-                    output[shape::getIndexOffset(k * maxIndex + i, outputShape, outputLen)] = B(true);
+                if (i < input[shape::getIndexOffset(k, inputShape)])
+                    output[shape::getIndexOffset(k * maxIndex + i, outputShape)] = B(true);
 
     }
 
diff --git a/libnd4j/include/ops/declarable/helpers/cuda/sru.cu b/libnd4j/include/ops/declarable/helpers/cuda/sru.cu
index 5c00244f8..5ce883a59 100644
--- a/libnd4j/include/ops/declarable/helpers/cuda/sru.cu
+++ b/libnd4j/include/ops/declarable/helpers/cuda/sru.cu
@@ -157,11 +157,11 @@ __global__ static void sruBICuda(const void* vx,    const Nd4jLong* xShapeInfo,
     if(tid >= len)
         return;
 
-    shape::index2coords(rank, xShapeInfo + 2, tid, len, coords + 1);    // loop through last two dimensions of x : {bS, 2*K}
+    shape::index2coords(tid, rank - 1, xShapeInfo + 2, coords + 1);    // loop through last two dimensions of x : {bS, 2*K}
 
-    const auto maskOffst = mask ? shape::getOffset(0, maskShapeInfo + 1, maskShapeInfo + rank, coords + 1, rank - 1) : 0;
-    const auto c0Offset  = shape::getOffset(0, c0ShapeInfo + 1, c0ShapeInfo + rank, coords + 1, rank - 1);
-    const auto bFOffset  = shape::getOffset(0, bShapeInfo + 1, bShapeInfo + rank - 1, coords + 2, rank - 2);
+    const auto maskOffst = mask ? shape::getOffset(maskShapeInfo, coords + 1) : 0;
+    const auto c0Offset  = shape::getOffset(c0ShapeInfo, coords + 1);
+    const auto bFOffset  = shape::getOffset(bShapeInfo, coords + 2);
     const auto bROffset  = bFOffset + 2 * K * bShapeInfo[2];    // 2*K*b_stride
 
     const T maskVal = mask ? mask[maskOffst] : static_cast<T>(1);
@@ -176,12 +176,12 @@ __global__ static void sruBICuda(const void* vx,    const Nd4jLong* xShapeInfo,
     else
         coords[0] = 0;
 
-    auto xOffset  = shape::getOffset(0, xShapeInfo + 1, xShapeInfo + rank + 1, coords, rank);
-    auto htOffset = shape::getOffset(0, htShapeInfo + 1, htShapeInfo + rank + 1, coords, rank);
-    auto ctOffset = shape::getOffset(0, ctShapeInfo + 1, ctShapeInfo + rank + 1, coords, rank);
+    auto xOffset  = shape::getOffset(xShapeInfo, coords);
+    auto htOffset = shape::getOffset(htShapeInfo, coords);
+    auto ctOffset = shape::getOffset(ctShapeInfo, coords);
 
     coords[2] *= 3;
-    auto wiOffset0 = shape::getOffset(0, wiShapeInfo + 1, wiShapeInfo + rank + 1, coords, rank);
+    auto wiOffset0 = shape::getOffset(wiShapeInfo, coords);
     auto wiOffset1 = wiOffset0 + wiShapeInfo[rank + 3];   // add last stride
     auto wiOffset2 = wiOffset1 + wiShapeInfo[rank + 3];   // add last stride
 
@@ -363,15 +363,15 @@ __global__ static void sruBIBPCuda(const void* vx,       const Nd4jLong* xShapeI
     if(tid >= len)
         return;
 
-    shape::index2coords(rank, xShapeInfo + 2, tid, len, coords + 1);    // loop through last two dimensions of x : {bS, 2*K}
+    shape::index2coords(tid, rank - 1, xShapeInfo + 2, coords + 1);    // loop through last two dimensions of x : {bS, 2*K}
 
-    const auto maskOffst    = mask ? shape::getOffset(0, maskShapeInfo + 1, maskShapeInfo + rank, coords + 1, rank - 1) : 0;
-    const auto c0Offset     = shape::getOffset(0, c0ShapeInfo + 1, c0ShapeInfo + rank, coords + 1, rank - 1);
-    const auto gradCtOffset = shape::getOffset(0, gradCtShapeInfo + 1, gradCtShapeInfo + rank, coords + 1, rank - 1);
-    const auto gradC0Offset = shape::getOffset(0, gradC0ShapeInfo + 1, gradC0ShapeInfo + rank, coords + 1, rank - 1);
-    const auto bFOffset     = shape::getOffset(0, bShapeInfo + 1, bShapeInfo + rank - 1, coords + 2, rank - 2);
+    const auto maskOffst    = mask ? shape::getOffset(maskShapeInfo, coords + 1) : 0;
+    const auto c0Offset     = shape::getOffset(c0ShapeInfo, coords + 1);
+    const auto gradCtOffset = shape::getOffset(gradCtShapeInfo, coords + 1);
+    const auto gradC0Offset = shape::getOffset(gradC0ShapeInfo, coords + 1);
+    const auto bFOffset     = shape::getOffset(bShapeInfo, coords + 2);
     const auto bROffset     = bFOffset + 2 * K * bShapeInfo[2];         // 2*K*b_stride
-    // const auto gradBFOffset = shape::getOffset(0, gradBShapeInfo + 1, gradBShapeInfo + rank, coords + 1, rank - 1);
+    // const auto gradBFOffset = shape::getOffset(gradBShapeInfo, coords + 1);
     const auto gradBFOffset = coords[1] * gradBShapeInfo[3] / 2 + coords[2] * gradBShapeInfo[4];
     const auto gradBROffset = gradBFOffset + gradBShapeInfo[3];
 
@@ -382,16 +382,16 @@ __global__ static void sruBIBPCuda(const void* vx,       const Nd4jLong* xShapeI
     else
         coords[0] = time - 1;
 
-    auto xOffset      = shape::getOffset(0, xShapeInfo + 1, xShapeInfo + rank + 1, coords, rank);
-    auto ctOffset     = shape::getOffset(0, ctShapeInfo + 1, ctShapeInfo + rank + 1, coords, rank);
-    auto gradIOffset  = shape::getOffset(0, gradIShapeInfo + 1, gradIShapeInfo + rank + 1, coords, rank);
-    auto gradHtOffset = shape::getOffset(0, gradHtShapeInfo + 1, gradHtShapeInfo + rank + 1, coords, rank);
+    auto xOffset      = shape::getOffset(xShapeInfo, coords);
+    auto ctOffset     = shape::getOffset(ctShapeInfo, coords);
+    auto gradIOffset  = shape::getOffset(gradIShapeInfo, coords);
+    auto gradHtOffset = shape::getOffset(gradHtShapeInfo, coords);
 
     coords[2] *= 3;
-    auto gradWiOffset0 = shape::getOffset(0, gradWiShapeInfo + 1, gradWiShapeInfo + rank + 1, coords, rank);
+    auto gradWiOffset0 = shape::getOffset(gradWiShapeInfo, coords);
     auto gradWiOffset1 = gradWiOffset0 + gradWiShapeInfo[rank + 3];   // add last stride
     auto gradWiOffset2 = gradWiOffset1 + gradWiShapeInfo[rank + 3];   // add last stride
-    auto wiOffset0     = shape::getOffset(0, wiShapeInfo + 1, wiShapeInfo + rank + 1, coords, rank);
+    auto wiOffset0     = shape::getOffset(wiShapeInfo, coords);
     auto wiOffset1     = wiOffset0 + wiShapeInfo[rank + 3];   // add last stride
     auto wiOffset2     = wiOffset1 + wiShapeInfo[rank + 3];   // add last stride
 
diff --git a/libnd4j/include/ops/declarable/helpers/cuda/stack.cu b/libnd4j/include/ops/declarable/helpers/cuda/stack.cu
index e492baf8e..e88f5ade8 100644
--- a/libnd4j/include/ops/declarable/helpers/cuda/stack.cu
+++ b/libnd4j/include/ops/declarable/helpers/cuda/stack.cu
@@ -39,7 +39,7 @@ namespace helpers {
 		if(tadShape == nullptr) {	// scalar case
 
 			for (Nd4jLong i = blockIdx.x * blockDim.x + threadIdx.x; i < inputListLength; i += gridDim.x * blockDim.x)
-				z[shape::getIndexOffset(i, zShapeInfo, inputListLength)] = reinterpret_cast<T*>(inputList[i])[0];
+				z[shape::getIndexOffset(i, zShapeInfo)] = reinterpret_cast<T*>(inputList[i])[0];
 		}
 		else {
 
@@ -50,7 +50,7 @@ namespace helpers {
 			    auto xShapeInfo = reinterpret_cast<Nd4jLong*>(inputShapeList[t]);
 
 			    for (int e = threadIdx.x; e < arrLen; e += blockDim.x)
-			        tZ[shape::getIndexOffset(e, tadShape, arrLen)] = tX[shape::getIndexOffset(e, xShapeInfo, arrLen)];
+			        tZ[shape::getIndexOffset(e, tadShape)] = tX[shape::getIndexOffset(e, xShapeInfo)];
 			}
 		}
 	}
diff --git a/libnd4j/include/ops/declarable/helpers/cuda/svd.cu b/libnd4j/include/ops/declarable/helpers/cuda/svd.cu
index 0695119da..b39ebf81b 100644
--- a/libnd4j/include/ops/declarable/helpers/cuda/svd.cu
+++ b/libnd4j/include/ops/declarable/helpers/cuda/svd.cu
@@ -65,12 +65,12 @@ __global__ static void inverseColumnSignCuda(void* vu, const Nd4jLong* uShapeInf
     // u
     for (Nd4jLong i = ind; i < uLen; i += gridDim.x * blockDim.x) {
 
-        shape::index2coords(rank, uShapeInfo + 1, i, uLen, coords);
+        shape::index2coords(i, uShapeInfo, coords);
 
         if(coords[rank - 1] == 0 || coords[rank - 1] == uLastButOneColumn)   // do not change sign in first and last but one columns
             continue;
 
-        const auto uOffset = shape::getOffset(0, uShapeInfo + 1, uShapeInfo + rank + 1, coords, rank);
+        const auto uOffset = shape::getOffset(uShapeInfo, coords);
 
         u[uOffset] = -u[uOffset];
     }
@@ -78,12 +78,12 @@ __global__ static void inverseColumnSignCuda(void* vu, const Nd4jLong* uShapeInf
     // v
     for (Nd4jLong i = ind; i < vLen; i += gridDim.x * blockDim.x) {
 
-        shape::index2coords(rank, vShapeInfo + 1, i, vLen, coords);
+        shape::index2coords(i, vShapeInfo, coords);
 
         if(coords[rank - 2] == 0 || coords[rank - 2] == vLastButOneColumn)   // do not change sign in first and last but one columns
             continue;
 
-        const auto vOffset = shape::getOffset(0, vShapeInfo + 1, vShapeInfo + rank + 1, coords, rank);
+        const auto vOffset = shape::getOffset(vShapeInfo, coords);
 
         v[vOffset] = -v[vOffset];
     }
diff --git a/libnd4j/include/ops/declarable/helpers/cuda/top_k.cu b/libnd4j/include/ops/declarable/helpers/cuda/top_k.cu
index db6213dd3..972013835 100644
--- a/libnd4j/include/ops/declarable/helpers/cuda/top_k.cu
+++ b/libnd4j/include/ops/declarable/helpers/cuda/top_k.cu
@@ -50,15 +50,15 @@ __global__ static void inTopKCuda(const void* vx, const Nd4jLong* xShapeInfo,
         xTadLen = shape::length(xTadShapeInfo);
 
         xTad = reinterpret_cast<const X*>(vx) + xTadOffsets[blockIdx.x];
-        idx = y[shape::getIndexOffset(blockIdx.x, yShapeInfo, shape::length(yShapeInfo))]; // shape::length(yShapeInfo) == numTads
-        elemToCompare = xTad[shape::getIndexOffset(idx, xTadShapeInfo, xTadLen)];
+        idx = y[shape::getIndexOffset(blockIdx.x, yShapeInfo)]; // shape::length(yShapeInfo) == numTads
+        elemToCompare = xTad[shape::getIndexOffset(idx, xTadShapeInfo)];
     }
 
     __syncthreads();
 
     sharedMem[threadIdx.x] = 0;
     for (Nd4jLong i = threadIdx.x; i < xTadLen; i += blockDim.x)
-        if(elemToCompare < xTad[shape::getIndexOffset(i, xTadShapeInfo, xTadLen)])
+        if(elemToCompare < xTad[shape::getIndexOffset(i, xTadShapeInfo)])
             ++sharedMem[threadIdx.x];
 
     __syncthreads();
@@ -71,7 +71,7 @@ __global__ static void inTopKCuda(const void* vx, const Nd4jLong* xShapeInfo,
     }
 
     if (threadIdx.x == 0)
-        z[shape::getIndexOffset(blockIdx.x, zShapeInfo, shape::length(zShapeInfo))] = *sharedMem < k;
+        z[shape::getIndexOffset(blockIdx.x, zShapeInfo)] = *sharedMem < k;
 }
 
 ///////////////////////////////////////////////////////////////////
@@ -117,9 +117,9 @@ int inTopKFunctor(nd4j::LaunchContext * context, const NDArray* predictions, con
             auto z = reinterpret_cast<X*>(vz) + zTadOffsets[t];
 
             for (int e = threadIdx.x; e < k; e += blockDim.x) {
-                auto idx = i[shape::getIndexOffset(e, iTadShapeInfo, k)];
+                auto idx = i[shape::getIndexOffset(e, iTadShapeInfo)];
 
-                z[shape::getIndexOffset(e, zTadShapeInfo, k)] = x[shape::getIndexOffset(idx, xTadShapeInfo, tadLength)];
+                z[shape::getIndexOffset(e, zTadShapeInfo)] = x[shape::getIndexOffset(idx, xTadShapeInfo)];
             }
         }
     }
@@ -153,7 +153,7 @@ int inTopKFunctor(nd4j::LaunchContext * context, const NDArray* predictions, con
 
                 // local max values/indices
                 for (int e = threadIdx.x; e < tadLength; e++) {
-                    auto value = x[shape::getIndexOffset(e, xTadShapeInfo, tadLength)];
+                    auto value = x[shape::getIndexOffset(e, xTadShapeInfo)];
 
                     // we'll compare this value to current stored ones
                     for (int f = 0; f < scanWidth; f++) {
@@ -180,8 +180,8 @@ int inTopKFunctor(nd4j::LaunchContext * context, const NDArray* predictions, con
                 // at this point we know local minimum for next iteration
                 if (threadIdx.x == 0) {
                     localMaximum = tempValues[scanWidth - 1];
-                    z[shape::getIndexOffset(p, zTadShapeInfo, k)] = tempValues[scanWidth - 1];
-                    i[shape::getIndexOffset(p, iTadShapeInfo, k)] = tempIndices[scanWidth - 1];
+                    z[shape::getIndexOffset(p, zTadShapeInfo)] = tempValues[scanWidth - 1];
+                    i[shape::getIndexOffset(p, iTadShapeInfo)] = tempIndices[scanWidth - 1];
                 }
                 __syncthreads();
             }
@@ -194,8 +194,8 @@ int inTopKFunctor(nd4j::LaunchContext * context, const NDArray* predictions, con
                         for (int tid = threadIdx.x; tid < k; tid += blockDim.x) {
                             auto top = 2 * tid + 1;
                             if (top < k) {
-                                auto t0 = shape::getIndexOffset(top - 1, iTadShapeInfo, k);
-                                auto t1 = shape::getIndexOffset(top, iTadShapeInfo, k);
+                                auto t0 = shape::getIndexOffset(top - 1, iTadShapeInfo);
+                                auto t1 = shape::getIndexOffset(top, iTadShapeInfo);
 
                                 if (i[t0] > i[t1]) {
                                     // swap indices first
@@ -215,8 +215,8 @@ int inTopKFunctor(nd4j::LaunchContext * context, const NDArray* predictions, con
                         for (int tid = threadIdx.x; tid < k; tid += blockDim.x) {
                             auto top = 2 * tid + 2;
                             if (top < k) {
-                                auto t0 = shape::getIndexOffset(top - 1, iTadShapeInfo, k);
-                                auto t1 = shape::getIndexOffset(top, iTadShapeInfo, k);
+                                auto t0 = shape::getIndexOffset(top - 1, iTadShapeInfo);
+                                auto t1 = shape::getIndexOffset(top, iTadShapeInfo);
 
                                 if (i[t0] > i[t1]) {
                                     // swap indices first
diff --git a/libnd4j/include/ops/declarable/helpers/cuda/transforms.cu b/libnd4j/include/ops/declarable/helpers/cuda/transforms.cu
index c3e4f497e..0a707ffb3 100644
--- a/libnd4j/include/ops/declarable/helpers/cuda/transforms.cu
+++ b/libnd4j/include/ops/declarable/helpers/cuda/transforms.cu
@@ -54,9 +54,9 @@ __global__ static void invertPermutationCuda(const void* vx, const Nd4jLong* xSh
 
     for (Nd4jLong i = tid; i < len; i += totalThreads) {
 
-        const auto xOffset = shape::getIndexOffset(i, xShapeInfo, len);
+        const auto xOffset = shape::getIndexOffset(i, xShapeInfo);
         const Nd4jLong index = x[xOffset];
-        const auto zOffset = shape::getIndexOffset(index, zShapeInfo, len);
+        const auto zOffset = shape::getIndexOffset(index, zShapeInfo);
         z[zOffset] = i;
     }
 }
@@ -112,15 +112,15 @@ __global__ static void traceCuda(const void* vx, const Nd4jLong* xShapeInfo, voi
 
     for (uint m = blockIdx.x; m < zLen; m += gridDim.x) {   // one block per each element of z, that is per each matrix
 
-        shape::index2coords(zRank, shape::shapeOf(const_cast<Nd4jLong*>(zShapeInfo)), m, zLen, coords);
-        const auto zOffset = shape::getOffset(0, shape::shapeOf(const_cast<Nd4jLong*>(zShapeInfo)), shape::stride(const_cast<Nd4jLong*>(zShapeInfo)), coords, zRank);
+        shape::index2coords(m, zShapeInfo, coords);
+        const auto zOffset = shape::getOffset(zShapeInfo, coords);
 
         sharedMem[threadIdx.x] = 0;
 
           for (uint i = threadIdx.x; i < diagLen; i += blockDim.x) {
 
             coords[zRank] = coords[zRank + 1] = i;
-            const auto xOffset = shape::getOffset(0, shape::shapeOf(const_cast<Nd4jLong*>(xShapeInfo)), shape::stride(const_cast<Nd4jLong*>(xShapeInfo)), coords, xRank);
+            const auto xOffset = shape::getOffset(xShapeInfo, coords);
             sharedMem[threadIdx.x] += x[xOffset];
           }
 
@@ -197,14 +197,14 @@ __global__ static void triuBPCuda(const void* vx, const Nd4jLong* xShapeInfo, vo
 
     for (Nd4jLong i = tid; i < len; i += totalThreads) {
 
-        shape::index2coords(rank, zShapeInfo + 1, i, len, coords);
+        shape::index2coords(i, zShapeInfo, coords);
 
-        const auto zOffset = shape::getOffset(0, zShapeInfo + 1, zShapeInfo + rank + 1, coords, rank);
+        const auto zOffset = shape::getOffset(zShapeInfo, coords);
 
         if((coords[rank - 2] + diag > coords[rank - 1]))    // row + diag > col
             z[zOffset] = 0;
         else
-            z[zOffset] = x[areSameOffsets ? zOffset : shape::getOffset(0, xShapeInfo + 1, xShapeInfo + rank + 1, coords, rank)];
+            z[zOffset] = x[areSameOffsets ? zOffset : shape::getOffset(xShapeInfo, coords)];
     }
 }
 
@@ -263,7 +263,7 @@ __global__ static void tileBPCuda(const void* vx, const Nd4jLong* xShapeInfo, vo
 
     for (Nd4jLong i = tid; i < zLen; i += totalThreads) {
 
-        const auto zOffset = shape::getIndexOffset(i, zShapeInfo, zLen);
+        const auto zOffset = shape::getIndexOffset(i, zShapeInfo);
 
         shape::outerArrayOffsets(xOffsets, i, xShapeInfo, zShapeInfo, memBuff);
 
@@ -329,8 +329,8 @@ __global__ static void clipByNormBPWholeArrCuda(const void* vx, const Nd4jLong*
     __syncthreads();
 
     // fill shared memory with array elements
-    const auto xVal = x[shape::getIndexOffset(tid, xShapeInfo, len)];
-    const auto yVal = y[shape::getIndexOffset(tid, yShapeInfo, len)];
+    const auto xVal = x[shape::getIndexOffset(tid, xShapeInfo)];
+    const auto yVal = y[shape::getIndexOffset(tid, yShapeInfo)];
 
     shMem[2*threadIdx.x]     = static_cast<Z>(xVal * xVal);   // for norm
     shMem[2*threadIdx.x + 1] = static_cast<Z>(xVal * yVal);   // for input * gradO
@@ -414,12 +414,12 @@ __global__ static void clipByNormBPCalcGradCuda(const void* vx, const Nd4jLong*
     }
     __syncthreads();
 
-    const auto yOffset = shape::getIndexOffset(tid, yShapeInfo, len);
-    const auto zOffset = shape::getIndexOffset(tid, zShapeInfo, len);
+    const auto yOffset = shape::getIndexOffset(tid, yShapeInfo);
+    const auto zOffset = shape::getIndexOffset(tid, zShapeInfo);
 
    if(norm > clipNormVal) {
 
-        const auto xOffset = shape::getIndexOffset(tid, xShapeInfo, len);
+        const auto xOffset = shape::getIndexOffset(tid, xShapeInfo);
 
         const Z factor1 = static_cast<Z>(1) / norm;             // 1 / norm
         const Z factor2 = factor1 / (norm * norm);              // 1 / (norm * norm * norm)
@@ -462,8 +462,8 @@ __global__ static void clipByNormBPTadsCuda(const void* vx, const Nd4jLong* xTad
 
     for (uint i = threadIdx.x; i < tadLen; i += blockDim.x) {
 
-        const auto xOffset = shape::getIndexOffset(i, xTadShapeInfo, tadLen);
-        const auto yOffset = shape::getIndexOffset(i, yTadShapeInfo, tadLen);
+        const auto xOffset = shape::getIndexOffset(i, xTadShapeInfo);
+        const auto yOffset = shape::getIndexOffset(i, yTadShapeInfo);
 
         shMem[2*threadIdx.x]     = static_cast<Z>(xTad[xOffset] * xTad[xOffset]);   // for norm
         shMem[2*threadIdx.x + 1] = static_cast<Z>(xTad[xOffset] * yTad[yOffset]);   // for input * gradO
@@ -491,12 +491,12 @@ __global__ static void clipByNormBPTadsCuda(const void* vx, const Nd4jLong* xTad
 
     for (uint i = threadIdx.x; i < tadLen; i += blockDim.x) {
 
-        const auto yOffset = shape::getIndexOffset(i, yTadShapeInfo, tadLen);
-        const auto zOffset = shape::getIndexOffset(i, zTadShapeInfo, tadLen);
+        const auto yOffset = shape::getIndexOffset(i, yTadShapeInfo);
+        const auto zOffset = shape::getIndexOffset(i, zTadShapeInfo);
 
         if(norm > clipNormVal) {
 
-            const auto xOffset = shape::getIndexOffset(i, xTadShapeInfo, tadLen);
+            const auto xOffset = shape::getIndexOffset(i, xTadShapeInfo);
 
             const Z factor1 = static_cast<Z>(1) / norm;             // 1 / norm
             const Z factor2 = factor1 / (norm * norm);              // 1 / (norm * norm * norm)
@@ -563,23 +563,25 @@ void clipByNormBP(nd4j::LaunchContext* context, const NDArray& input, const NDAr
 }
 
     template <typename T>
-    static __global__ void swapShuffleKernel(T* input, Nd4jLong* shape, Nd4jLong firstDim, Nd4jLong len, nd4j::graph::RandomGenerator* rng) {
+    static __global__ void swapShuffleKernel(T* input, Nd4jLong* shape, Nd4jLong firstDim, nd4j::graph::RandomGenerator* rng) {
         auto tid = blockIdx.x * blockDim.x;
         auto step = blockDim.x * gridDim.x;
 
         for (int i = firstDim - 1 - tid - threadIdx.x; i > 0; i -= step) {
             int r = rng->relativeInt(i) % i;
             if (i != r) {
-                T e0 = input[shape::getIndexOffset(i, shape, len)];
-                T e1 = input[shape::getIndexOffset(r, shape, len)];
+                const auto iOffset = shape::getIndexOffset(i, shape);
+                const auto rOffset = shape::getIndexOffset(r, shape);
+                T e0 = input[iOffset];
+                T e1 = input[rOffset];
                 //math::nd4j_swap<T>(input(i), input(r));
-                input[shape::getIndexOffset(i, shape, len)] = e1;
-                input[shape::getIndexOffset(r, shape, len)] = e0;
+                input[iOffset] = e1;
+                input[rOffset] = e0;
             }
         }
     }
     template <typename T>
-    static __global__ void fillShuffleKernel(T* input, Nd4jLong* inputShape, T* output, Nd4jLong* outputShape, Nd4jLong firstDim, Nd4jLong len, int* indices, nd4j::graph::RandomGenerator* rng) {
+    static __global__ void fillShuffleKernel(T* input, Nd4jLong* inputShape, T* output, Nd4jLong* outputShape, Nd4jLong firstDim, int* indices, nd4j::graph::RandomGenerator* rng) {
 
 //        PRAGMA_OMP_PARALLEL_FOR_IF((firstDim-1) > Environment::getInstance()->tadThreshold())
         auto tid = blockIdx.x * blockDim.x;
@@ -587,9 +589,9 @@ void clipByNormBP(nd4j::LaunchContext* context, const NDArray& input, const NDAr
 
         for(int i = firstDim - 1 - tid - threadIdx.x; i > 0; i -= step) {
             int r = rng->relativeInt(i) % i;
-            output[shape::getIndexOffset(i, outputShape, len)] = input[shape::getIndexOffset(indices[r], inputShape, len)];
+            output[shape::getIndexOffset(i, outputShape)] = input[shape::getIndexOffset(indices[r], inputShape)];
             if(i != r) {
-                output[shape::getIndexOffset(r, outputShape, len)] = input[shape::getIndexOffset(indices[i], inputShape, len)];
+                output[shape::getIndexOffset(r, outputShape)] = input[shape::getIndexOffset(indices[i], inputShape)];
 //                output.p(r, input.e<T>(indices[i]));
 //                math::nd4j_swap<int>(indices[i], indices[r]);
                 atomicExch(&indices[i], indices[r]);
@@ -618,7 +620,7 @@ void clipByNormBP(nd4j::LaunchContext* context, const NDArray& input, const NDAr
             cudaMemcpy(dRandom, &rng, sizeof(nd4j::graph::RandomGenerator), cudaMemcpyHostToDevice);
             T* inputBuf = reinterpret_cast<T*>(input.specialBuffer());
             if(isInplace) {
-                swapShuffleKernel<T><<<128, 256, 1024, *stream>>>(inputBuf, input.specialShapeInfo(), firstDim, input.lengthOf(), dRandom);
+                swapShuffleKernel<T><<<128, 256, 1024, *stream>>>(inputBuf, input.specialShapeInfo(), firstDim, dRandom);
             }
             else {
                 std::vector<int> indices(firstDim);
@@ -628,7 +630,7 @@ void clipByNormBP(nd4j::LaunchContext* context, const NDArray& input, const NDAr
                 PointersManager pointersManager(context, "helper::randomShuffle_");
                 int* indicesDev = reinterpret_cast<int*>(pointersManager.replicatePointer(indices.data(), indices.size() * sizeof(int)));
                 T* outputBuf = reinterpret_cast<T*>(output.specialBuffer());
-                fillShuffleKernel<T><<<128, 256, 1024, *stream>>>(inputBuf, input.specialShapeInfo(), outputBuf, output.specialShapeInfo(), firstDim, input.lengthOf(), indicesDev, dRandom);
+                fillShuffleKernel<T><<<128, 256, 1024, *stream>>>(inputBuf, input.specialShapeInfo(), outputBuf, output.specialShapeInfo(), firstDim, indicesDev, dRandom);
                 pointersManager.synchronize();
             }
 //            rng.rewindH(firstDim - 1);
@@ -704,7 +706,7 @@ void clipByNormBP(nd4j::LaunchContext* context, const NDArray& input, const NDAr
             }
             __syncthreads();
             for (int j = threadIdx.x; j < len; j+= blockDim.x) {
-                auto xIndex = shape::getIndexOffset(j, shape, len);
+                auto xIndex = shape::getIndexOffset(j, shape);
 
                 if(norm2Buf[arr] > clipNorm)
                 z[xIndex] *= clipNorm / norm2Buf[arr]; // case with ews = 1 and ordering is 'c'
@@ -714,23 +716,22 @@ void clipByNormBP(nd4j::LaunchContext* context, const NDArray& input, const NDAr
     ////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
     template <typename T>
     static __global__ void clipByNormKernel(Nd4jLong numOfSubArrs, T* inputBuffer, Nd4jLong* shape, Nd4jLong* inputOffsets, T* outputBuffer, Nd4jLong* outputShape, Nd4jLong* outputOffsets, T* norm2Buf, Nd4jLong* norm2shape, T clipNorm) {
+
         for (Nd4jLong arr = blockIdx.x; arr < numOfSubArrs; arr += gridDim.x) {
             __shared__ T* x, *z;
-            __shared__ Nd4jLong lenX, lenZ;
+            __shared__ Nd4jLong lenZ;
             __shared__ T norm2;
 
             if (threadIdx.x == 0) {
-                lenX = shape::length(shape);
                 x = inputBuffer + inputOffsets[arr];
                 z = outputBuffer + outputOffsets[arr];
                 lenZ = shape::length(outputShape);
-                norm2 = norm2Buf[shape::getIndexOffset(arr, norm2shape, numOfSubArrs)];
-                //printf("%d: %lf (vs %lf) %lld %lld\n", arr, norm2, clipNorm, lenX, lenZ);
+                norm2 = norm2Buf[shape::getIndexOffset(arr, norm2shape)];
             }
             __syncthreads();
             for (Nd4jLong j = threadIdx.x; j < lenZ; j+= blockDim.x) {
-                auto xIndex = shape::getIndexOffset(j, shape, lenX);
-                auto zIndex = shape::getIndexOffset(j, outputShape, lenZ);
+                auto xIndex = shape::getIndexOffset(j, shape);
+                auto zIndex = shape::getIndexOffset(j, outputShape);
                 if(norm2 > clipNorm) {
                     z[zIndex] = x[xIndex] * clipNorm / norm2; // case with ews = 1 and ordering is 'c'
                 } else {
@@ -916,8 +917,8 @@ void clipByNormBP(nd4j::LaunchContext* context, const NDArray& input, const NDAr
                 else outputBuf[e] = inputBuf[e];
             }
             else {
-                auto inputOffset = shape::getIndexOffset(e, inputShape, length);
-                auto outputOffset = shape::getIndexOffset(e, outputShape, length);
+                auto inputOffset = shape::getIndexOffset(e, inputShape);
+                auto outputOffset = shape::getIndexOffset(e, outputShape);
                 if (inputBuf[inputOffset] > rightBound) outputBuf[outputOffset] = (T) rightBound;
                 else if (inputBuf[inputOffset] < leftBound) outputBuf[outputOffset] = (T) leftBound;
                 else outputBuf[outputOffset] = inputBuf[outputOffset];
diff --git a/libnd4j/include/ops/declarable/helpers/cuda/weights.cu b/libnd4j/include/ops/declarable/helpers/cuda/weights.cu
index 55f859295..622732d7d 100644
--- a/libnd4j/include/ops/declarable/helpers/cuda/weights.cu
+++ b/libnd4j/include/ops/declarable/helpers/cuda/weights.cu
@@ -29,7 +29,7 @@ namespace helpers {
     template <typename T>
     static __device__ void adjustWeightsKernelD(void* inputBuffer,   Nd4jLong* inputShape,
                                                void* weightsBuffer, Nd4jLong* weightsShape,
-                                               void* outputBuffer,  Nd4jLong inputLength, Nd4jLong weightsLength,
+                                               void* outputBuffer,  Nd4jLong inputLength,
                                                Nd4jLong outputLength, int val) {
     //    typedef Nd4jLong T;
         auto tid = threadIdx.x;
@@ -39,13 +39,13 @@ namespace helpers {
         //for (int e = 0; e < inputLength; e++) {
         for (Nd4jLong e = tid; e < inputLength; e += blockDim.x) {
 
-            Nd4jLong xOffset = shape::getIndexOffset(e, inputShape, inputLength);
+            Nd4jLong xOffset = shape::getIndexOffset(e, inputShape);
             int current = *(reinterpret_cast<int*>(inputBuffer) + xOffset);
             if (current == val) {
                 //printf("%lld\n", xOffset);
-                //Nd4jLong zOffset = shape::getIndexOffset(val, outputShape, outputLength);
+                //Nd4jLong zOffset = shape::getIndexOffset(val, outputShape);
                 if (weightsBuffer != nullptr) {
-                    Nd4jLong yOffset = shape::getIndexOffset(e, weightsShape, weightsLength);
+                    Nd4jLong yOffset = shape::getIndexOffset(e, weightsShape);
                     //atomicAdd();
                     //*reinterpret_cast<int *>(outputBuffer) +=  reinterpret_cast<int *>(weightsBuffer)[yOffset];
                     nd4j::math::atomics::nd4j_atomicAdd(reinterpret_cast<T *>(outputBuffer), reinterpret_cast<T *>(weightsBuffer)[yOffset]); //output->p(val, output->e<T>(val) + 1);
@@ -74,22 +74,19 @@ namespace helpers {
         //auto tid = blockIdx.x * blockDim.x + threadIdx.x; // * blockDim.x; // + threadIdx.x;
         int threadCount = gridDim.x * blockDim.x;
         Nd4jLong inputLength = shape::length(inputShape);
-        Nd4jLong weightsLength = 0;
-        if (weightsBuffer != nullptr)
-            weightsLength = shape::length(weightsShape);
 
         Nd4jLong outputLength = shape::length(outputShape);
-        Nd4jLong borderLen = 1;//outputLength / gridDim.x + outputLength % gridDim.x;
+        Nd4jLong borderLen = 1;
 
         for (Nd4jLong e = blockIdx.x; e < outputLength; e += threadCount) {
         //if (blockIdx.x < outputLength) {
             //if (e + threadCount < outputLength) {
-            Nd4jLong zOffset = shape::getIndexOffset(e, outputShape, outputLength);
+            Nd4jLong zOffset = shape::getIndexOffset(e, outputShape);
             //printf("%d %d %d\n", blockIdx.x, blockDim.x, threadIdx.x);
             //Nd4jLong borderLen = 1;
             T* outputBufferZ = reinterpret_cast<T*>(outputBuffer) + zOffset;
             adjustWeightsKernelD<T>(inputBuffer, inputShape, weightsBuffer, weightsShape, (void*)outputBufferZ,
-                                 inputLength, weightsLength, outputLength, (int)zOffset);
+                                 inputLength, outputLength, (int)zOffset);
 
         }
     }
diff --git a/libnd4j/include/ops/declarable/helpers/cuda/zeta.cu b/libnd4j/include/ops/declarable/helpers/cuda/zeta.cu
index b131ff83f..ada547ac3 100644
--- a/libnd4j/include/ops/declarable/helpers/cuda/zeta.cu
+++ b/libnd4j/include/ops/declarable/helpers/cuda/zeta.cu
@@ -46,9 +46,9 @@ __global__ static void zetaCuda(const void *vx, const Nd4jLong *xShapeInfo,
 
     for (int i = tid; i < len; i += totalThreads) {
 
-        const auto xOffset = shape::getIndexOffset(i, xShapeInfo, len);
-        const auto qOffset = shape::getIndexOffset(i, qShapeInfo, len);
-        const auto zOffset = shape::getIndexOffset(i, zShapeInfo, len);
+        const auto xOffset = shape::getIndexOffset(i, xShapeInfo);
+        const auto qOffset = shape::getIndexOffset(i, qShapeInfo);
+        const auto zOffset = shape::getIndexOffset(i, zShapeInfo);
 
         z[zOffset] = zetaScalar<T>(x[xOffset], q[qOffset]);
     }
diff --git a/libnd4j/include/ops/declarable/helpers/flatten.h b/libnd4j/include/ops/declarable/helpers/flatten.h
index 05421383f..0513e45ea 100644
--- a/libnd4j/include/ops/declarable/helpers/flatten.h
+++ b/libnd4j/include/ops/declarable/helpers/flatten.h
@@ -24,12 +24,45 @@
 #include <vector>
 #include <NDArray.h>
 
-namespace nd4j {
-    namespace ops {
-        namespace helpers {
-            void flatten(nd4j::LaunchContext *context, std::vector<NDArray*> &inputs, NDArray *output, char order);
+namespace nd4j    {
+namespace ops     {
+namespace helpers {
+
+
+//////////////////////////////////////////////////////////////////////
+void flatten(nd4j::LaunchContext *context, std::vector<NDArray*> &inputs, NDArray *output, char order);
+
+
+//////////////////////////////////////////////////////////////////////
+INLINEDEF _CUDA_HD Nd4jLong getIndexOffsetOrdered(Nd4jLong index, const Nd4jLong *shapeInfo, const char order) {
+
+    Nd4jLong offset = 0;
+
+    if (order == 'c') {
+
+        for(uint i = shapeInfo[0]; i > 1; --i) {
+            offset += (index % shapeInfo[i]) * shapeInfo[i + shapeInfo[0]];
+            index /= shapeInfo[i];
         }
+
+        offset += index * shapeInfo[1 + shapeInfo[0]];  // last iteration
     }
+    else {
+
+        for(uint i = 1; i < shapeInfo[0]; ++i) {
+            offset += (index % shapeInfo[i]) * shapeInfo[i + shapeInfo[0]];
+            index /= shapeInfo[i];
+        }
+
+        offset += index * shapeInfo[2 * shapeInfo[0]];  // last iteration
+    }
+
+    return offset;
+}
+
+
+}
+}
 }
 
 #endif //DEV_TESTS_FLATTEN_H
diff --git a/libnd4j/include/ops/declarable/helpers/impl/where.cpp b/libnd4j/include/ops/declarable/helpers/impl/where.cpp
index 120ecdf16..5ca54ced9 100644
--- a/libnd4j/include/ops/declarable/helpers/impl/where.cpp
+++ b/libnd4j/include/ops/declarable/helpers/impl/where.cpp
@@ -31,9 +31,9 @@ namespace nd4j {
 
                 Nd4jLong idx[MAX_RANK];
                 for (int e = 0; e < condition.lengthOf(); e++) {
-                    shape::index2coords(condition.rankOf(), condition.shapeOf(), e, idx);
+                    shape::index2coords(e, condition.getShapeInfo(), idx);
 
-                    auto offset = shape::getOffset(0, condition.shapeOf(), condition.stridesOf(), idx, condition.rankOf());
+                    auto offset = shape::getOffset(condition.getShapeInfo(), idx);
                     if (condition.e<bool>(offset)) {
                         auto array = NDArrayFactory::create_('c', {1, condition.rankOf()}, output.dataType(), output.getContext());
                         for (int f = 0; f < condition.rankOf(); f++)
diff --git a/libnd4j/include/ops/impl/specials.cpp b/libnd4j/include/ops/impl/specials.cpp
index 5eb64fdb4..85642d6c8 100644
--- a/libnd4j/include/ops/impl/specials.cpp
+++ b/libnd4j/include/ops/impl/specials.cpp
@@ -231,12 +231,12 @@ void SpecialMethods<T>::concatCpuGeneric(int dimension, int numArrays, Nd4jPoint
     Nd4jLong SpecialMethods<T>::getPosition(Nd4jLong *xShapeInfo, Nd4jLong index) {
         auto xEWS = shape::elementWiseStride(xShapeInfo);
 
-        if (xEWS == 1) 
-            return index;        
+        if (xEWS == 1)
+            return index;
         else if (xEWS > 1)
             return index * xEWS;
-        else 
-            return shape::getIndexOffset(index, xShapeInfo, shape::length(xShapeInfo));
+        else
+            return shape::getIndexOffset(index, xShapeInfo);
     }
 
     template<typename T>
@@ -457,10 +457,9 @@ PRAGMA_OMP_PARALLEL_FOR_ARGS(schedule(guided) proc_bind(close) reduction(+:retVa
 
     template <typename X, typename Y>
     void quickSort_parallel_internal_key(X* key, Nd4jLong *xShapeInfo, Y* values, Nd4jLong *yShapeInfo, int left, int right, int cutoff, bool descending) {
-        auto length = shape::length(xShapeInfo);
         int i = left, j = right;
         X ktmp;
-        X pivot = key[shape::getIndexOffset((left + right) / 2, xShapeInfo, length)];
+        X pivot = key[shape::getIndexOffset((left + right) / 2, xShapeInfo)];
 
         Y vtmp;
 
@@ -468,35 +467,35 @@ PRAGMA_OMP_PARALLEL_FOR_ARGS(schedule(guided) proc_bind(close) reduction(+:retVa
             /* PARTITION PART */
             while (i <= j) {
                 if (descending) {
-                    while (key[shape::getIndexOffset(i, xShapeInfo, length)] > pivot)
+                    while (key[shape::getIndexOffset(i, xShapeInfo)] > pivot)
                         i++;
-                    while (key[shape::getIndexOffset(j, xShapeInfo, length)] < pivot)
+                    while (key[shape::getIndexOffset(j, xShapeInfo)] < pivot)
                         j--;
                     if (i <= j) {
-                        ktmp = key[shape::getIndexOffset(i, xShapeInfo, length)];
-                        key[shape::getIndexOffset(i, xShapeInfo, length)] = key[shape::getIndexOffset(j, xShapeInfo, length)];
-                        key[shape::getIndexOffset(j, xShapeInfo, length)] = ktmp;
+                        ktmp = key[shape::getIndexOffset(i, xShapeInfo)];
+                        key[shape::getIndexOffset(i, xShapeInfo)] = key[shape::getIndexOffset(j, xShapeInfo)];
+                        key[shape::getIndexOffset(j, xShapeInfo)] = ktmp;
 
-                        vtmp = values[shape::getIndexOffset(i, yShapeInfo, length)];
-                        values[shape::getIndexOffset(i, yShapeInfo, length)] = values[shape::getIndexOffset(j, yShapeInfo, length)];
-                        values[shape::getIndexOffset(j, yShapeInfo, length)] = vtmp;
+                        vtmp = values[shape::getIndexOffset(i, yShapeInfo)];
+                        values[shape::getIndexOffset(i, yShapeInfo)] = values[shape::getIndexOffset(j, yShapeInfo)];
+                        values[shape::getIndexOffset(j, yShapeInfo)] = vtmp;
 
                         i++;
                         j--;
                     }
                 } else {
-                    while (key[shape::getIndexOffset(i, xShapeInfo, length)] < pivot)
+                    while (key[shape::getIndexOffset(i, xShapeInfo)] < pivot)
                         i++;
-                    while (key[shape::getIndexOffset(j, xShapeInfo, length)] > pivot)
+                    while (key[shape::getIndexOffset(j, xShapeInfo)] > pivot)
                         j--;
                     if (i <= j) {
-                        ktmp = key[shape::getIndexOffset(i, xShapeInfo, length)];
-                        key[shape::getIndexOffset(i, xShapeInfo, length)] = key[shape::getIndexOffset(j, xShapeInfo, length)];
-                        key[shape::getIndexOffset(j, xShapeInfo, length)] = ktmp;
+                        ktmp = key[shape::getIndexOffset(i, xShapeInfo)];
+                        key[shape::getIndexOffset(i, xShapeInfo)] = key[shape::getIndexOffset(j, xShapeInfo)];
+                        key[shape::getIndexOffset(j, xShapeInfo)] = ktmp;
 
-                        vtmp = values[shape::getIndexOffset(i, yShapeInfo, length)];
-                        values[shape::getIndexOffset(i, yShapeInfo, length)] = values[shape::getIndexOffset(j, yShapeInfo, length)];
-                        values[shape::getIndexOffset(j, yShapeInfo, length)] = vtmp;
+                        vtmp = values[shape::getIndexOffset(i, yShapeInfo)];
+                        values[shape::getIndexOffset(i, yShapeInfo)] = values[shape::getIndexOffset(j, yShapeInfo)];
+                        values[shape::getIndexOffset(j, yShapeInfo)] = vtmp;
 
                         i++;
                         j--;
@@ -523,10 +522,9 @@ PRAGMA_OMP_TASK
 
     template <typename X, typename Y>
     void quickSort_parallel_internal_value(X* key, Nd4jLong *xShapeInfo, Y* value, Nd4jLong *yShapeInfo, int left, int right, int cutoff, bool descending) {
-        auto length = shape::length(xShapeInfo);
         int i = left, j = right;
         X ktmp;
-        Y pivot = value[shape::getIndexOffset((left + right) / 2, yShapeInfo, length)];
+        Y pivot = value[shape::getIndexOffset((left + right) / 2, yShapeInfo)];
 
         Y vtmp;
 
@@ -534,35 +532,35 @@ PRAGMA_OMP_TASK
             /* PARTITION PART */
             while (i <= j) {
                 if (descending) {
-                    while (value[shape::getIndexOffset(i, yShapeInfo, length)] > pivot)
+                    while (value[shape::getIndexOffset(i, yShapeInfo)] > pivot)
                         i++;
-                    while (value[shape::getIndexOffset(j, yShapeInfo, length)] < pivot)
+                    while (value[shape::getIndexOffset(j, yShapeInfo)] < pivot)
                         j--;
                     if (i <= j) {
-                        ktmp = key[shape::getIndexOffset(i, xShapeInfo, length)];
-                        key[shape::getIndexOffset(i, xShapeInfo, length)] = key[shape::getIndexOffset(j, xShapeInfo, length)];
-                        key[shape::getIndexOffset(j, xShapeInfo, length)] = ktmp;
+                        ktmp = key[shape::getIndexOffset(i, xShapeInfo)];
+                        key[shape::getIndexOffset(i, xShapeInfo)] = key[shape::getIndexOffset(j, xShapeInfo)];
+                        key[shape::getIndexOffset(j, xShapeInfo)] = ktmp;
 
-                        vtmp = value[shape::getIndexOffset(i, yShapeInfo, length)];
-                        value[shape::getIndexOffset(i, yShapeInfo, length)] = value[shape::getIndexOffset(j, yShapeInfo, length)];
-                        value[shape::getIndexOffset(j, yShapeInfo, length)] = vtmp;
+                        vtmp = value[shape::getIndexOffset(i, yShapeInfo)];
+                        value[shape::getIndexOffset(i, yShapeInfo)] = value[shape::getIndexOffset(j, yShapeInfo)];
+                        value[shape::getIndexOffset(j, yShapeInfo)] = vtmp;
 
                         i++;
                         j--;
                     }
                 } else {
-                    while (value[shape::getIndexOffset(i, yShapeInfo, length)] < pivot)
+                    while (value[shape::getIndexOffset(i, yShapeInfo)] < pivot)
                         i++;
-                    while (value[shape::getIndexOffset(j, yShapeInfo, length)] > pivot)
+                    while (value[shape::getIndexOffset(j, yShapeInfo)] > pivot)
                         j--;
                     if (i <= j) {
-                        ktmp = key[shape::getIndexOffset(i, xShapeInfo, length)];
-                        key[shape::getIndexOffset(i, xShapeInfo, length)] = key[shape::getIndexOffset(j, xShapeInfo, length)];
-                        key[shape::getIndexOffset(j, xShapeInfo, length)] = ktmp;
+                        ktmp = key[shape::getIndexOffset(i, xShapeInfo)];
+                        key[shape::getIndexOffset(i, xShapeInfo)] = key[shape::getIndexOffset(j, xShapeInfo)];
+                        key[shape::getIndexOffset(j, xShapeInfo)] = ktmp;
 
-                        vtmp = value[shape::getIndexOffset(i, yShapeInfo, length)];
-                        value[shape::getIndexOffset(i, yShapeInfo, length)] = value[shape::getIndexOffset(j, yShapeInfo, length)];
-                        value[shape::getIndexOffset(j, yShapeInfo, length)] = vtmp;
+                        vtmp = value[shape::getIndexOffset(i, yShapeInfo)];
+                        value[shape::getIndexOffset(i, yShapeInfo)] = value[shape::getIndexOffset(j, yShapeInfo)];
+                        value[shape::getIndexOffset(j, yShapeInfo)] = vtmp;
 
                         i++;
                         j--;
diff --git a/libnd4j/include/ops/special_accumulation_ops.h b/libnd4j/include/ops/special_accumulation_ops.h
index 7a587e754..3f2b2ed1d 100644
--- a/libnd4j/include/ops/special_accumulation_ops.h
+++ b/libnd4j/include/ops/special_accumulation_ops.h
@@ -114,15 +114,15 @@ namespace simdOps {
 					tadLength = shape::length(tadOnlyShapeInfo);//shape::tadLength(xShapeInfo, dimension, dimensionLength);
 					numTads = shape::length(xShapeInfo) / tadLength;
 				}
-				__syncthreads();				
+				__syncthreads();
 
 				for (int r = blockIdx.x; r < numTads; r += gridDim.x) {
 					auto tadOffsetForBlock = tadOffsets[r];
 
 					sPartials[threadIdx.x] = startingValue(dx + tadOffsetForBlock);
 
-					for (int i = threadIdx.x; i < tadLength; i += blockDim.x) {						
-						auto xOffset = tadOffsetForBlock + shape::getIndexOffset(i, tadOnlyShapeInfo, tadLength);
+					for (int i = threadIdx.x; i < tadLength; i += blockDim.x) {
+						auto xOffset = tadOffsetForBlock + shape::getIndexOffset(i, tadOnlyShapeInfo);
 						sPartials[threadIdx.x] = update(sPartials[threadIdx.x], op(dx[xOffset], result[r]), extraParams);
 					}
 					__syncthreads();
@@ -198,8 +198,8 @@ namespace simdOps {
                     auto offset = tadOffsets[i];
                     T start = startingValue(x + offset);
 
-                    for (int j = 0; j < tadLength; j++) {                        
-                        auto xOffset = offset + shape::getIndexOffset(j, tadOnlyShapeInfo, tadLength);
+                    for (int j = 0; j < tadLength; j++) {
+                        auto xOffset = offset + shape::getIndexOffset(j, tadOnlyShapeInfo);
                         start = update(start, op(x[xOffset], result[i]), extraParams);
                     }
 
diff --git a/libnd4j/include/ops/special_ops.h b/libnd4j/include/ops/special_ops.h
index 33cce53c6..8f6ef6b5b 100644
--- a/libnd4j/include/ops/special_ops.h
+++ b/libnd4j/include/ops/special_ops.h
@@ -81,8 +81,8 @@ namespace simdOps {
 		static inline __device__ void execSpecialCuda(
 			             T *dx, Nd4jLong *xShapeBuffer,
 			             Z *result, Nd4jLong *zShapeBuffer,
-			             Z *extraParams, 
-                         int *allocationPointer, Z *reductionPointer, 
+			             Z *extraParams,
+                         int *allocationPointer, Z *reductionPointer,
                          Nd4jLong *tadShapeInfo, Nd4jLong *tadOffsets) {
 
 			__shared__ int kH;
@@ -119,7 +119,7 @@ namespace simdOps {
             __shared__ int kHEff;
             __shared__ int kWEff;
 			__shared__ bool fOrder;
-		
+
 
 			if (threadIdx.x == 0) {
 				kH = (int)extraParams[0];
@@ -266,7 +266,7 @@ static void execSpecial(T *in, Nd4jLong *inShapeBuffer, Z *out, Nd4jLong *outSha
     const Nd4jLong sH = (int)extraParams[2];
     const Nd4jLong sW = (int)extraParams[3];
     const Nd4jLong pH = (int)extraParams[4];
-    const Nd4jLong pW = (int)extraParams[5];    
+    const Nd4jLong pW = (int)extraParams[5];
     const Nd4jLong dH = (int)extraParams[6];
     const Nd4jLong dW = (int)extraParams[7];
     Nd4jLong poolingMode = (int)extraParams[9];
@@ -285,7 +285,7 @@ static void execSpecial(T *in, Nd4jLong *inShapeBuffer, Z *out, Nd4jLong *outSha
     const int iH = shape::sizeAt(inShapeBuffer, 2);
     const int iW = shape::sizeAt(inShapeBuffer, 3);
     const int oH = shape::sizeAt(outShapeBuffer, 2);
-    const int oW = shape::sizeAt(outShapeBuffer, 3);            
+    const int oW = shape::sizeAt(outShapeBuffer, 3);
     const Nd4jLong iStride0 = shape::stride(inShapeBuffer)[0];
     const Nd4jLong iStride1 = shape::stride(inShapeBuffer)[1];
     const Nd4jLong iStride2 = shape::stride(inShapeBuffer)[2];
@@ -296,28 +296,28 @@ static void execSpecial(T *in, Nd4jLong *inShapeBuffer, Z *out, Nd4jLong *outSha
     const Nd4jLong oStride3 = shape::stride(outShapeBuffer)[3];
 
     const Nd4jLong iStep2 = dH*iStride2;
-    const Nd4jLong iStep3 = dW*iStride3;        
+    const Nd4jLong iStep3 = dW*iStride3;
     const int kProd  = kH*kW;
-    const T iStep2Inv = 1./iStep2; 
+    const T iStep2Inv = 1./iStep2;
     const T iStep3Inv = 1./iStep3;
 
     Nd4jLong hstart, wstart, hend, wend;
     T sum, *pIn;
 
-    if(poolingMode == 0) {        // max 
+    if(poolingMode == 0) {        // max
         PRAGMA_OMP_PARALLEL_FOR_ARGS(private(pIn, sum, hstart, wstart, hend, wend) collapse(2))
         for(int b = 0; b < bS; ++b) {
-            for(int c = 0; c < iC; ++c) {                                                            
+            for(int c = 0; c < iC; ++c) {
                 for(int oh = 0; oh < oH; ++oh) {
                     for(int ow = 0; ow < oW; ++ow) {
-                        
+
                         pIn  = in  + b * iStride0 + c * iStride1;
-                        
+
                         hstart = oh * sH - pH;
-                        wstart = ow * sW - pW;                        
+                        wstart = ow * sW - pW;
                         hend = hstart + kHEff;
                         wend = wstart + kWEff;
-                        
+
                         if(hstart < 0)
                             hstart += dH * (Nd4jLong)nd4j::math::nd4j_ceil<T,Nd4jLong>(static_cast<T>(-hstart) / static_cast<T>(dH));
                         if(wstart < 0)
@@ -333,8 +333,8 @@ static void execSpecial(T *in, Nd4jLong *inShapeBuffer, Z *out, Nd4jLong *outSha
                         wend   *= iStride3;
 
                         sum = -nd4j::DataTypeUtils::max<Z>();
-                                                                    
-                        for (Nd4jLong kh = hstart; kh < hend; kh += iStep2) 
+
+                        for (Nd4jLong kh = hstart; kh < hend; kh += iStep2)
                             for (Nd4jLong kw = wstart; kw < wend; kw += iStep3) {
                                 T val = pIn[kh + kw];
                                     if (val > sum)
@@ -344,16 +344,16 @@ static void execSpecial(T *in, Nd4jLong *inShapeBuffer, Z *out, Nd4jLong *outSha
                     }
                 }
             }
-        }    
+        }
     }
-/*************************************************************************/    
+/*************************************************************************/
     else if(poolingMode == 1) {      // avg
         PRAGMA_OMP_PARALLEL_FOR_ARGS(private(pIn, sum, hstart, wstart, hend, wend) collapse(2))
         for(int b = 0; b < bS; ++b) {
-            for(int c = 0; c < iC; ++c) {                                                            
+            for(int c = 0; c < iC; ++c) {
                 for(int oh = 0; oh < oH; ++oh) {
                     for(int ow = 0; ow < oW; ++ow) {
-                        
+
                         pIn  = in  + b * iStride0 + c * iStride1;
 
                         hstart = oh * sH - pH;
@@ -376,30 +376,30 @@ static void execSpecial(T *in, Nd4jLong *inShapeBuffer, Z *out, Nd4jLong *outSha
                         wend   *= iStride3;
 
                         sum = static_cast<Z>(0.);
-                                            
-                        for (Nd4jLong kh = hstart; kh < hend; kh += iStep2) 
+
+                        for (Nd4jLong kh = hstart; kh < hend; kh += iStep2)
                             for (Nd4jLong kw = wstart; kw < wend; kw += iStep3)
                                 sum += pIn[kh + kw];
-                                
+
                         if ((int) extraParam0 == 0)         //Exclude padding
                             sum /= static_cast<T>(nd4j::math::nd4j_ceil<double,T>(static_cast<double>(hend-hstart) / static_cast<double>(iStep2))) * static_cast<T>(nd4j::math::nd4j_ceil<double,T>(static_cast<double>(wend-wstart) / static_cast<double>(iStep3)));   //Accounts for dilation
                         else if ((int) extraParam0 == 1)    //Include padding
                             sum /= kProd;
-                    
+
                         out[b * oStride0 + c * oStride1 + oh * oStride2 + ow * oStride3] = sum;
                     }
                 }
             }
         }
-    }    
-/*************************************************************************/    
+    }
+/*************************************************************************/
     else if(poolingMode == 2) {  // pnorm
         PRAGMA_OMP_PARALLEL_FOR_ARGS(private(pIn, sum, hstart, wstart, hend, wend) collapse(2))
         for(int b = 0; b < bS; ++b) {
-            for(int c = 0; c < iC; ++c) {                                                            
+            for(int c = 0; c < iC; ++c) {
                 for(int oh = 0; oh < oH; ++oh) {
                     for(int ow = 0; ow < oW; ++ow) {
-                        
+
                         pIn  = in  + b * iStride0 + c * iStride1;
 
                         hstart = oh * sH - pH;
@@ -422,13 +422,13 @@ static void execSpecial(T *in, Nd4jLong *inShapeBuffer, Z *out, Nd4jLong *outSha
                         wend   *= iStride3;
 
                         sum = static_cast<T>(0.);
-                                                                    
-                        for (Nd4jLong kh = hstart; kh < hend; kh += iStep2) 
+
+                        for (Nd4jLong kh = hstart; kh < hend; kh += iStep2)
                             for (Nd4jLong kw = wstart; kw < wend; kw += iStep3)
                                 sum += nd4j::math::nd4j_pow<T, T, T>(nd4j::math::nd4j_abs<T>(pIn[kh + kw]), extraParam0);
-                                
+
                         sum = nd4j::math::nd4j_pow<T,T,T>(sum, (T) 1. / extraParam0);
-                                                          
+
                         out[b * oStride0 + c * oStride1 + oh * oStride2 + ow * oStride3] = sum;
                     }
                 }
@@ -482,7 +482,7 @@ static void execSpecial(T *in, Nd4jLong *inShapeBuffer, Z *out, Nd4jLong *outSha
     }
 
 	template<typename T>
-	class 
+	class
 	Im2col {
 	public:
 		static const bool requiresSpecial = true;
@@ -502,8 +502,8 @@ static void execSpecial(T *in, Nd4jLong *inShapeBuffer, Z *out, Nd4jLong *outSha
 		static inline __device__ void execSpecialCuda(
 			                             T *dx, Nd4jLong *xShapeBuffer,
 			                             T *result, Nd4jLong *zShapeBuffer,
-			                             T *extraParams, 
-                                         int *allocationPointer, T *reductionPointer, 
+			                             T *extraParams,
+                                         int *allocationPointer, T *reductionPointer,
                                          Nd4jLong *tadShapeInfo, Nd4jLong *tadOffsets) {
 
 			/*kernel[0], kernel[1], stride[0], stride[1], padding[0], padding[1], 0, false*/
@@ -606,7 +606,7 @@ static void execSpecial(T *in, Nd4jLong *inShapeBuffer, Z *out, Nd4jLong *outSha
 			T *extraParams, Nd4jLong *tadShapeInfo, Nd4jLong *tadOffsets) {
 			/*kernel[0], kernel[1], stride[0], stride[1], padding[0], padding[1], 0, false*/
 
-			// [bS, iC, iH, iW] is convoluted to [bS, iC, kH, kW, oH, oW]        
+			// [bS, iC, iH, iW] is convoluted to [bS, iC, kH, kW, oH, oW]
 
 			int kH = (int)extraParams[0];
 			int kW = (int)extraParams[1];
@@ -615,7 +615,7 @@ static void execSpecial(T *in, Nd4jLong *inShapeBuffer, Z *out, Nd4jLong *outSha
 			int pH = (int)extraParams[4];
 			int pW = (int)extraParams[5];
 			int dH = (int)extraParams[6];			//Dilation, height/y dimension
-			int dW = (int)extraParams[7];			//Dilation, width/x dimension            
+			int dW = (int)extraParams[7];			//Dilation, width/x dimension
             T zeroPadVal = extraParams[9];
 
             auto colShape  = shape::shapeOf(colShapeBuffer);
@@ -642,33 +642,33 @@ static void execSpecial(T *in, Nd4jLong *inShapeBuffer, Z *out, Nd4jLong *outSha
 
             T *col, *im;
             int imRow, imCol;
-            
+
             if (shape::order(imShapeBuffer) == 'c' &&  shape::order(colShapeBuffer) == 'c' && shape::strideDescendingCAscendingF(imShapeBuffer) && shape::strideDescendingCAscendingF(colShapeBuffer)) {
 
                 PRAGMA_OMP_PARALLEL_FOR_ARGS(private(col, im, imRow, imCol) collapse(2))
                 for (int b = 0; b < bS; b++) {
-                    for (int c = 0; c < iC; ++c) {        
-                        for (int kRow = 0; kRow < kH; ++kRow) {                        
-                            for (int kCol = 0; kCol < kW; ++kCol) {                            
+                    for (int c = 0; c < iC; ++c) {
+                        for (int kRow = 0; kRow < kH; ++kRow) {
+                            for (int kCol = 0; kCol < kW; ++kCol) {
                                 for (int colH = 0; colH < oH; ++colH) {
-                                    for (int colW = 0; colW < oW; ++colW) {                    
-                                
+                                    for (int colW = 0; colW < oW; ++colW) {
+
                                         imRow = (-pH + kRow * dH) + colH*sH;
                                         imCol = (-pW + kCol * dW) + colW*sW;
-                                        
+
                                         col = colBuff + b*colStride0 + c*colStride1 + kRow*colStride2 + kCol*colStride3 + colH*colStride4 + colW*colStride5;
-                                        im  = imBuff  + b*imStride0  + c*imStride1  + imRow*imStride2 + imCol*imStride3; 
-                                                    
+                                        im  = imBuff  + b*imStride0  + c*imStride1  + imRow*imStride2 + imCol*imStride3;
+
                                         if (static_cast<unsigned>(imRow) >= static_cast<unsigned>(iH) || static_cast<unsigned>(imCol) >= static_cast<unsigned>(iW))
                                             *col = zeroPadVal;
-                                        else 
+                                        else
                                             *col = *im;
                                     }
                                 }
                             }
                         }
                     }
-                }  
+                }
             }
             else {
 
@@ -677,18 +677,18 @@ static void execSpecial(T *in, Nd4jLong *inShapeBuffer, Z *out, Nd4jLong *outSha
                     for (int colH = 0; colH < oH; ++colH) {
                         for (int colW = 0; colW < oW; ++colW) {
                             for (int c = 0; c < iC; ++c) {
-                                for (int kRow = 0; kRow < kH; ++kRow) {                        
-                                    for (int kCol = 0; kCol < kW; ++kCol) {                            
-                        
+                                for (int kRow = 0; kRow < kH; ++kRow) {
+                                    for (int kCol = 0; kCol < kW; ++kCol) {
+
                                         imRow = (-pH + kRow * dH) + colH*sH;
                                         imCol = (-pW + kCol * dW) + colW*sW;
-                                        
+
                                         col = colBuff + b*colStride0 + c*colStride1 + kRow*colStride2 + kCol*colStride3 + colH*colStride4 + colW*colStride5;
                                         im  = imBuff  + b*imStride0  + c*imStride1  + imRow*imStride2 + imCol*imStride3;
-                                                    
+
                                         if (static_cast<unsigned>(imRow) >= static_cast<unsigned>(iH) || static_cast<unsigned>(imCol) >= static_cast<unsigned>(iW))
                                             *col = zeroPadVal;
-                                        else 
+                                        else
                                             *col = *im;
                                     }
                                 }
@@ -743,8 +743,8 @@ static void execSpecial(T *in, Nd4jLong *inShapeBuffer, Z *out, Nd4jLong *outSha
 		static inline __device__ void execSpecialCuda(
 			                 T *dx, Nd4jLong *xShapeBuffer,
 			                 Z *result, Nd4jLong *zShapeBuffer,
-			                 Z *extraParams, 
-                             int *allocationPointer, Z *reductionPointer, 
+			                 Z *extraParams,
+                             int *allocationPointer, Z *reductionPointer,
                              Nd4jLong *tadShapeInfo, Nd4jLong *tadOffsets) {
 
 
@@ -782,8 +782,8 @@ static void execSpecial(T *in, Nd4jLong *inShapeBuffer, Z *out, Nd4jLong *outSha
 		static inline __device__ void execSpecialCuda(
 			X *dx, Nd4jLong *xShapeBuffer,
 			X *result, Nd4jLong *zShapeBuffer,
-			X *extraParams, int *allocationPointer, 
-            X *reductionPointer, 
+			X *extraParams, int *allocationPointer,
+            X *reductionPointer,
             Nd4jLong *tadShapeInfo, Nd4jLong *tadOffsets) {
 
 		    __shared__ int strideex, stridech, stridekrow, stridekcol, striderow, stridecol, kernelHeight, kernelWidth, strideY, strideX, padHeight, padWidth, imgHeight, imgWidth, dY, dX, samples, depth, imgH, imgW, height_col, width_col, n, kEffectiveW, kEffectiveH;
@@ -856,7 +856,7 @@ static void execSpecial(T *in, Nd4jLong *inShapeBuffer, Z *out, Nd4jLong *outSha
 					for (int w_col = w_col_start; w_col < w_col_end; w_col += 1) {
 						int h_k = (h_im - h_col * strideY);
 						int w_k = (w_im - w_col * strideX);
-						
+
 						if(h_k % dY == 0 && w_k % dX == 0){
 							h_k /= dY;
 							w_k /= dX;
@@ -892,7 +892,7 @@ static void execSpecial(T *in, Nd4jLong *inShapeBuffer, Z *out, Nd4jLong *outSha
             auto colShape  = shape::shapeOf(colShapeBuffer);
             auto colStride = shape::stride(colShapeBuffer);
             auto imShape = shape::shapeOf(imShapeBuffer);
-            auto imStride = shape::stride(imShapeBuffer);            
+            auto imStride = shape::stride(imShapeBuffer);
 
             const int sH = (int)extraParams[0];
             const int sW = (int)extraParams[1];
@@ -900,13 +900,13 @@ static void execSpecial(T *in, Nd4jLong *inShapeBuffer, Z *out, Nd4jLong *outSha
             const int pW = (int)extraParams[3];
             const int iH = (int)extraParams[4];
             const int iW = (int)extraParams[5];
-            const int dH = (int)extraParams[6];     
-            const int dW = (int)extraParams[7];     
+            const int dH = (int)extraParams[6];
+            const int dW = (int)extraParams[7];
 
             const int bS = imShape[0];
             const int iC = imShape[1];
             const int kH = colShape[2];
-            const int kW = colShape[3];                    
+            const int kW = colShape[3];
             const int oH = colShape[4];
             const int oW = colShape[5];
             const Nd4jLong colStride0 = colStride[0];
@@ -932,12 +932,12 @@ static void execSpecial(T *in, Nd4jLong *inShapeBuffer, Z *out, Nd4jLong *outSha
             if (shape::order(colShapeBuffer) == 'c' &&  shape::order(imShapeBuffer) == 'c' && shape::strideDescendingCAscendingF(colShapeBuffer) && shape::strideDescendingCAscendingF(imShapeBuffer)) {
 
                 PRAGMA_OMP_PARALLEL_FOR_ARGS(private(col, im, imRow, imCol) collapse(2))
-                for (int b = 0; b < bS; b++) {        
-                    for (int c = 0; c < iC; ++c) {                    
-                        for (int kRow = 0; kRow < kH; ++kRow) {                        
-                            for (int kCol = 0; kCol < kW; ++kCol) {                            
+                for (int b = 0; b < bS; b++) {
+                    for (int c = 0; c < iC; ++c) {
+                        for (int kRow = 0; kRow < kH; ++kRow) {
+                            for (int kCol = 0; kCol < kW; ++kCol) {
                                 for (int colH = 0; colH < oH; ++colH) {
-                                    for (int colW = 0; colW < oW; ++colW) {                    
+                                    for (int colW = 0; colW < oW; ++colW) {
 
                                         imRow = (-pH + kRow * dH) + colH*sH;
                                         imCol = (-pW + kCol * dW) + colW*sW;
@@ -952,21 +952,21 @@ static void execSpecial(T *in, Nd4jLong *inShapeBuffer, Z *out, Nd4jLong *outSha
                             }
                         }
                     }
-                }  
+                }
             }
             else {
 
                 PRAGMA_OMP_PARALLEL_FOR_ARGS(private(im, col, imRow, imCol))
-                for (int b = 0; b < bS; b++) {        
+                for (int b = 0; b < bS; b++) {
                     for (int colH = 0; colH < oH; ++colH) {
                         for (int colW = 0; colW < oW; ++colW) {
-                            for (int c = 0; c < iC; ++c) {                        
-                                for (int kRow = 0; kRow < kH; ++kRow) {                        
-                                    for (int kCol = 0; kCol < kW; ++kCol) {                            
-                        
+                            for (int c = 0; c < iC; ++c) {
+                                for (int kRow = 0; kRow < kH; ++kRow) {
+                                    for (int kCol = 0; kCol < kW; ++kCol) {
+
                                         imRow = (-pH + kRow * dH) + colH*sH;
                                         imCol = (-pW + kCol * dW) + colW*sW;
-                                        
+
                                         col = colBuff + b*colStride0 + c*colStride1 + kRow*colStride2 + kCol*colStride3 + colH*colStride4 + colW*colStride5;
                                         im  = imBuff  + b*imStride0  + c*imStride1  + imRow*imStride2 + imCol*imStride3;
 
@@ -975,9 +975,9 @@ static void execSpecial(T *in, Nd4jLong *inShapeBuffer, Z *out, Nd4jLong *outSha
                                     }
                                 }
                             }
-                        }                           
+                        }
                     }
-                }  
+                }
             }
         }
 
@@ -1021,10 +1021,10 @@ static void execSpecial(T *in, Nd4jLong *inShapeBuffer, Z *out, Nd4jLong *outSha
 		static const bool requiresSpecial = true;
 
 #ifdef __CUDACC__
-		static inline __device__ void execSpecialCuda(X *dx, Nd4jLong *xShapeBuffer, 
-                                                    X *result, Nd4jLong *zShapeBuffer, 
-                                                    X *extraParams, int *allocationPointer, 
-                                                    X *reductionPointer, 
+		static inline __device__ void execSpecialCuda(X *dx, Nd4jLong *xShapeBuffer,
+                                                    X *result, Nd4jLong *zShapeBuffer,
+                                                    X *extraParams, int *allocationPointer,
+                                                    X *reductionPointer,
                                                     Nd4jLong *tadShapeInfo, Nd4jLong *tadOffsets) {
 
             __shared__ Nd4jLong xLength;
@@ -1064,12 +1064,12 @@ static void execSpecial(T *in, Nd4jLong *inShapeBuffer, Z *out, Nd4jLong *outSha
                         dx[idx2] = dx[idx1];
                         dx[idx1] = tmp;
                     }
-                } 
-                else {                    
+                }
+                else {
 
 					for (int e = tid; e < xLength / 2; e += blockDim.x * gridDim.x) {
-                        auto xOffset = shape::getIndexOffset(e, xShapeBuffer, xLength);
-                        auto zOffset = shape::getIndexOffset(sLength - e, xShapeBuffer, xLength);
+                        auto xOffset = shape::getIndexOffset(e, xShapeBuffer);
+                        auto zOffset = shape::getIndexOffset(sLength - e, xShapeBuffer);
                         result[zOffset] = dx[xOffset];
 					}
                 }
@@ -1094,12 +1094,12 @@ static void execSpecial(T *in, Nd4jLong *inShapeBuffer, Z *out, Nd4jLong *outSha
                     for (int e = tid; e < xLength; e += blockDim.x * gridDim.x) {
                         result[(sLength - e) * zEWS] = dx[e * xEWS];
                     }
-                } 
-                else {                  
+                }
+                else {
 
                     for (int e = tid; e < xLength; e += blockDim.x * gridDim.x) {
-                        auto xOffset = shape::getIndexOffset(e, xShapeBuffer, xLength);
-                        auto zOffset = shape::getIndexOffset(sLength - e, xShapeBuffer, xLength);
+                        auto xOffset = shape::getIndexOffset(e, xShapeBuffer);
+                        auto zOffset = shape::getIndexOffset(sLength - e, xShapeBuffer);
                         result[zOffset] = dx[xOffset];
                     }
                 }
@@ -1134,13 +1134,13 @@ static void execSpecial(T *in, Nd4jLong *inShapeBuffer, Z *out, Nd4jLong *outSha
                         dx[idx2] = dx[idx1];
                         dx[idx1] = tmp;
                     }
-				} 
+				}
                 else {
 
                     PRAGMA_OMP_PARALLEL_FOR_SIMD
-                    for (Nd4jLong e = 0; e < xLength / 2; e++) {                        
-                        auto xOffset = shape::getIndexOffset(e, xShapeBuffer, xLength);
-                        auto zOffset = shape::getIndexOffset(sLength - e, xShapeBuffer, xLength);
+                    for (Nd4jLong e = 0; e < xLength / 2; e++) {
+                        auto xOffset = shape::getIndexOffset(e, xShapeBuffer);
+                        auto zOffset = shape::getIndexOffset(sLength - e, xShapeBuffer);
 
                         result[zOffset] = dx[xOffset];
                     }
@@ -1160,13 +1160,13 @@ static void execSpecial(T *in, Nd4jLong *inShapeBuffer, Z *out, Nd4jLong *outSha
 					for (Nd4jLong e = 0; e < xLength; e++) {
 						result[(sLength - e) * zEWS] = dx[e * xEWS];
 					}
-				} 
+				}
                 else {
 
                     PRAGMA_OMP_PARALLEL_FOR_SIMD
 					for (Nd4jLong e = 0; e < xLength; e++) {
-						auto xOffset = shape::getIndexOffset(e, xShapeBuffer, xLength);
-                        auto zOffset = shape::getIndexOffset(sLength - e, zShapeBuffer, xLength);
+						auto xOffset = shape::getIndexOffset(e, xShapeBuffer);
+                        auto zOffset = shape::getIndexOffset(sLength - e, zShapeBuffer);
 						result[zOffset] = dx[xOffset];
 					}
 				}
@@ -1192,7 +1192,7 @@ static void execSpecial(T *in, Nd4jLong *inShapeBuffer, Z *out, Nd4jLong *outSha
 			void *vx, Nd4jLong *xShapeBuffer,
 			void *vresult, Nd4jLong *zShapeBuffer,
 			void *vextraParams,
-			int *allocationPointer, void *reductionPointer, 
+			int *allocationPointer, void *reductionPointer,
             Nd4jLong *tadShapeInfo, Nd4jLong *tadOffsets) {
 
             auto dx = reinterpret_cast<X *>(vx);
@@ -1263,10 +1263,10 @@ static void execSpecial(T *in, Nd4jLong *inShapeBuffer, Z *out, Nd4jLong *outSha
                         tadShapeInfo = tadPack.primaryShapeInfo();
                         tadOffsets = tadPack.primaryOffsets();
                     }
-                    
+
                     const uint tadLen    = shape::length(tadShapeInfo);
                     const uint numOfTads = shape::length(xShapeInfo) / tadLen;
-        
+
                     if(shape::elementWiseStride(tadShapeInfo) == 1) {
 
                         PRAGMA_OMP_PARALLEL_FOR_SIMD
@@ -1277,18 +1277,18 @@ static void execSpecial(T *in, Nd4jLong *inShapeBuffer, Z *out, Nd4jLong *outSha
 
                             X max = -nd4j::DataTypeUtils::max<X>();
                             X sum = 0;
-                        
+
                             for(uint j = 0; j < tadLen; ++j)
-                                max = nd4j::math::nd4j_max<X>(max, inBuff[j]);            
-            
+                                max = nd4j::math::nd4j_max<X>(max, inBuff[j]);
+
                             for (uint j = 0; j < tadLen; ++j) {
                                 X temp = nd4j::math::nd4j_exp<X,X>(inBuff[j] - max);
                                 outBuff[j] = temp;
                                 sum += temp;
                             }
-            
+
                             for (uint j = 0; j < tadLen; ++j)
-                            outBuff[j] /= sum;            
+                            outBuff[j] /= sum;
                         }
                     }
                     else {
@@ -1300,17 +1300,17 @@ static void execSpecial(T *in, Nd4jLong *inShapeBuffer, Z *out, Nd4jLong *outSha
                         shape::calcOffsets(tadShapeInfo, offsets);
 
                         PRAGMA_OMP_PARALLEL_FOR_SIMD
-                        for (uint i = 0; i < numOfTads; ++i) {                        
+                        for (uint i = 0; i < numOfTads; ++i) {
 
                             X* inBuff  = x  + tadOffsets[i];
                             X* outBuff = z + tadOffsets[i];
 
                             X max = -nd4j::DataTypeUtils::max<X>();
-                            X sum = 0.f;                                
+                            X sum = 0.f;
+
+                            for(uint j = 0; j < tadLen; ++j)
+                                max = nd4j::math::nd4j_max<X>(max, inBuff[offsets[j]]);
 
-                            for(uint j = 0; j < tadLen; ++j)                                 
-                                max = nd4j::math::nd4j_max<X>(max, inBuff[offsets[j]]);                            
-            
                             for (uint j = 0; j < tadLen; ++j) {
                                 X temp = nd4j::math::nd4j_exp<X,X>(inBuff[offsets[j]] - max);
                                 outBuff[offsets[j]] = temp;
@@ -1351,7 +1351,7 @@ static void execSpecial(T *in, Nd4jLong *inShapeBuffer, Z *out, Nd4jLong *outSha
 
                     delete[] maxResultShapeBuffer;
                     delete[] maxResult;
-                }                
+                }
             }
             else if (shape::isVector(xShapeInfo)) {
                 auto max = -nd4j::DataTypeUtils::max<X>();
@@ -1416,7 +1416,7 @@ static void execSpecial(T *in, Nd4jLong *inShapeBuffer, Z *out, Nd4jLong *outSha
             			void *vx, Nd4jLong *xShapeBuffer,
             			void *vresult, Nd4jLong *zShapeBuffer,
             			void *vextraParams,
-            			int *allocationPointer, void *reductionPointer, 
+            			int *allocationPointer, void *reductionPointer,
                         Nd4jLong *tadShapeInfo, Nd4jLong *tadOffsets) {
 
 			auto shape = shape::shapeOf(xShapeBuffer);
@@ -1578,7 +1578,7 @@ static void execSpecial(T *in, Nd4jLong *inShapeBuffer, Z *out, Nd4jLong *outSha
 			                 void *vx, Nd4jLong *xShapeBuffer,
 			                 void *vresult, Nd4jLong *zShapeBuffer,
 			                 void *vextraParams,
-			                 int *allocationPointer, void *reductionPointer, 
+			                 int *allocationPointer, void *reductionPointer,
                              Nd4jLong *tadShapeInfo, Nd4jLong *tadOffsets) {
 
             auto dx = reinterpret_cast<X *>(vx);
@@ -1650,7 +1650,7 @@ static void execSpecial(T *in, Nd4jLong *inShapeBuffer, Z *out, Nd4jLong *outSha
             auto dx = reinterpret_cast<X *>(vx);
             auto result = reinterpret_cast<X *>(vresult);
             auto extraParams = reinterpret_cast<X *>(vextraParams);
-            
+
 			if (shape::isMatrix(xShapeBuffer, 2)) {
 				auto shape = shape::shapeOf(xShapeBuffer);
 
@@ -1700,9 +1700,9 @@ static void execSpecial(T *in, Nd4jLong *inShapeBuffer, Z *out, Nd4jLong *outSha
 					}
 				}
 				else {
-                    
-                    for (int i = 0; i < len; i++) {                        
-                        Nd4jLong zOffset = shape::getIndexOffset(i, zShapeBuffer, len);
+
+                    for (int i = 0; i < len; i++) {
+                        Nd4jLong zOffset = shape::getIndexOffset(i, zShapeBuffer);
                         result[zOffset] = result[zOffset] * ((X) 1.0f - result[zOffset]);
                     }
                 }
@@ -2013,8 +2013,8 @@ PRAGMA_OMP_CRITICAL
 		static inline __device__ void execSpecialCuda(
 			             void *vx, Nd4jLong *xShapeBuffer,
 			             void *vresult, Nd4jLong *zShapeBuffer,
-			             void *vextraParams, int *allocationPointer, 
-                         void *reductionPointer, 
+			             void *vextraParams, int *allocationPointer,
+                         void *reductionPointer,
                          Nd4jLong *tadShapeInfo, Nd4jLong *tadOffsets) {
 
             auto dx = reinterpret_cast<X *>(vx);
@@ -2162,7 +2162,7 @@ PRAGMA_OMP_CRITICAL
                 //decompose in to several sub tads after
                 //moving all dimensions (in sorted order)
                 //to the back.
-                //permuted version of the x shape info for setting up the tad problem				
+                //permuted version of the x shape info for setting up the tad problem
 				auto tadShapeShapeInfo = tadShapeInfo;
 				if(tadShapeInfo==nullptr) {
                     auto tadPack = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(xShapeBuffer, dimension, dimensionLength);
@@ -2170,7 +2170,7 @@ PRAGMA_OMP_CRITICAL
 					tadShapeShapeInfo = tadPack.primaryShapeInfo();
 					tadOffsets = tadPack.primaryOffsets();
                     tadShapeInfo = tadShapeShapeInfo;
-				}						                                				
+				}
 
                 auto tadLength = shape::length(tadShapeInfo);//shape::tadLength(xShapeBuffer, dimension, dimensionLength);
                 auto tads = shape::length(xShapeBuffer) / tadLength;
diff --git a/libnd4j/include/ops/special_random_ops.h b/libnd4j/include/ops/special_random_ops.h
index 0d90c212a..1ae310ad4 100644
--- a/libnd4j/include/ops/special_random_ops.h
+++ b/libnd4j/include/ops/special_random_ops.h
@@ -111,24 +111,24 @@ namespace randomOps {
                     }
 //                    __syncthreads();  // Eliminated due RTX20xx specific
                 }
-            } 
+            }
             else {
-            
+
                 for (Nd4jLong i = tid; i < zLength; i+=blockDim.x * gridDim.x) {
 
-                    auto zOffset2 = shape::getIndexOffset(i, zShapeBuffer, zLength);
+                    auto zOffset2 = shape::getIndexOffset(i, zShapeBuffer);
                     T prob = rng->relativeT<T>(i);
                     T cumProb = (T) 0.0f;
 
                     for (Nd4jLong f = 0; f < yLength; f++) {
-                        
-                        auto yOffset2 = shape::getIndexOffset(f, yShapeBuffer, yLength);
+
+                        auto yOffset2 = shape::getIndexOffset(f, yShapeBuffer);
                         T relProb = y[yOffset2];
                         cumProb += relProb;
 
-                        if (prob <= cumProb || f == yLength - 1) {                            
-                            
-                            auto xOffset2 = shape::getIndexOffset(f, xShapeBuffer, xLength);
+                        if (prob <= cumProb || f == yLength - 1) {
+
+                            auto xOffset2 = shape::getIndexOffset(f, xShapeBuffer);
                             z[zOffset2] = x[xOffset2];
                             f += yLength;
                         }
@@ -179,25 +179,25 @@ namespace randomOps {
                         }
                     }
                 }
-            } 
+            }
             else {
 
                 PRAGMA_OMP_PARALLEL_FOR_THREADS(_threads)
                 for (Nd4jLong i = 0; i < zLength; i++) {
 
-                    auto zOffset2 = shape::getIndexOffset(i, zShapeBuffer, zLength);
+                    auto zOffset2 = shape::getIndexOffset(i, zShapeBuffer);
                     T prob = rng->relativeT<T>(i);
                     T cumProb = (T) 0.0f;
 
                     for (Nd4jLong f = 0; f < yLength; f++) {
-                        
-                        auto yOffset2 = shape::getIndexOffset(f, yShapeBuffer, yLength);
+
+                        auto yOffset2 = shape::getIndexOffset(f, yShapeBuffer);
                         T relProb = y[yOffset2];
                         cumProb += relProb;
 
-                        if (prob <= cumProb || f == yLength - 1) {                        
-                            
-                            auto xOffset2 = shape::getIndexOffset(f, xShapeBuffer, xLength);
+                        if (prob <= cumProb || f == yLength - 1) {
+
+                            auto xOffset2 = shape::getIndexOffset(f, xShapeBuffer);
                             z[zOffset2] = x[xOffset2];
                             break;
                         }
@@ -571,8 +571,8 @@ namespace randomOps {
             }
         }
     };
-    
-//////////////////////////////////////////////////////////////////////        
+
+//////////////////////////////////////////////////////////////////////
     // This Op produces random Gaussian values within [mean-2*stddev,mean+2*stddev]
     template<typename T>
     class TruncatedNormalDistribution {
diff --git a/libnd4j/include/pairwise_util.h b/libnd4j/include/pairwise_util.h
index c4b84bee2..e87e8961d 100755
--- a/libnd4j/include/pairwise_util.h
+++ b/libnd4j/include/pairwise_util.h
@@ -50,7 +50,7 @@ namespace shape {
     Nd4jLong elementWiseStride(const Nd4jLong *shapeInfo);
     char order(const Nd4jLong *shapeInfo);
     bool isStrideSimple(const Nd4jLong* shapeInfo);
-    Nd4jLong getIndexOffset(Nd4jLong index, const Nd4jLong *shapeInfo, Nd4jLong arrLen);
+    Nd4jLong getIndexOffset(Nd4jLong index, const Nd4jLong *shapeInfo);
 }
 
  */
@@ -269,13 +269,13 @@ public:
     Nd4jLong chunks;
     Nd4jLong modulo;
     Nd4jLong remainder;
-    
+
     BlockInformation(Nd4jLong length, int threshold) {
 
     threads = length / threshold;
     threads = nd4j::math::nd4j_max<int>(1, threads);
     threads = nd4j::math::nd4j_min<int>(threads, omp_get_max_threads());
-    
+
     items = length / threads;
     remainder = length % threads;
     if(items < 1)
diff --git a/libnd4j/linuxOnPower.md b/libnd4j/linuxOnPower.md
index b2142b3f7..cbecda9c1 100644
--- a/libnd4j/linuxOnPower.md
+++ b/libnd4j/linuxOnPower.md
@@ -40,12 +40,7 @@ export CUDA_VISIBLE_DEVICES=0
 
 1. Clone necessary modules from github as follows.
 ```
-git clone https://github.com/deeplearning4j/Canova
-git clone https://github.com/deeplearning4j/nd4j.git
-git clone https://github.com/deeplearning4j/libnd4j.git
-git clone https://github.com/deeplearning4j/dl4j-0.4-examples.git
-git clone https://github.com/deeplearning4j/deeplearning4j.git
-git clone https://github.com/bytedeco/javacpp.git
+git clone https://github.com/eclipse/deeplearning4j.git
 ```
 
 2. Modify the setting file to enable GPU. (Skip this step if you do not use GPU)
diff --git a/libnd4j/macOSx10 (CPU only).md b/libnd4j/macOSx10 (CPU only).md
index 8710ea60c..59031e41e 100644
--- a/libnd4j/macOSx10 (CPU only).md	
+++ b/libnd4j/macOSx10 (CPU only).md	
@@ -41,7 +41,7 @@ brew insall gcc
 
 10. Now download the latest JavaCPP (https://github.com/bytedeco/javacpp) and build it using `mvn clean install -X -DskipTests -Dmaven.javadoc.skip=true`
 
-11. Now download the latest [nd4j](https://github.com/deeplearning4j/nd4j.git) project from the repo. Make sure that nd4j is at the same leve in the tree as libnd4j, otherwise the maven will complain about not being able to find the header files.
+11. Now download the latest [nd4j](https://github.com/eclipse/deeplearning4j.git) project from the repo. Make sure that nd4j is at the same leve in the tree as libnd4j, otherwise the maven will complain about not being able to find the header files.
 
 12. inside the nd4j directory run `mvn clean install -X -DskipTests -Dmaven.javadoc.skip=true -pl '!org.nd4j:nd4j-cuda-9.0,!org.nd4j:nd4j-tests,!org.nd4j:nd4j-cuda-9.0-platform'` this should mainly fly by.
 
diff --git a/libnd4j/tests_cpu/layers_tests/ConvolutionTests1.cpp b/libnd4j/tests_cpu/layers_tests/ConvolutionTests1.cpp
index ff36c9cca..b8845cb8d 100644
--- a/libnd4j/tests_cpu/layers_tests/ConvolutionTests1.cpp
+++ b/libnd4j/tests_cpu/layers_tests/ConvolutionTests1.cpp
@@ -407,7 +407,7 @@ TYPED_TEST(TypedConvolutionTests1, sconv2d_3) {
     auto input = NDArrayFactory::create<TypeParam>('c', {3, 3, 8, 8});
     auto weightsD = NDArrayFactory::create<TypeParam>('c', {1, 3, 1, 1});
     auto weightsP = NDArrayFactory::create<TypeParam>('c', {2, 3, 1, 1});
-    auto bias = NDArrayFactory::create<TypeParam>('c', {1, 2});
+    auto bias = NDArrayFactory::create<TypeParam>('c', {2});
     auto output = NDArrayFactory::create<TypeParam>('c', {3, 2, 8, 8});
     output.assign(0.0);
 
@@ -915,7 +915,7 @@ TEST_F(ConvolutionTests1, TestDeconv_ff_2) {
 
     auto input = NDArrayFactory::create<double>('c', {3, 3, 4, 4});
     auto weights = NDArrayFactory::create<double>('c',{3, 2, 1, 1});
-    auto bias = NDArrayFactory::create<double>('c', {1, 2});
+    auto bias = NDArrayFactory::create<double>('c', {2});
 
     input.linspace(1);
     weights.linspace(1);
@@ -939,11 +939,11 @@ TEST_F(ConvolutionTests1, TestDeconv_ff_2) {
 TYPED_TEST(TypedConvolutionTests1, Test_Conv1D_ff_1) {
     auto input = NDArrayFactory::create<TypeParam>('c', {2, 2, 6});
     auto weights = NDArrayFactory::create<TypeParam>('c', {2, 2, 3}, {1,5,9,3,7,11,2,6,10,4,8,12});
-    auto bias = NDArrayFactory::create<TypeParam>('c', {1, 3});
+    auto bias = NDArrayFactory::create<TypeParam>('c', {3});
     auto expFF = NDArrayFactory::create<TypeParam>('c', {2, 3, 5}, {59.0, 69.0, 79.0, 89.0, 99.0, 132.0, 158.0, 184.0, 210.0, 236.0, 205.0, 247.0, 289.0, 331.0, 373.0, 179.0, 189.0, 199.0, 209.0, 219.0, 444.0, 470.0, 496.0, 522.0, 548.0, 709.0, 751.0, 793.0, 835.0, 877.0});
     auto expEps = NDArrayFactory::create<TypeParam>('c', {2, 2, 6}, {130.0, 293.0, 326.0, 359.0, 392.0, 220.0, 166.0, 371.0, 416.0, 461.0, 506.0, 280.0, 355.0, 788.0, 821.0, 854.0, 887.0, 490.0, 481.0, 1046.0, 1091.0, 1136.0, 1181.0, 640.0});
     auto expGW = NDArrayFactory::create<TypeParam>('c', {3, 2, 2}, {1415.0, 1520.0, 2045.0, 2150.0, 1865.0, 2020.0, 2795.0, 2950.0, 2315.0, 2520.0, 3545.0, 3750.0});
-    auto expGB = NDArrayFactory::create<TypeParam>('c', {1, 3}, {105.0, 155.0, 205.0});
+    auto expGB = NDArrayFactory::create<TypeParam>('c', {3}, {105.0, 155.0, 205.0});
 
     expGW.permutei({2,1,0});
     input.linspace(1);
diff --git a/libnd4j/tests_cpu/layers_tests/DeclarableOpsTests11.cpp b/libnd4j/tests_cpu/layers_tests/DeclarableOpsTests11.cpp
index 01e8e82c2..7428539f3 100644
--- a/libnd4j/tests_cpu/layers_tests/DeclarableOpsTests11.cpp
+++ b/libnd4j/tests_cpu/layers_tests/DeclarableOpsTests11.cpp
@@ -47,7 +47,7 @@ TEST_F(DeclarableOpsTests11, test_mixed_biasadd_1) {
     auto exp = NDArrayFactory::create<float>('c', {2, 3}, {1.f, 2.f, 3.f, 1.f, 2.f, 3.f});
 
     nd4j::ops::biasadd op;
-    auto status = op.execute({&x, &y}, {&z}, {}, {}, {});
+    auto status = op.execute({&x, &y}, {&z}, {}, {}, {true});
     ASSERT_EQ(Status::OK(), status);
 
     ASSERT_EQ(exp, z);
@@ -66,11 +66,11 @@ TEST_F(DeclarableOpsTests11, test_listdiff_1) {
 
 ///////////////////////////////////////////////////////////////////
 TEST_F(DeclarableOpsTests11, log_loss_grad_test1) {
-    
+
     NDArray labels('c', {2,3,4}, nd4j::DataType::DOUBLE);
     NDArray predictions('c', {2,3,4}, nd4j::DataType::DOUBLE);
     NDArray weights('c', {2,3,4}, nd4j::DataType::DOUBLE);
-    
+
     NDArray dLdpExp('c', {2,3,4}, {-12.49997,-13.04346, -13.63635, -14.28571,-14.99999,-15.78947, -16.66666, -17.64705,-18.75   ,-20.     , -21.42857, -23.07692,
                                    -24.99999,-27.27272, -29.99999, -33.33332,-37.49999,-42.85713, -49.99998, -59.99998,-74.99995,-99.99992,-149.99986,-299.99911});
     NDArray dLdwExp('c', {2,3,4}, {3.21887,  4.96807,  6.10512,  6.80726,  7.15461,  7.19051,  6.93973,  6.41584,  5.62456,  4.56548,  3.2326 ,  1.61444,
@@ -80,14 +80,14 @@ TEST_F(DeclarableOpsTests11, log_loss_grad_test1) {
 
     predictions.linspace(0.04, 0.04);
     labels.linspace(1);
-    weights.assign(0.5);    
+    weights.assign(0.5);
 
     nd4j::ops::log_loss_grad op;
     auto results = op.execute({&predictions, &weights, &labels}, {1e-7}, {0}, {});
 
     ASSERT_EQ(ND4J_STATUS_OK, results->status());
 
-    auto *dLdp = results->at(0);       
+    auto *dLdp = results->at(0);
     auto *dLdw = results->at(1);
     auto *dLdl = results->at(2);
 
@@ -103,16 +103,16 @@ TEST_F(DeclarableOpsTests11, log_loss_grad_test1) {
 
 ///////////////////////////////////////////////////////////////////
 TEST_F(DeclarableOpsTests11, log_loss_grad_test2) {
-    
+
     NDArray labels('c', {2,3,4}, nd4j::DataType::DOUBLE);
     NDArray predictions('c', {2,3,4}, nd4j::DataType::DOUBLE);
     NDArray weights('c', {2,1,4}, nd4j::DataType::DOUBLE);
-    
+
     NDArray dLdwExp('c', {2,1,4}, {15.99805, 16.72406, 16.27746,  14.83754,-44.97147,-59.99582,-79.28771,-107.35497});
 
     predictions.linspace(0.04, 0.04);
     labels.linspace(1);
-    weights.assign(0.5);    
+    weights.assign(0.5);
 
     nd4j::ops::log_loss_grad op;
     auto results = op.execute({&predictions, &weights, &labels}, {1e-7}, {0});
@@ -120,7 +120,7 @@ TEST_F(DeclarableOpsTests11, log_loss_grad_test2) {
     ASSERT_EQ(ND4J_STATUS_OK, results->status());
 
     auto *dLdw = results->at(1);
-    
+
     ASSERT_TRUE(dLdwExp.isSameShape(dLdw));
     ASSERT_TRUE(dLdwExp.equalsTo(dLdw));
 
@@ -129,11 +129,11 @@ TEST_F(DeclarableOpsTests11, log_loss_grad_test2) {
 
 ///////////////////////////////////////////////////////////////////
 TEST_F(DeclarableOpsTests11, log_loss_grad_test3) {
-    
+
     NDArray labels('c', {2,3,4}, nd4j::DataType::DOUBLE);
     NDArray predictions('c', {2,3,4}, nd4j::DataType::DOUBLE);
     NDArray weights(nd4j::DataType::DOUBLE);
-    
+
     NDArray dLdpExp('c', {2,3,4}, {-12.49997,-13.04346, -13.63635, -14.28571,-14.99999,-15.78947, -16.66666, -17.64705,-18.75   ,-20.     , -21.42857, -23.07692,
                                    -24.99999,-27.27272, -29.99999, -33.33332,-37.49999,-42.85713, -49.99998, -59.99998,-74.99995,-99.99992,-149.99986,-299.99911});
     NDArray dLdwExp('c', {}, {-227.77286});
@@ -142,14 +142,14 @@ TEST_F(DeclarableOpsTests11, log_loss_grad_test3) {
 
     predictions.linspace(0.04, 0.04);
     labels.linspace(1);
-    weights.assign(0.5);    
+    weights.assign(0.5);
 
     nd4j::ops::log_loss_grad op;
     auto results = op.execute({&predictions, &weights, &labels}, {1e-7}, {1});
 
     ASSERT_EQ(ND4J_STATUS_OK, results->status());
 
-    auto *dLdp = results->at(0);       
+    auto *dLdp = results->at(0);
     auto *dLdw = results->at(1);
     auto *dLdl = results->at(2);
 
@@ -165,22 +165,22 @@ TEST_F(DeclarableOpsTests11, log_loss_grad_test3) {
 
 ///////////////////////////////////////////////////////////////////
 TEST_F(DeclarableOpsTests11, log_loss_grad_test4) {
-    
+
     NDArray labels('c', {2,3,4}, nd4j::DataType::DOUBLE);
     NDArray predictions('c', {2,3,4}, nd4j::DataType::DOUBLE);
     NDArray weights('c', {1,3,1}, nd4j::DataType::DOUBLE);
-    
+
     NDArray dLdwExp('c', {1,3,1}, {4.8876 , -46.29156, -186.36887});
-  
+
     predictions.linspace(0.04, 0.04);
     labels.linspace(1);
-    weights.assign(0.5);    
+    weights.assign(0.5);
 
     nd4j::ops::log_loss_grad op;
     auto results = op.execute({&predictions, &weights, &labels}, {1e-7}, {1});
 
     ASSERT_EQ(ND4J_STATUS_OK, results->status());
-        
+
     auto *dLdw = results->at(1);
     // dLdw->printIndexedBuffer();
     // dLdw->printShapeInfo();
@@ -193,11 +193,11 @@ TEST_F(DeclarableOpsTests11, log_loss_grad_test4) {
 
 ///////////////////////////////////////////////////////////////////
 TEST_F(DeclarableOpsTests11, log_loss_grad_test5) {
-    
+
     NDArray labels('c', {2,3,4}, nd4j::DataType::DOUBLE);
     NDArray predictions('c', {2,3,4}, nd4j::DataType::DOUBLE);
     NDArray weights('c', {2,3,4}, nd4j::DataType::DOUBLE);
-    
+
     NDArray dLdpExp('c', {2,3,4}, {-1.04166,-1.08696, -1.13636, -1.19048,-1.25   ,-1.31579, -1.38889, -1.47059,-1.5625 ,-1.66667, -1.78571, -1.92308,
                                    -2.08333,-2.27273, -2.5    , -2.77778,-3.125  ,-3.57143, -4.16667, -5.     ,-6.25   ,-8.33333,-12.49999,-24.99993});
     NDArray dLdwExp('c', {2,3,4}, {1.05912, 1.20488, 1.29964, 1.35815, 1.3871 , 1.39009, 1.36919, 1.32553, 1.25959, 1.17133, 1.06026, 0.92541,
@@ -207,14 +207,14 @@ TEST_F(DeclarableOpsTests11, log_loss_grad_test5) {
 
     predictions.linspace(0.04, 0.04);
     labels.linspace(1);
-    weights.assign(0.5);    
+    weights.assign(0.5);
 
     nd4j::ops::log_loss_grad op;
     auto results = op.execute({&predictions, &weights, &labels}, {1e-7}, {2});
 
     ASSERT_EQ(ND4J_STATUS_OK, results->status());
 
-    auto *dLdp = results->at(0);       
+    auto *dLdp = results->at(0);
     auto *dLdw = results->at(1);
     auto *dLdl = results->at(2);
 
@@ -230,16 +230,16 @@ TEST_F(DeclarableOpsTests11, log_loss_grad_test5) {
 
 ///////////////////////////////////////////////////////////////////
 TEST_F(DeclarableOpsTests11, log_loss_grad_test6) {
-    
+
     NDArray labels('c', {2,3,4}, nd4j::DataType::DOUBLE);
     NDArray predictions('c', {2,3,4}, nd4j::DataType::DOUBLE);
     NDArray weights('c', {1,3,1}, nd4j::DataType::DOUBLE);
-        
+
     NDArray dLdwExp('c', {1,3,1}, {6.73432, 2.46939,-9.20372});
-    
+
     predictions.linspace(0.04, 0.04);
     labels.linspace(1);
-    weights.assign(0.5);    
+    weights.assign(0.5);
 
     nd4j::ops::log_loss_grad op;
     auto results = op.execute({&predictions, &weights, &labels}, {1e-7}, {2});
@@ -247,7 +247,7 @@ TEST_F(DeclarableOpsTests11, log_loss_grad_test6) {
     ASSERT_EQ(ND4J_STATUS_OK, results->status());
 
     auto *dLdw = results->at(1);
-    
+
     ASSERT_TRUE(dLdwExp.isSameShape(dLdw));
     ASSERT_TRUE(dLdwExp.equalsTo(dLdw));
 
@@ -256,16 +256,16 @@ TEST_F(DeclarableOpsTests11, log_loss_grad_test6) {
 
 ///////////////////////////////////////////////////////////////////
 TEST_F(DeclarableOpsTests11, log_loss_grad_test7) {
-    
+
     NDArray labels('c', {2,3,4}, nd4j::DataType::DOUBLE);
     NDArray predictions('c', {2,3,4}, nd4j::DataType::DOUBLE);
     NDArray weights(nd4j::DataType::DOUBLE);
-        
+
     NDArray dLdwExp('c', {}, {0.});
-    
+
     predictions.linspace(0.04, 0.04);
     labels.linspace(1);
-    weights.assign(0.5);    
+    weights.assign(0.5);
 
     nd4j::ops::log_loss_grad op;
     auto results = op.execute({&predictions, &weights, &labels}, {1e-7}, {2});
@@ -273,20 +273,20 @@ TEST_F(DeclarableOpsTests11, log_loss_grad_test7) {
     ASSERT_EQ(ND4J_STATUS_OK, results->status());
 
     auto *dLdw = results->at(1);
-    
+
     ASSERT_TRUE(dLdwExp.isSameShape(dLdw));
     ASSERT_TRUE(dLdwExp.equalsTo(dLdw));
 
     delete results;
 }
- 
+
 ///////////////////////////////////////////////////////////////////
 TEST_F(DeclarableOpsTests11, log_loss_grad_test8) {
-    
+
     NDArray labels('c', {2,3,4}, nd4j::DataType::DOUBLE);
     NDArray predictions('c', {2,3,4}, nd4j::DataType::DOUBLE);
     NDArray weights('c', {2,3,4}, nd4j::DataType::DOUBLE);
-    
+
     NDArray dLdpExp('c', {2,3,4}, {0.     , 0.     ,  0.     ,  0.     ,-1.5    ,-1.57895, -1.66667, -1.76471,-1.875  ,-2.     , -2.14286, -2.30769,
                                   -2.5    ,-2.72727, -3.     , -3.33333,-3.75   ,-4.28571, -5.     , -6.     ,-7.49999,-9.99999,-14.99999,-29.99991});
     NDArray dLdwExp('c', {2,3,4}, {1.56625, 1.74117, 1.85487, 1.92509, 1.95982, 1.96341, 1.93833, 1.88594, 1.80682, 1.70091, 1.56762, 1.4058 ,
@@ -307,7 +307,7 @@ TEST_F(DeclarableOpsTests11, log_loss_grad_test8) {
 
     ASSERT_EQ(ND4J_STATUS_OK, results->status());
 
-    auto *dLdp = results->at(0);       
+    auto *dLdp = results->at(0);
     auto *dLdw = results->at(1);
     auto *dLdl = results->at(2);
 
@@ -323,11 +323,11 @@ TEST_F(DeclarableOpsTests11, log_loss_grad_test8) {
 
 ///////////////////////////////////////////////////////////////////
 TEST_F(DeclarableOpsTests11, log_loss_grad_test9) {
-    
+
     NDArray labels('c', {2,3,4}, nd4j::DataType::DOUBLE);
     NDArray predictions('c', {2,3,4}, nd4j::DataType::DOUBLE);
     NDArray weights('c', {2,3,4}, nd4j::DataType::DOUBLE);
-    
+
     NDArray dLdpExp('c', {2,3,4}, {-0.52083,-0.54348,-0.56818, -0.59524,-0.625  ,-0.65789,-0.69444, -0.73529,-0.78125,-0.83333,-0.89286, -0.96154,
                                    -1.04167,-1.13636,-1.25   , -1.38889,-1.5625 ,-1.78571,-2.08333, -2.5    ,-3.125  ,-4.16666,-6.24999,-12.49996});
     NDArray dLdwExp('c', {2,3,4}, {0.13412, 0.207  , 0.25438, 0.28364, 0.29811, 0.2996 , 0.28916, 0.26733, 0.23436, 0.19023, 0.13469, 0.06727,
@@ -338,13 +338,13 @@ TEST_F(DeclarableOpsTests11, log_loss_grad_test9) {
     predictions.linspace(0.04, 0.04);
     labels.linspace(1);
     weights.assign(0.5);
-    
+
     nd4j::ops::log_loss_grad op;
     auto results = op.execute({&predictions, &weights, &labels}, {1e-7}, {3});
 
     ASSERT_EQ(ND4J_STATUS_OK, results->status());
 
-    auto *dLdp = results->at(0);       
+    auto *dLdp = results->at(0);
     auto *dLdw = results->at(1);
     auto *dLdl = results->at(2);
 
@@ -357,10 +357,10 @@ TEST_F(DeclarableOpsTests11, log_loss_grad_test9) {
 
     delete results;
 }
- 
+
 ///////////////////////////////////////////////////////////////////
 TEST_F(DeclarableOpsTests11, log_loss_grad_test10) {
-    
+
     NDArray labels('c', {2,3,4}, nd4j::DataType::DOUBLE);
     NDArray predictions('c', {2,3,4}, nd4j::DataType::DOUBLE);
     NDArray weights('c', {1,1}, nd4j::DataType::DOUBLE);
@@ -375,8 +375,8 @@ TEST_F(DeclarableOpsTests11, log_loss_grad_test10) {
     auto results = op.execute({&predictions, &weights, &labels}, {1e-7}, {3});
 
     ASSERT_EQ(ND4J_STATUS_OK, results->status());
-        
-    auto *dLdw = results->at(1);    
+
+    auto *dLdw = results->at(1);
 
     ASSERT_TRUE(dLdwExp.isSameShape(dLdw));
     ASSERT_TRUE(dLdwExp.equalsTo(dLdw));
@@ -386,7 +386,7 @@ TEST_F(DeclarableOpsTests11, log_loss_grad_test10) {
 
 ///////////////////////////////////////////////////////////////////
 TEST_F(DeclarableOpsTests11, log_loss_grad_test11) {
-    
+
     NDArray labels('c', {2,3,4}, nd4j::DataType::DOUBLE);
     NDArray predictions('c', {2,3,4}, nd4j::DataType::DOUBLE);
     NDArray weights('c', {1,3,1}, nd4j::DataType::DOUBLE);
@@ -401,8 +401,8 @@ TEST_F(DeclarableOpsTests11, log_loss_grad_test11) {
     auto results = op.execute({&predictions, &weights, &labels}, {1e-7}, {3});
 
     ASSERT_EQ(ND4J_STATUS_OK, results->status());
-        
-    auto *dLdw = results->at(1);    
+
+    auto *dLdw = results->at(1);
 
     ASSERT_TRUE(dLdwExp.isSameShape(dLdw));
     ASSERT_TRUE(dLdwExp.equalsTo(dLdw));
@@ -412,11 +412,11 @@ TEST_F(DeclarableOpsTests11, log_loss_grad_test11) {
 
 ///////////////////////////////////////////////////////////////////
 TEST_F(DeclarableOpsTests11, log_loss_grad_test12) {
-    
+
     NDArray labels('c', {2,3,4}, nd4j::DataType::DOUBLE);
     NDArray predictions('c', {2,3,4}, nd4j::DataType::DOUBLE);
     NDArray weights('c', {2,3,4}, nd4j::DataType::DOUBLE);
-    
+
     NDArray dLdpExp('c', {2,3,4}, { 0.      , 0.      , 0.      ,  0.      ,-0.75    ,-0.789473,-0.833333, -0.882353,-0.9375  ,-1.      ,-1.071428, -1.153846,
                                 -1.25    ,-1.363636,-1.5     , -1.666666,-1.875   ,-2.142857,-2.499999, -2.999999,-3.749997,-4.999997,-7.499993,-14.999956});
     NDArray dLdwExp('c', {2,3,4}, {0.16094, 0.2484 , 0.30526, 0.34036, 0.35773, 0.35953, 0.34699, 0.32079, 0.28123, 0.22827, 0.16163, 0.08072,
@@ -433,15 +433,15 @@ TEST_F(DeclarableOpsTests11, log_loss_grad_test12) {
     weights.t<double>(2) = 0.;
     weights.t<double>(3) = 0.;
 
-    
+
     nd4j::ops::log_loss_grad op;
     auto results = op.execute({&predictions, &weights, &labels}, {1e-7}, {3});
 
     ASSERT_EQ(ND4J_STATUS_OK, results->status());
 
-    auto *dLdp = results->at(0);       
+    auto *dLdp = results->at(0);
     auto *dLdw = results->at(1);
-    auto *dLdl = results->at(2);    
+    auto *dLdl = results->at(2);
 
     ASSERT_TRUE(dLdpExp.isSameShape(dLdp));
     ASSERT_TRUE(dLdpExp.equalsTo(dLdp));
@@ -455,11 +455,11 @@ TEST_F(DeclarableOpsTests11, log_loss_grad_test12) {
 
 ///////////////////////////////////////////////////////////////////
 TEST_F(DeclarableOpsTests11, log_loss_grad_test13) {
-    
+
     NDArray labels('c', {2,3,4}, nd4j::DataType::DOUBLE);
     NDArray predictions('c', {2,3,4}, nd4j::DataType::DOUBLE);
     NDArray weights('c', {2,3,1}, nd4j::DataType::DOUBLE);
-    
+
     NDArray dLdpExp('c', {2,3,4}, {0.     , 0.     ,  0.     ,  0.     , 0.     , 0.     ,  0.     ,  0.     , 0.     , 0.     ,  0.     ,  0.     ,
                                   -2.08333,-2.27273, -2.5    , -2.77778,-3.125  ,-3.57143, -4.16667, -5.     ,-6.25   ,-8.33333,-12.49999,-24.99993});
     NDArray dLdwExp('c', {2,3,1}, {1.75828,  2.30839,  1.25309, -1.35098, -6.16602,-16.78383});
@@ -471,16 +471,16 @@ TEST_F(DeclarableOpsTests11, log_loss_grad_test13) {
     weights.assign(0.5);
     weights.t<double>(0) = 0.;
     weights.t<double>(1) = 0.;
-    weights.t<double>(2) = 0.;    
-    
+    weights.t<double>(2) = 0.;
+
     nd4j::ops::log_loss_grad op;
     auto results = op.execute({&predictions, &weights, &labels}, {1e-7}, {3});
 
     ASSERT_EQ(ND4J_STATUS_OK, results->status());
 
-    auto *dLdp = results->at(0);       
+    auto *dLdp = results->at(0);
     auto *dLdw = results->at(1);
-    auto *dLdl = results->at(2);    
+    auto *dLdl = results->at(2);
 
     ASSERT_TRUE(dLdpExp.isSameShape(dLdp));
     ASSERT_TRUE(dLdpExp.equalsTo(dLdp));
@@ -494,10 +494,10 @@ TEST_F(DeclarableOpsTests11, log_loss_grad_test13) {
 
 ///////////////////////////////////////////////////////////////////
 TEST_F(DeclarableOpsTests11, summaryStatsData_test1) {
-    
+
     functions::summarystats::SummaryStatsData<double> var1;
     functions::summarystats::SummaryStatsData<double> var2;
-    var2.n = var2.mean = var2.M2 = var2.M3 = var2.M4 = var2.bias = 5; 
+    var2.n = var2.mean = var2.M2 = var2.M3 = var2.M4 = var2.bias = 5;
 
     functions::summarystats::SummaryStatsData<double>* arr = new functions::summarystats::SummaryStatsData<double>[2];
     arr[0] = var1;
@@ -515,11 +515,11 @@ TEST_F(DeclarableOpsTests11, summaryStatsData_test1) {
 
 ///////////////////////////////////////////////////////////////////
 TEST_F(DeclarableOpsTests11, mean_sqerr_loss_grad_test1) {
-    
+
     NDArray labels('c', {2,3,4}, nd4j::DataType::DOUBLE);
     NDArray predictions('c', {2,3,4}, nd4j::DataType::DOUBLE);
     NDArray weights('c', {2,3,4}, nd4j::DataType::DOUBLE);
-    
+
     NDArray dLdpExp('c', {2,3,4}, {-0.96, -1.92, -2.88, -3.84, -4.8 , -5.76, -6.72, -7.68, -8.64, -9.6 ,-10.56,-11.52,
                                    -12.48,-13.44,-14.4 ,-15.36,-16.32,-17.28,-18.24,-19.2 ,-20.16,-21.12,-22.08,-23.04});
     NDArray dLdwExp('c', {2,3,4}, {0.9216 ,  3.6864 ,  8.2944 , 14.7456 , 23.04   , 33.1776 , 45.1584 , 58.9824 , 74.6496 , 92.16   ,111.51361,132.7104 ,
@@ -527,14 +527,14 @@ TEST_F(DeclarableOpsTests11, mean_sqerr_loss_grad_test1) {
 
     predictions.linspace(0.04, 0.04);
     labels.linspace(1);
-    weights.assign(0.5);    
+    weights.assign(0.5);
 
     nd4j::ops::mean_sqerr_loss_grad op;
     auto results = op.execute({&predictions, &weights, &labels}, {}, {0});
 
     ASSERT_EQ(ND4J_STATUS_OK, results->status());
 
-    auto dLdp = results->at(0);       
+    auto dLdp = results->at(0);
     auto dLdw = results->at(1);
     auto dLdl = results->at(2);
 
@@ -547,19 +547,19 @@ TEST_F(DeclarableOpsTests11, mean_sqerr_loss_grad_test1) {
 
     delete results;
 }
- 
+
 ///////////////////////////////////////////////////////////////////
 TEST_F(DeclarableOpsTests11, mean_sqerr_loss_grad_test2) {
-    
+
     NDArray labels('c', {2,3,4}, nd4j::DataType::DOUBLE);
     NDArray predictions('c', {2,3,4}, nd4j::DataType::DOUBLE);
     NDArray weights('c', {2,1,4}, nd4j::DataType::DOUBLE);
-    
+
     NDArray dLdwExp('c', {2,1,4}, {98.61121,129.024  , 164.9664 , 206.4384 , 828.51837,925.28644,1027.58398,1135.41113});
 
     predictions.linspace(0.04, 0.04);
     labels.linspace(1);
-    weights.assign(0.5);    
+    weights.assign(0.5);
 
     nd4j::ops::mean_sqerr_loss_grad op;
     auto results = op.execute({&predictions, &weights, &labels}, {}, {0});
@@ -567,7 +567,7 @@ TEST_F(DeclarableOpsTests11, mean_sqerr_loss_grad_test2) {
     ASSERT_EQ(ND4J_STATUS_OK, results->status());
 
     auto *dLdw = results->at(1);
-    
+
     ASSERT_TRUE(dLdwExp.isSameShape(dLdw));
     ASSERT_TRUE(dLdwExp.equalsTo(dLdw));
 
@@ -576,25 +576,25 @@ TEST_F(DeclarableOpsTests11, mean_sqerr_loss_grad_test2) {
 
 ///////////////////////////////////////////////////////////////////
 TEST_F(DeclarableOpsTests11, mean_sqerr_loss_grad_test3) {
-    
+
     NDArray labels('c', {2,3,4}, nd4j::DataType::DOUBLE);
     NDArray predictions('c', {2,3,4}, nd4j::DataType::DOUBLE);
     NDArray weights(nd4j::DataType::DOUBLE);
-    
+
     NDArray dLdpExp('c', {2,3,4}, {-0.96, -1.92, -2.88, -3.84, -4.8 , -5.76, -6.72, -7.68, -8.64, -9.6 ,-10.56,-11.52,
                                    -12.48,-13.44,-14.4 ,-15.36,-16.32,-17.28,-18.24,-19.2 ,-20.16,-21.12,-22.08,-23.04});
     NDArray dLdwExp('c', {}, {4515.84});
-    
+
     predictions.linspace(0.04, 0.04);
     labels.linspace(1);
-    weights.assign(0.5);    
+    weights.assign(0.5);
 
     nd4j::ops::mean_sqerr_loss_grad op;
     auto results = op.execute({&predictions, &weights, &labels}, {}, {1});
 
     ASSERT_EQ(ND4J_STATUS_OK, results->status());
 
-    auto *dLdp = results->at(0);       
+    auto *dLdp = results->at(0);
     auto *dLdw = results->at(1);
     auto *dLdl = results->at(2);
 
@@ -610,22 +610,22 @@ TEST_F(DeclarableOpsTests11, mean_sqerr_loss_grad_test3) {
 
 ///////////////////////////////////////////////////////////////////
 TEST_F(DeclarableOpsTests11, mean_sqerr_loss_grad_test4) {
-    
+
     NDArray labels('c', {2,3,4}, nd4j::DataType::DOUBLE);
     NDArray predictions('c', {2,3,4}, nd4j::DataType::DOUBLE);
     NDArray weights('c', {1,3,1}, nd4j::DataType::DOUBLE);
-    
+
     NDArray dLdwExp('c', {1,3,1}, {807.32153, 1426.63684, 2281.88159});
 
     predictions.linspace(0.04, 0.04);
     labels.linspace(1);
-    weights.assign(0.5);    
+    weights.assign(0.5);
 
     nd4j::ops::mean_sqerr_loss_grad op;
     auto results = op.execute({&predictions, &weights, &labels}, {}, {1});
 
     ASSERT_EQ(ND4J_STATUS_OK, results->status());
-        
+
     auto *dLdw = results->at(1);
 
     ASSERT_TRUE(dLdwExp.isSameShape(dLdw));
@@ -636,11 +636,11 @@ TEST_F(DeclarableOpsTests11, mean_sqerr_loss_grad_test4) {
 
 ///////////////////////////////////////////////////////////////////
 TEST_F(DeclarableOpsTests11, mean_sqerr_loss_grad_test5) {
-    
+
     NDArray labels('c', {2,3,4}, nd4j::DataType::DOUBLE);
     NDArray predictions('c', {2,3,4}, nd4j::DataType::DOUBLE);
     NDArray weights('c', {2,3,4}, nd4j::DataType::DOUBLE);
-    
+
     NDArray dLdpExp('c', {2,3,4}, {-0.08,-0.16,-0.24,-0.32,-0.4 ,-0.48,-0.56,-0.64,-0.72,-0.8 ,-0.88,-0.96,
                                    -1.04,-1.12,-1.2 ,-1.28,-1.36,-1.44,-1.52,-1.6 ,-1.68,-1.76,-1.84,-1.92});
     NDArray dLdwExp('c', {2,3,4}, {-15.6032,-15.3728,-14.9888,-14.4512,-13.76  ,-12.9152,-11.9168,-10.7648, -9.4592, -8.    , -6.3872, -4.6208,
@@ -648,14 +648,14 @@ TEST_F(DeclarableOpsTests11, mean_sqerr_loss_grad_test5) {
 
     predictions.linspace(0.04, 0.04);
     labels.linspace(1);
-    weights.assign(0.5);    
+    weights.assign(0.5);
 
     nd4j::ops::mean_sqerr_loss_grad op;
     auto results = op.execute({&predictions, &weights, &labels}, {}, {2});
 
     ASSERT_EQ(ND4J_STATUS_OK, results->status());
 
-    auto *dLdp = results->at(0);       
+    auto *dLdp = results->at(0);
     auto *dLdw = results->at(1);
     auto *dLdl = results->at(2);
 
@@ -671,16 +671,16 @@ TEST_F(DeclarableOpsTests11, mean_sqerr_loss_grad_test5) {
 
 ///////////////////////////////////////////////////////////////////
 TEST_F(DeclarableOpsTests11, mean_sqerr_loss_grad_test6) {
-    
+
     NDArray labels('c', {2,3,4}, nd4j::DataType::DOUBLE);
     NDArray predictions('c', {2,3,4}, nd4j::DataType::DOUBLE);
     NDArray weights('c', {1,3,1}, nd4j::DataType::DOUBLE);
-        
+
     NDArray dLdwExp('c', {1,3,1}, {-58.16319, -6.5536 , 64.71682});
-    
+
     predictions.linspace(0.04, 0.04);
     labels.linspace(1);
-    weights.assign(0.5);    
+    weights.assign(0.5);
 
     nd4j::ops::mean_sqerr_loss_grad op;
     auto results = op.execute({&predictions, &weights, &labels}, {}, {2});
@@ -688,7 +688,7 @@ TEST_F(DeclarableOpsTests11, mean_sqerr_loss_grad_test6) {
     ASSERT_EQ(ND4J_STATUS_OK, results->status());
 
     auto *dLdw = results->at(1);
-    
+
     ASSERT_TRUE(dLdwExp.isSameShape(dLdw));
     ASSERT_TRUE(dLdwExp.equalsTo(dLdw));
 
@@ -697,16 +697,16 @@ TEST_F(DeclarableOpsTests11, mean_sqerr_loss_grad_test6) {
 
 ///////////////////////////////////////////////////////////////////
 TEST_F(DeclarableOpsTests11, mean_sqerr_loss_grad_test7) {
-    
+
     NDArray labels('c', {2,3,4}, nd4j::DataType::DOUBLE);
     NDArray predictions('c', {2,3,4}, nd4j::DataType::DOUBLE);
     NDArray weights(nd4j::DataType::DOUBLE);
-        
+
     NDArray dLdwExp('c', {}, {0.});
-    
+
     predictions.linspace(0.04, 0.04);
     labels.linspace(1);
-    weights.assign(0.5);    
+    weights.assign(0.5);
 
     nd4j::ops::mean_sqerr_loss_grad op;
     auto results = op.execute({&predictions, &weights, &labels}, {}, {2});
@@ -714,20 +714,20 @@ TEST_F(DeclarableOpsTests11, mean_sqerr_loss_grad_test7) {
     ASSERT_EQ(ND4J_STATUS_OK, results->status());
 
     auto *dLdw = results->at(1);
-    
+
     ASSERT_TRUE(dLdwExp.isSameShape(dLdw));
     ASSERT_TRUE(dLdwExp.equalsTo(dLdw));
 
     delete results;
 }
- 
+
 ///////////////////////////////////////////////////////////////////
 TEST_F(DeclarableOpsTests11, mean_sqerr_loss_grad_test8) {
-    
+
     NDArray labels('c', {2,3,4}, nd4j::DataType::DOUBLE);
     NDArray predictions('c', {2,3,4}, nd4j::DataType::DOUBLE);
     NDArray weights('c', {2,3,4}, nd4j::DataType::DOUBLE);
-    
+
     NDArray dLdpExp('c', {2,3,4}, {0. ,0. ,0. ,0. ,-0.48 ,-0.576,-0.672,-0.768,-0.864,-0.96 ,-1.056,-1.152,
                                  -1.248,-1.344,-1.44 ,-1.536,-1.632,-1.728,-1.824,-1.92 ,-2.016,-2.112,-2.208,-2.304});
     NDArray dLdwExp('c', {2,3,4}, {-22.3488 ,-22.07232,-21.61152,-20.9664 ,-20.13696,-19.1232 ,-17.92512,-16.54272,-14.976  ,-13.22496,-11.2896 , -9.16992,
@@ -746,7 +746,7 @@ TEST_F(DeclarableOpsTests11, mean_sqerr_loss_grad_test8) {
 
     ASSERT_EQ(ND4J_STATUS_OK, results->status());
 
-    auto *dLdp = results->at(0);       
+    auto *dLdp = results->at(0);
     auto *dLdw = results->at(1);
     auto *dLdl = results->at(2);
 
@@ -762,11 +762,11 @@ TEST_F(DeclarableOpsTests11, mean_sqerr_loss_grad_test8) {
 
 ///////////////////////////////////////////////////////////////////
 TEST_F(DeclarableOpsTests11, mean_sqerr_loss_grad_test9) {
-    
+
     NDArray labels('c', {2,3,4}, nd4j::DataType::DOUBLE);
     NDArray predictions('c', {2,3,4}, nd4j::DataType::DOUBLE);
     NDArray weights('c', {2,3,4}, nd4j::DataType::DOUBLE);
-    
+
     NDArray dLdpExp('c', {2,3,4}, {-0.04,-0.08,-0.12,-0.16,-0.2 ,-0.24,-0.28,-0.32,-0.36,-0.4 ,-0.44,-0.48,
                                    -0.52,-0.56,-0.6 ,-0.64,-0.68,-0.72,-0.76,-0.8 ,-0.84,-0.88,-0.92,-0.96});
     NDArray dLdwExp('c', {2,3,4}, {0.0384, 0.1536, 0.3456, 0.6144, 0.96  , 1.3824, 1.8816, 2.4576, 3.1104, 3.84  , 4.6464, 5.5296,
@@ -775,13 +775,13 @@ TEST_F(DeclarableOpsTests11, mean_sqerr_loss_grad_test9) {
     predictions.linspace(0.04, 0.04);
     labels.linspace(1);
     weights.assign(0.5);
-    
+
     nd4j::ops::mean_sqerr_loss_grad op;
     auto results = op.execute({&predictions, &weights, &labels}, {}, {3});
 
     ASSERT_EQ(ND4J_STATUS_OK, results->status());
 
-    auto *dLdp = results->at(0);       
+    auto *dLdp = results->at(0);
     auto *dLdw = results->at(1);
     auto *dLdl = results->at(2);
 
@@ -797,7 +797,7 @@ TEST_F(DeclarableOpsTests11, mean_sqerr_loss_grad_test9) {
 
 ///////////////////////////////////////////////////////////////////
 TEST_F(DeclarableOpsTests11, mean_sqerr_loss_grad_test10) {
-    
+
     NDArray labels('c', {2,3,4}, nd4j::DataType::DOUBLE);
     NDArray predictions('c', {2,3,4}, nd4j::DataType::DOUBLE);
     NDArray weights('c', {1,1}, nd4j::DataType::DOUBLE);
@@ -812,8 +812,8 @@ TEST_F(DeclarableOpsTests11, mean_sqerr_loss_grad_test10) {
     auto results = op.execute({&predictions, &weights, &labels}, {}, {3});
 
     ASSERT_EQ(ND4J_STATUS_OK, results->status());
-        
-    auto *dLdw = results->at(1);    
+
+    auto *dLdw = results->at(1);
 
     ASSERT_TRUE(dLdwExp.isSameShape(dLdw));
     ASSERT_TRUE(dLdwExp.equalsTo(dLdw));
@@ -823,7 +823,7 @@ TEST_F(DeclarableOpsTests11, mean_sqerr_loss_grad_test10) {
 
 ///////////////////////////////////////////////////////////////////
 TEST_F(DeclarableOpsTests11, mean_sqerr_loss_grad_test11) {
-    
+
     NDArray labels('c', {2,3,4}, nd4j::DataType::DOUBLE);
     NDArray predictions('c', {2,3,4}, nd4j::DataType::DOUBLE);
     NDArray weights('c', {1,3,1}, nd4j::DataType::DOUBLE);
@@ -838,8 +838,8 @@ TEST_F(DeclarableOpsTests11, mean_sqerr_loss_grad_test11) {
     auto results = op.execute({&predictions, &weights, &labels}, {}, {3});
 
     ASSERT_EQ(ND4J_STATUS_OK, results->status());
-        
-    auto *dLdw = results->at(1);    
+
+    auto *dLdw = results->at(1);
 
     ASSERT_TRUE(dLdwExp.isSameShape(dLdw));
     ASSERT_TRUE(dLdwExp.equalsTo(dLdw));
@@ -849,11 +849,11 @@ TEST_F(DeclarableOpsTests11, mean_sqerr_loss_grad_test11) {
 
 ///////////////////////////////////////////////////////////////////
 TEST_F(DeclarableOpsTests11, mean_sqerr_loss_grad_test12) {
-    
+
     NDArray labels('c', {2,3,4}, nd4j::DataType::DOUBLE);
     NDArray predictions('c', {2,3,4}, nd4j::DataType::DOUBLE);
     NDArray weights('c', {2,3,4}, nd4j::DataType::DOUBLE);
-    
+
     NDArray dLdpExp('c', {2,3,4}, {0.,0.,0.,0., -0.24 ,-0.288,-0.336,-0.384,-0.432,-0.48 ,-0.528,-0.576,
                                   -0.624,-0.672,-0.72 ,-0.768,-0.816,-0.864,-0.912,-0.96 ,-1.008,-1.056,-1.104,-1.152});
     NDArray dLdwExp('c', {2,3,4}, {0.04608, 0.18432, 0.41472, 0.73728, 1.152  , 1.65888, 2.25792, 2.94912, 3.73248, 4.608  , 5.57568, 6.63552,
@@ -866,15 +866,15 @@ TEST_F(DeclarableOpsTests11, mean_sqerr_loss_grad_test12) {
     weights.t<double>(1) = 0.;
     weights.t<double>(2) = 0.;
     weights.t<double>(3) = 0.;
-    
+
     nd4j::ops::mean_sqerr_loss_grad op;
     auto results = op.execute({&predictions, &weights, &labels}, {}, {3});
 
     ASSERT_EQ(ND4J_STATUS_OK, results->status());
 
-    auto *dLdp = results->at(0);       
+    auto *dLdp = results->at(0);
     auto *dLdw = results->at(1);
-    auto *dLdl = results->at(2);    
+    auto *dLdl = results->at(2);
 
     ASSERT_TRUE(dLdpExp.isSameShape(dLdp));
     ASSERT_TRUE(dLdpExp.equalsTo(dLdp));
@@ -888,11 +888,11 @@ TEST_F(DeclarableOpsTests11, mean_sqerr_loss_grad_test12) {
 
 ///////////////////////////////////////////////////////////////////
 TEST_F(DeclarableOpsTests11, mean_sqerr_loss_grad_test13) {
-    
+
     NDArray labels('c', {2,3,4}, nd4j::DataType::DOUBLE);
     NDArray predictions('c', {2,3,4}, nd4j::DataType::DOUBLE);
     NDArray weights('c', {2,3,1}, nd4j::DataType::DOUBLE);
-    
+
     NDArray dLdpExp('c', {2,3,4}, {0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
                                   -1.04,-1.12,-1.2 ,-1.28,-1.36,-1.44,-1.52,-1.6 ,-1.68,-1.76,-1.84,-1.92});
     NDArray dLdwExp('c', {2,3,1}, {2.304  , 13.3632 , 34.2528 , 64.97279,105.5232 ,155.90401});
@@ -902,16 +902,16 @@ TEST_F(DeclarableOpsTests11, mean_sqerr_loss_grad_test13) {
     weights.assign(0.5);
     weights.t<double>(0) = 0.;
     weights.t<double>(1) = 0.;
-    weights.t<double>(2) = 0.;    
-    
+    weights.t<double>(2) = 0.;
+
     nd4j::ops::mean_sqerr_loss_grad op;
     auto results = op.execute({&predictions, &weights, &labels}, {}, {3});
 
     ASSERT_EQ(ND4J_STATUS_OK, results->status());
 
-    auto *dLdp = results->at(0);       
+    auto *dLdp = results->at(0);
     auto *dLdw = results->at(1);
-    auto *dLdl = results->at(2);    
+    auto *dLdl = results->at(2);
 
     ASSERT_TRUE(dLdpExp.isSameShape(dLdp));
     ASSERT_TRUE(dLdpExp.equalsTo(dLdp));
@@ -963,11 +963,11 @@ TEST_F(DeclarableOpsTests11, SquaredSubtractTest_Test3) {
 
 ///////////////////////////////////////////////////////////////////
 TEST_F(DeclarableOpsTests11, absolute_difference_loss_grad_test1) {
-    
+
     NDArray labels('c', {2,3,4}, nd4j::DataType::DOUBLE);
     NDArray predictions('c', {2,3,4}, nd4j::DataType::DOUBLE);
     NDArray weights('c', {2,3,4}, nd4j::DataType::DOUBLE);
-    
+
     NDArray dLdpExp('c', {2,3,4}, {-0.5,-0.5,-0.5,-0.5,-0.5,-0.5,-0.5,-0.5,-0.5,-0.5,-0.5,-0.5,
                                    -0.5,-0.5,-0.5,-0.5,-0.5,-0.5,-0.5,-0.5,-0.5,-0.5,-0.5,-0.5});
     NDArray dLdwExp('c', {2,3,4}, {0.96, 1.92, 2.88, 3.84, 4.8 , 5.76, 6.72, 7.68, 8.64, 9.6 ,10.56,11.52,
@@ -975,14 +975,14 @@ TEST_F(DeclarableOpsTests11, absolute_difference_loss_grad_test1) {
 
     predictions.linspace(0.04, 0.04);
     labels.linspace(1);
-    weights.assign(0.5);    
+    weights.assign(0.5);
 
     nd4j::ops::absolute_difference_loss_grad op;
     auto results = op.execute({&predictions, &weights, &labels}, {}, {0});
 
     ASSERT_EQ(ND4J_STATUS_OK, results->status());
 
-    auto dLdp = results->at(0);       
+    auto dLdp = results->at(0);
     auto dLdw = results->at(1);
     auto dLdl = results->at(2);
 
@@ -995,19 +995,19 @@ TEST_F(DeclarableOpsTests11, absolute_difference_loss_grad_test1) {
 
     delete results;
 }
- 
+
 ///////////////////////////////////////////////////////////////////
 TEST_F(DeclarableOpsTests11, absolute_difference_loss_grad_test2) {
-    
+
     NDArray labels('c', {2,3,4}, nd4j::DataType::DOUBLE);
     NDArray predictions('c', {2,3,4}, nd4j::DataType::DOUBLE);
     NDArray weights('c', {2,1,4}, nd4j::DataType::DOUBLE);
-    
+
     NDArray dLdwExp('c', {2,1,4}, {14.4 , 17.28, 20.16, 23.04, 48.96, 51.84, 54.72, 57.6});
 
     predictions.linspace(0.04, 0.04);
     labels.linspace(1);
-    weights.assign(0.5);    
+    weights.assign(0.5);
 
     nd4j::ops::absolute_difference_loss_grad op;
     auto results = op.execute({&predictions, &weights, &labels}, {}, {0});
@@ -1015,7 +1015,7 @@ TEST_F(DeclarableOpsTests11, absolute_difference_loss_grad_test2) {
     ASSERT_EQ(ND4J_STATUS_OK, results->status());
 
     auto *dLdw = results->at(1);
-    
+
     ASSERT_TRUE(dLdwExp.isSameShape(dLdw));
     ASSERT_TRUE(dLdwExp.equalsTo(dLdw));
 
@@ -1024,25 +1024,25 @@ TEST_F(DeclarableOpsTests11, absolute_difference_loss_grad_test2) {
 
 ///////////////////////////////////////////////////////////////////
 TEST_F(DeclarableOpsTests11, absolute_difference_loss_grad_test3) {
-    
+
     NDArray labels('c', {2,3,4}, nd4j::DataType::DOUBLE);
     NDArray predictions('c', {2,3,4}, nd4j::DataType::DOUBLE);
     NDArray weights(nd4j::DataType::DOUBLE);
-    
+
     NDArray dLdpExp('c', {2,3,4}, {-0.5,-0.5,-0.5,-0.5,-0.5,-0.5,-0.5,-0.5,-0.5,-0.5,-0.5,-0.5,
                                    -0.5,-0.5,-0.5,-0.5,-0.5,-0.5,-0.5,-0.5,-0.5,-0.5,-0.5,-0.5});
     NDArray dLdwExp('c', {}, {288.});
-    
+
     predictions.linspace(0.04, 0.04);
     labels.linspace(1);
-    weights.assign(0.5);    
+    weights.assign(0.5);
 
     nd4j::ops::absolute_difference_loss_grad op;
     auto results = op.execute({&predictions, &weights, &labels}, {}, {1});
 
     ASSERT_EQ(ND4J_STATUS_OK, results->status());
 
-    auto *dLdp = results->at(0);       
+    auto *dLdp = results->at(0);
     auto *dLdw = results->at(1);
     auto *dLdl = results->at(2);
 
@@ -1058,22 +1058,22 @@ TEST_F(DeclarableOpsTests11, absolute_difference_loss_grad_test3) {
 
 ///////////////////////////////////////////////////////////////////
 TEST_F(DeclarableOpsTests11, absolute_difference_loss_grad_test4) {
-    
+
     NDArray labels('c', {2,3,4}, nd4j::DataType::DOUBLE);
     NDArray predictions('c', {2,3,4}, nd4j::DataType::DOUBLE);
     NDArray weights('c', {1,3,1}, nd4j::DataType::DOUBLE);
-    
+
     NDArray dLdwExp('c', {1,3,1}, {65.28, 96., 126.72001});
 
     predictions.linspace(0.04, 0.04);
     labels.linspace(1);
-    weights.assign(0.5);    
+    weights.assign(0.5);
 
     nd4j::ops::absolute_difference_loss_grad op;
     auto results = op.execute({&predictions, &weights, &labels}, {}, {1});
 
     ASSERT_EQ(ND4J_STATUS_OK, results->status());
-        
+
     auto *dLdw = results->at(1);
 
     ASSERT_TRUE(dLdwExp.isSameShape(dLdw));
@@ -1084,11 +1084,11 @@ TEST_F(DeclarableOpsTests11, absolute_difference_loss_grad_test4) {
 
 ///////////////////////////////////////////////////////////////////
 TEST_F(DeclarableOpsTests11, absolute_difference_loss_grad_test5) {
-    
+
     NDArray labels('c', {2,3,4}, nd4j::DataType::DOUBLE);
     NDArray predictions('c', {2,3,4}, nd4j::DataType::DOUBLE);
     NDArray weights('c', {2,3,4}, nd4j::DataType::DOUBLE);
-    
+
     NDArray dLdpExp('c', {2,3,4}, {-0.04167,-0.04167,-0.04167,-0.04167,-0.04167,-0.04167,-0.04167,-0.04167,-0.04167,-0.04167,-0.04167,-0.04167,
                                    -0.04167,-0.04167,-0.04167,-0.04167,-0.04167,-0.04167,-0.04167,-0.04167,-0.04167,-0.04167,-0.04167,-0.04167});
     NDArray dLdwExp('c', {2,3,4}, {-0.92,-0.84,-0.76,-0.68,-0.6 ,-0.52,-0.44,-0.36,-0.28,-0.2 ,-0.12,-0.04,
@@ -1096,14 +1096,14 @@ TEST_F(DeclarableOpsTests11, absolute_difference_loss_grad_test5) {
 
     predictions.linspace(0.04, 0.04);
     labels.linspace(1);
-    weights.assign(0.5);    
+    weights.assign(0.5);
 
     nd4j::ops::absolute_difference_loss_grad op;
     auto results = op.execute({&predictions, &weights, &labels}, {}, {2});
 
     ASSERT_EQ(ND4J_STATUS_OK, results->status());
 
-    auto *dLdp = results->at(0);       
+    auto *dLdp = results->at(0);
     auto *dLdw = results->at(1);
     auto *dLdl = results->at(2);
 
@@ -1119,16 +1119,16 @@ TEST_F(DeclarableOpsTests11, absolute_difference_loss_grad_test5) {
 
 ///////////////////////////////////////////////////////////////////
 TEST_F(DeclarableOpsTests11, absolute_difference_loss_grad_test6) {
-    
+
     NDArray labels('c', {2,3,4}, nd4j::DataType::DOUBLE);
     NDArray predictions('c', {2,3,4}, nd4j::DataType::DOUBLE);
     NDArray weights('c', {1,3,1}, nd4j::DataType::DOUBLE);
-        
+
     NDArray dLdwExp('c', {1,3,1}, {-2.56, 0., 2.56});
-    
+
     predictions.linspace(0.04, 0.04);
     labels.linspace(1);
-    weights.assign(0.5);    
+    weights.assign(0.5);
 
     nd4j::ops::absolute_difference_loss_grad op;
     auto results = op.execute({&predictions, &weights, &labels}, {}, {2});
@@ -1136,7 +1136,7 @@ TEST_F(DeclarableOpsTests11, absolute_difference_loss_grad_test6) {
     ASSERT_EQ(ND4J_STATUS_OK, results->status());
 
     auto *dLdw = results->at(1);
-    
+
     ASSERT_TRUE(dLdwExp.isSameShape(dLdw));
     ASSERT_TRUE(dLdwExp.equalsTo(dLdw));
 
@@ -1145,16 +1145,16 @@ TEST_F(DeclarableOpsTests11, absolute_difference_loss_grad_test6) {
 
 ///////////////////////////////////////////////////////////////////
 TEST_F(DeclarableOpsTests11, absolute_difference_loss_grad_test7) {
-    
+
     NDArray labels('c', {2,3,4}, nd4j::DataType::DOUBLE);
     NDArray predictions('c', {2,3,4}, nd4j::DataType::DOUBLE);
     NDArray weights(nd4j::DataType::DOUBLE);
-        
+
     NDArray dLdwExp('c', {}, {0.});
-    
+
     predictions.linspace(0.04, 0.04);
     labels.linspace(1);
-    weights.assign(0.5);    
+    weights.assign(0.5);
 
     nd4j::ops::absolute_difference_loss_grad op;
     auto results = op.execute({&predictions, &weights, &labels}, {}, {2});
@@ -1162,20 +1162,20 @@ TEST_F(DeclarableOpsTests11, absolute_difference_loss_grad_test7) {
     ASSERT_EQ(ND4J_STATUS_OK, results->status());
 
     auto *dLdw = results->at(1);
-    
+
     ASSERT_TRUE(dLdwExp.isSameShape(dLdw));
     ASSERT_TRUE(dLdwExp.equalsTo(dLdw));
 
     delete results;
 }
- 
+
 ///////////////////////////////////////////////////////////////////
 TEST_F(DeclarableOpsTests11, absolute_difference_loss_grad_test8) {
-    
+
     NDArray labels('c', {2,3,4}, nd4j::DataType::DOUBLE);
     NDArray predictions('c', {2,3,4}, nd4j::DataType::DOUBLE);
     NDArray weights('c', {2,3,4}, nd4j::DataType::DOUBLE);
-    
+
     NDArray dLdpExp('c', {2,3,4}, {-0.  ,-0.  ,-0.  ,-0.  ,-0.05,-0.05,-0.05,-0.05,-0.05,-0.05,-0.05,-0.05,
                                    -0.05,-0.05,-0.05,-0.05,-0.05,-0.05,-0.05,-0.05,-0.05,-0.05,-0.05,-0.05});
     NDArray dLdwExp('c', {2,3,4}, {-1.296,-1.2  ,-1.104,-1.008,-0.912,-0.816,-0.72 ,-0.624,-0.528,-0.432,-0.336,-0.24 ,
@@ -1194,7 +1194,7 @@ TEST_F(DeclarableOpsTests11, absolute_difference_loss_grad_test8) {
 
     ASSERT_EQ(ND4J_STATUS_OK, results->status());
 
-    auto *dLdp = results->at(0);       
+    auto *dLdp = results->at(0);
     auto *dLdw = results->at(1);
     auto *dLdl = results->at(2);
 
@@ -1210,11 +1210,11 @@ TEST_F(DeclarableOpsTests11, absolute_difference_loss_grad_test8) {
 
 ///////////////////////////////////////////////////////////////////
 TEST_F(DeclarableOpsTests11, absolute_difference_loss_grad_test9) {
-    
+
     NDArray labels('c', {2,3,4}, nd4j::DataType::DOUBLE);
     NDArray predictions('c', {2,3,4}, nd4j::DataType::DOUBLE);
     NDArray weights('c', {2,3,4}, nd4j::DataType::DOUBLE);
-    
+
     NDArray dLdpExp('c', {2,3,4}, {-0.02083, -0.02083, -0.02083, -0.02083,-0.02083, -0.02083, -0.02083, -0.02083,-0.02083, -0.02083, -0.02083, -0.02083,
                                    -0.02083, -0.02083, -0.02083, -0.02083,-0.02083, -0.02083, -0.02083, -0.02083,-0.02083, -0.02083, -0.02083, -0.02083});
     NDArray dLdwExp('c', {2,3,4}, {0.04, 0.08, 0.12, 0.16, 0.2 , 0.24, 0.28, 0.32,0.36, 0.4 , 0.44, 0.48,
@@ -1223,13 +1223,13 @@ TEST_F(DeclarableOpsTests11, absolute_difference_loss_grad_test9) {
     predictions.linspace(0.04, 0.04);
     labels.linspace(1);
     weights.assign(0.5);
-    
+
     nd4j::ops::absolute_difference_loss_grad op;
     auto results = op.execute({&predictions, &weights, &labels}, {}, {3});
 
     ASSERT_EQ(ND4J_STATUS_OK, results->status());
 
-    auto *dLdp = results->at(0);       
+    auto *dLdp = results->at(0);
     auto *dLdw = results->at(1);
     auto *dLdl = results->at(2);
 
@@ -1245,7 +1245,7 @@ TEST_F(DeclarableOpsTests11, absolute_difference_loss_grad_test9) {
 
 ///////////////////////////////////////////////////////////////////
 TEST_F(DeclarableOpsTests11, absolute_difference_loss_grad_test10) {
-    
+
     NDArray labels('c', {2,3,4}, nd4j::DataType::DOUBLE);
     NDArray predictions('c', {2,3,4}, nd4j::DataType::DOUBLE);
     NDArray weights('c', {1,1}, nd4j::DataType::DOUBLE);
@@ -1260,8 +1260,8 @@ TEST_F(DeclarableOpsTests11, absolute_difference_loss_grad_test10) {
     auto results = op.execute({&predictions, &weights, &labels}, {}, {3});
 
     ASSERT_EQ(ND4J_STATUS_OK, results->status());
-        
-    auto *dLdw = results->at(1);    
+
+    auto *dLdw = results->at(1);
 
     ASSERT_TRUE(dLdwExp.isSameShape(dLdw));
     ASSERT_TRUE(dLdwExp.equalsTo(dLdw));
@@ -1271,7 +1271,7 @@ TEST_F(DeclarableOpsTests11, absolute_difference_loss_grad_test10) {
 
 ///////////////////////////////////////////////////////////////////
 TEST_F(DeclarableOpsTests11, absolute_difference_loss_grad_test11) {
-    
+
     NDArray labels('c', {2,3,4}, nd4j::DataType::DOUBLE);
     NDArray predictions('c', {2,3,4}, nd4j::DataType::DOUBLE);
     NDArray weights('c', {1,3,1}, nd4j::DataType::DOUBLE);
@@ -1286,8 +1286,8 @@ TEST_F(DeclarableOpsTests11, absolute_difference_loss_grad_test11) {
     auto results = op.execute({&predictions, &weights, &labels}, {}, {3});
 
     ASSERT_EQ(ND4J_STATUS_OK, results->status());
-        
-    auto *dLdw = results->at(1);    
+
+    auto *dLdw = results->at(1);
 
     ASSERT_TRUE(dLdwExp.isSameShape(dLdw));
     ASSERT_TRUE(dLdwExp.equalsTo(dLdw));
@@ -1297,11 +1297,11 @@ TEST_F(DeclarableOpsTests11, absolute_difference_loss_grad_test11) {
 
 ///////////////////////////////////////////////////////////////////
 TEST_F(DeclarableOpsTests11, absolute_difference_loss_grad_test12) {
-    
+
     NDArray labels('c', {2,3,4}, nd4j::DataType::DOUBLE);
     NDArray predictions('c', {2,3,4}, nd4j::DataType::DOUBLE);
     NDArray weights('c', {2,3,4}, nd4j::DataType::DOUBLE);
-    
+
     NDArray dLdpExp('c', {2,3,4}, {0., 0., 0., 0., -0.025, -0.025, -0.025, -0.025,-0.025, -0.025, -0.025, -0.025,
                                    -0.025, -0.025, -0.025, -0.025,-0.025, -0.025, -0.025, -0.025,-0.025, -0.025, -0.025, -0.025});
     NDArray dLdwExp('c', {2,3,4}, {0.048, 0.096, 0.144, 0.192,0.24 , 0.288, 0.336, 0.384,0.432, 0.48 , 0.528, 0.576,
@@ -1314,15 +1314,15 @@ TEST_F(DeclarableOpsTests11, absolute_difference_loss_grad_test12) {
     weights.t<double>(1) = 0.;
     weights.t<double>(2) = 0.;
     weights.t<double>(3) = 0.;
-    
+
     nd4j::ops::absolute_difference_loss_grad op;
     auto results = op.execute({&predictions, &weights, &labels}, {}, {3});
 
     ASSERT_EQ(ND4J_STATUS_OK, results->status());
 
-    auto *dLdp = results->at(0);       
+    auto *dLdp = results->at(0);
     auto *dLdw = results->at(1);
-    auto *dLdl = results->at(2);    
+    auto *dLdl = results->at(2);
 
     ASSERT_TRUE(dLdpExp.isSameShape(dLdp));
     ASSERT_TRUE(dLdpExp.equalsTo(dLdp));
@@ -1336,11 +1336,11 @@ TEST_F(DeclarableOpsTests11, absolute_difference_loss_grad_test12) {
 
 ///////////////////////////////////////////////////////////////////
 TEST_F(DeclarableOpsTests11, absolute_difference_loss_grad_test13) {
-    
+
     NDArray labels('c', {2,3,4}, nd4j::DataType::DOUBLE);
     NDArray predictions('c', {2,3,4}, nd4j::DataType::DOUBLE);
     NDArray weights('c', {2,3,1}, nd4j::DataType::DOUBLE);
-    
+
     NDArray dLdpExp('c', {2,3,4}, {0., 0., 0., 0., 0., 0., 0., 0.,0., 0., 0., 0.,
                                   -0.04167, -0.04167, -0.04167, -0.04167,-0.04167, -0.04167, -0.04167, -0.04167,-0.04167, -0.04167, -0.04167, -0.04167});
     NDArray dLdwExp('c', {2,3,1}, {0.8 ,2.08,3.36,4.64,5.92,7.2 });
@@ -1350,16 +1350,16 @@ TEST_F(DeclarableOpsTests11, absolute_difference_loss_grad_test13) {
     weights.assign(0.5);
     weights.t<double>(0) = 0.;
     weights.t<double>(1) = 0.;
-    weights.t<double>(2) = 0.;    
-    
+    weights.t<double>(2) = 0.;
+
     nd4j::ops::absolute_difference_loss_grad op;
     auto results = op.execute({&predictions, &weights, &labels}, {}, {3});
 
     ASSERT_EQ(ND4J_STATUS_OK, results->status());
 
-    auto *dLdp = results->at(0);       
+    auto *dLdp = results->at(0);
     auto *dLdw = results->at(1);
-    auto *dLdl = results->at(2);    
+    auto *dLdl = results->at(2);
 
     ASSERT_TRUE(dLdpExp.isSameShape(dLdp));
     ASSERT_TRUE(dLdpExp.equalsTo(dLdp));
@@ -1373,11 +1373,11 @@ TEST_F(DeclarableOpsTests11, absolute_difference_loss_grad_test13) {
 
 ///////////////////////////////////////////////////////////////////
 TEST_F(DeclarableOpsTests11, BFloat16_Test_1) {
-    
+
     NDArray x = NDArrayFactory::create<bfloat16>('c', {2,3,4});
     NDArray y = NDArrayFactory::create<bfloat16>('c', {2,3,4});//('c', {2,3,4}, nd4j::DataType::BFLOAT16);
     NDArray exp = NDArrayFactory::create<bfloat16>('c', {2,3,4});//('c', {2,3,4}, nd4j::DataType::BFLOAT16);
-    
+
     x.linspace(1);
     y.linspace(1);
     exp.linspace(2,2);
@@ -1385,7 +1385,7 @@ TEST_F(DeclarableOpsTests11, BFloat16_Test_1) {
     auto results = op.execute({&x, &y}, {}, {});
 
     ASSERT_EQ(ND4J_STATUS_OK, results->status());
-        
+
     auto res = results->at(0);
     res->printIndexedBuffer("BFloat16 sum:");
     ASSERT_TRUE(res->equalsTo(exp));
@@ -1439,11 +1439,11 @@ TEST_F(DeclarableOpsTests11, BFloat16_Test_3) {
 
 ///////////////////////////////////////////////////////////////////
 TEST_F(DeclarableOpsTests11, sigm_cross_entropy_loss_grad_test1) {
-    
+
     NDArray labels('c', {2,3,4}, nd4j::DataType::DOUBLE);
     NDArray logits('c', {2,3,4}, nd4j::DataType::DOUBLE);
     NDArray weights('c', {2,3,4}, nd4j::DataType::DOUBLE);
-    
+
     NDArray dLdpExp('c', {2,3,4}, {-0.25999, -0.755  , -1.25   , -1.745  , -2.24001, -2.73502, -3.23004, -3.72508, -4.22014, -4.71523, -5.21034, -5.70548,
                                    -6.20066, -6.69587, -7.19113, -7.68643, -8.18177, -8.67717, -9.17262, -9.66813,-10.1637 ,-10.65932,-11.15501,-11.65077});
     NDArray dLdwExp('c', {2,3,4}, {0.73395,  0.75335,  0.69315,  0.55335,  0.33395,  0.03495, -0.34366, -0.80186, -1.33967, -1.95708, -2.65411, -3.43074,
@@ -1453,14 +1453,14 @@ TEST_F(DeclarableOpsTests11, sigm_cross_entropy_loss_grad_test1) {
 
     logits.linspace(-0.08, 0.04);
     labels.linspace(1);
-    weights.assign(0.5);    
+    weights.assign(0.5);
 
     nd4j::ops::sigm_cross_entropy_loss_grad op;
     auto results = op.execute({&logits, &weights, &labels}, {0.}, {0});
 
     ASSERT_EQ(ND4J_STATUS_OK, results->status());
 
-    auto *dLdp = results->at(0);       
+    auto *dLdp = results->at(0);
     auto *dLdw = results->at(1);
     auto *dLdl = results->at(2);
 
@@ -1473,14 +1473,14 @@ TEST_F(DeclarableOpsTests11, sigm_cross_entropy_loss_grad_test1) {
 
     delete results;
 }
- 
+
 ///////////////////////////////////////////////////////////////////
 TEST_F(DeclarableOpsTests11, sigm_cross_entropy_loss_grad_test2) {
-    
+
     NDArray labels('c', {2,3,4}, nd4j::DataType::DOUBLE);
     NDArray logits('c', {2,3,4}, nd4j::DataType::DOUBLE);
     NDArray weights('c', {2,1,4}, nd4j::DataType::DOUBLE);
-    
+
     NDArray dLdpExp('c', {2,3,4}, {-0.18499,-0.53   ,-0.875  ,-1.22   ,-1.56501,-1.91002,-2.25504,-2.60008,-2.94514,-3.29023,-3.63534,-3.98048,
                                    -4.32566,-4.67087,-5.01613,-5.36143,-5.70677,-6.05217,-6.39762,-6.74313,-7.0887 ,-7.43432,-7.78001,-8.12577});
     NDArray dLdwExp('c', {2,1,4}, {0.43622, -0.19079, -0.98462, -1.94525,-18.09855,-20.72768,-23.52373,-26.48669});
@@ -1489,14 +1489,14 @@ TEST_F(DeclarableOpsTests11, sigm_cross_entropy_loss_grad_test2) {
 
     logits.linspace(-0.08, 0.04);
     labels.linspace(1);
-    weights.assign(0.5);    
+    weights.assign(0.5);
 
     nd4j::ops::sigm_cross_entropy_loss_grad op;
     auto results = op.execute({&logits, &weights, &labels}, {0.3}, {0});
 
     ASSERT_EQ(ND4J_STATUS_OK, results->status());
 
-    auto *dLdp = results->at(0);       
+    auto *dLdp = results->at(0);
     auto *dLdw = results->at(1);
     auto *dLdl = results->at(2);
 
@@ -1512,11 +1512,11 @@ TEST_F(DeclarableOpsTests11, sigm_cross_entropy_loss_grad_test2) {
 
 ///////////////////////////////////////////////////////////////////
 TEST_F(DeclarableOpsTests11, sigm_cross_entropy_loss_grad_test3) {
-    
+
     NDArray labels('c', {2,3,4}, nd4j::DataType::DOUBLE);
     NDArray logits('c', {2,3,4}, nd4j::DataType::DOUBLE);
     NDArray weights(nd4j::DataType::DOUBLE);
-    
+
     NDArray dLdpExp('c', {2,3,4}, {-0.18499,-0.53   ,-0.875  ,-1.22   ,-1.56501,-1.91002,-2.25504,-2.60008,-2.94514,-3.29023,-3.63534,-3.98048,
                                    -4.32566,-4.67087,-5.01613,-5.36143,-5.70677,-6.05217,-6.39762,-6.74313,-7.0887 ,-7.43432,-7.78001,-8.12577});
     NDArray dLdwExp('c', {}, {-91.52109});
@@ -1525,14 +1525,14 @@ TEST_F(DeclarableOpsTests11, sigm_cross_entropy_loss_grad_test3) {
 
     logits.linspace(-0.08, 0.04);
     labels.linspace(1);
-    weights.assign(0.5);    
+    weights.assign(0.5);
 
     nd4j::ops::sigm_cross_entropy_loss_grad op;
     auto results = op.execute({&logits, &weights, &labels}, {0.3}, {1});
 
     ASSERT_EQ(ND4J_STATUS_OK, results->status());
 
-    auto *dLdp = results->at(0);       
+    auto *dLdp = results->at(0);
     auto *dLdw = results->at(1);
     auto *dLdl = results->at(2);
 
@@ -1548,22 +1548,22 @@ TEST_F(DeclarableOpsTests11, sigm_cross_entropy_loss_grad_test3) {
 
 ///////////////////////////////////////////////////////////////////
 TEST_F(DeclarableOpsTests11, sigm_cross_entropy_loss_grad_test4) {
-    
+
     NDArray labels('c', {2,3,4}, nd4j::DataType::DOUBLE);
     NDArray logits('c', {2,3,4}, nd4j::DataType::DOUBLE);
     NDArray weights('c', {1,3,1}, nd4j::DataType::DOUBLE);
-    
+
     NDArray dLdwExp('c', {1,3,1}, {-12.54779,-28.13393,-50.83936});
-  
+
     logits.linspace(-0.08, 0.04);
     labels.linspace(1);
-    weights.assign(0.5);    
+    weights.assign(0.5);
 
     nd4j::ops::sigm_cross_entropy_loss_grad op;
     auto results = op.execute({&logits, &weights, &labels}, {0.3}, {1});
 
     ASSERT_EQ(ND4J_STATUS_OK, results->status());
-        
+
     auto *dLdw = results->at(1);
 
     ASSERT_TRUE(dLdwExp.isSameShape(dLdw));
@@ -1574,11 +1574,11 @@ TEST_F(DeclarableOpsTests11, sigm_cross_entropy_loss_grad_test4) {
 
 ///////////////////////////////////////////////////////////////////
 TEST_F(DeclarableOpsTests11, sigm_cross_entropy_loss_grad_test5) {
-    
+
     NDArray labels('c', {2,3,4}, nd4j::DataType::DOUBLE);
     NDArray logits('c', {2,3,4}, nd4j::DataType::DOUBLE);
     NDArray weights('c', {2,3,4}, nd4j::DataType::DOUBLE);
-    
+
     NDArray dLdpExp('c', {2,3,4}, {-0.01542,-0.04417,-0.07292,-0.10167,-0.13042,-0.15917,-0.18792,-0.21667,-0.24543,-0.27419,-0.30294,-0.33171,
                                    -0.36047,-0.38924,-0.41801,-0.44679,-0.47556,-0.50435,-0.53314,-0.56193,-0.59072,-0.61953,-0.64833,-0.67715});
     NDArray dLdwExp('c', {2,3,4}, {0.37794, 0.37906, 0.37554, 0.36739, 0.35461, 0.33719, 0.31514, 0.28846, 0.25714, 0.22119, 0.18061, 0.13539,
@@ -1588,14 +1588,14 @@ TEST_F(DeclarableOpsTests11, sigm_cross_entropy_loss_grad_test5) {
 
     logits.linspace(-0.08, 0.04);
     labels.linspace(1);
-    weights.assign(0.5);    
+    weights.assign(0.5);
 
     nd4j::ops::sigm_cross_entropy_loss_grad op;
     auto results = op.execute({&logits, &weights, &labels}, {0.3}, {2});
 
     ASSERT_EQ(ND4J_STATUS_OK, results->status());
 
-    auto *dLdp = results->at(0);       
+    auto *dLdp = results->at(0);
     auto *dLdw = results->at(1);
     auto *dLdl = results->at(2);
 
@@ -1611,16 +1611,16 @@ TEST_F(DeclarableOpsTests11, sigm_cross_entropy_loss_grad_test5) {
 
 ///////////////////////////////////////////////////////////////////
 TEST_F(DeclarableOpsTests11, sigm_cross_entropy_loss_grad_test6) {
-    
+
     NDArray labels('c', {2,3,4}, nd4j::DataType::DOUBLE);
     NDArray logits('c', {2,3,4}, nd4j::DataType::DOUBLE);
     NDArray weights('c', {1,3,1}, nd4j::DataType::DOUBLE);
-        
+
     NDArray dLdwExp('c', {1,3,1}, {1.4966 , 0.19776,-1.69436});
 
     logits.linspace(-0.08, 0.04);
     labels.linspace(1);
-    weights.assign(0.5);    
+    weights.assign(0.5);
 
     nd4j::ops::sigm_cross_entropy_loss_grad op;
     auto results = op.execute({&logits, &weights, &labels}, {0.3}, {2});
@@ -1628,7 +1628,7 @@ TEST_F(DeclarableOpsTests11, sigm_cross_entropy_loss_grad_test6) {
     ASSERT_EQ(ND4J_STATUS_OK, results->status());
 
     auto *dLdw = results->at(1);
-    
+
     ASSERT_TRUE(dLdwExp.isSameShape(dLdw));
     ASSERT_TRUE(dLdwExp.equalsTo(dLdw));
 
@@ -1637,16 +1637,16 @@ TEST_F(DeclarableOpsTests11, sigm_cross_entropy_loss_grad_test6) {
 
 ///////////////////////////////////////////////////////////////////
 TEST_F(DeclarableOpsTests11, sigm_cross_entropy_loss_grad_test7) {
-    
+
     NDArray labels('c', {2,3,4}, nd4j::DataType::DOUBLE);
     NDArray logits('c', {2,3,4}, nd4j::DataType::DOUBLE);
     NDArray weights(nd4j::DataType::DOUBLE);
-        
+
     NDArray dLdwExp('c', {}, {0.});
-    
+
     logits.linspace(-0.08, 0.04);
     labels.linspace(1);
-    weights.assign(0.5);    
+    weights.assign(0.5);
 
     nd4j::ops::sigm_cross_entropy_loss_grad op;
     auto results = op.execute({&logits, &weights, &labels}, {0.3}, {2});
@@ -1654,20 +1654,20 @@ TEST_F(DeclarableOpsTests11, sigm_cross_entropy_loss_grad_test7) {
     ASSERT_EQ(ND4J_STATUS_OK, results->status());
 
     auto *dLdw = results->at(1);
-    
+
     ASSERT_TRUE(dLdwExp.isSameShape(dLdw));
     ASSERT_TRUE(dLdwExp.equalsTo(dLdw));
 
     delete results;
 }
- 
+
 ///////////////////////////////////////////////////////////////////
 TEST_F(DeclarableOpsTests11, sigm_cross_entropy_loss_grad_test8) {
-    
+
     NDArray labels('c', {2,3,4}, nd4j::DataType::DOUBLE);
     NDArray logits('c', {2,3,4}, nd4j::DataType::DOUBLE);
     NDArray weights('c', {2,3,4}, nd4j::DataType::DOUBLE);
-    
+
     NDArray dLdpExp('c', {2,3,4}, { 0.     , 0.     , 0.     , 0.     ,-0.1565 ,-0.191  ,-0.2255 ,-0.26001,-0.29451,-0.32902,-0.36353,-0.39805,
                                    -0.43257,-0.46709,-0.50161,-0.53614,-0.57068,-0.60522,-0.63976,-0.67431,-0.70887,-0.74343,-0.778  ,-0.81258});
     NDArray dLdwExp('c', {2,3,4}, {0.54353, 0.54487, 0.54065, 0.53087, 0.51553, 0.49463, 0.46817, 0.43615, 0.39857, 0.35543, 0.30672, 0.25246,
@@ -1687,7 +1687,7 @@ TEST_F(DeclarableOpsTests11, sigm_cross_entropy_loss_grad_test8) {
 
     ASSERT_EQ(ND4J_STATUS_OK, results->status());
 
-    auto *dLdp = results->at(0);       
+    auto *dLdp = results->at(0);
     auto *dLdw = results->at(1);
     auto *dLdl = results->at(2);
 
@@ -1703,11 +1703,11 @@ TEST_F(DeclarableOpsTests11, sigm_cross_entropy_loss_grad_test8) {
 
 ///////////////////////////////////////////////////////////////////
 TEST_F(DeclarableOpsTests11, sigm_cross_entropy_loss_grad_test9) {
-    
+
     NDArray labels('c', {2,3,4}, nd4j::DataType::DOUBLE);
     NDArray logits('c', {2,3,4}, nd4j::DataType::DOUBLE);
     NDArray weights('c', {2,3,4}, nd4j::DataType::DOUBLE);
-    
+
     NDArray dLdpExp('c', {2,3,4}, {-0.00771, -0.02208, -0.03646, -0.05083,-0.06521, -0.07958, -0.09396, -0.10834,-0.12271, -0.13709, -0.15147, -0.16585,
                                    -0.18024, -0.19462, -0.20901, -0.22339,-0.23778, -0.25217, -0.26657, -0.28096,-0.29536, -0.30976, -0.32417, -0.33857});
     NDArray dLdwExp('c', {2,3,4}, {0.03008,  0.03064,  0.02888,  0.02481, 0.01841,  0.00971, -0.00132, -0.01466,-0.03032, -0.0483 , -0.06859, -0.0912 ,
@@ -1717,13 +1717,13 @@ TEST_F(DeclarableOpsTests11, sigm_cross_entropy_loss_grad_test9) {
     logits.linspace(-0.08, 0.04);
     labels.linspace(1);
     weights.assign(0.5);
-    
+
     nd4j::ops::sigm_cross_entropy_loss_grad op;
     auto results = op.execute({&logits, &weights, &labels}, {0.3}, {3});
 
     ASSERT_EQ(ND4J_STATUS_OK, results->status());
 
-    auto *dLdp = results->at(0);       
+    auto *dLdp = results->at(0);
     auto *dLdw = results->at(1);
     auto *dLdl = results->at(2);
 
@@ -1736,10 +1736,10 @@ TEST_F(DeclarableOpsTests11, sigm_cross_entropy_loss_grad_test9) {
 
     delete results;
 }
- 
+
 ///////////////////////////////////////////////////////////////////
 TEST_F(DeclarableOpsTests11, sigm_cross_entropy_loss_grad_test10) {
-    
+
     NDArray labels('c', {2,3,4}, nd4j::DataType::DOUBLE);
     NDArray logits('c', {2,3,4}, nd4j::DataType::DOUBLE);
     NDArray weights('c', {1,1}, nd4j::DataType::DOUBLE);
@@ -1754,8 +1754,8 @@ TEST_F(DeclarableOpsTests11, sigm_cross_entropy_loss_grad_test10) {
     auto results = op.execute({&logits, &weights, &labels}, {0.3}, {3});
 
     ASSERT_EQ(ND4J_STATUS_OK, results->status());
-        
-    auto *dLdw = results->at(1);    
+
+    auto *dLdw = results->at(1);
 
     ASSERT_TRUE(dLdwExp.isSameShape(dLdw));
     ASSERT_TRUE(dLdwExp.equalsTo(dLdw));
@@ -1765,7 +1765,7 @@ TEST_F(DeclarableOpsTests11, sigm_cross_entropy_loss_grad_test10) {
 
 ///////////////////////////////////////////////////////////////////
 TEST_F(DeclarableOpsTests11, sigm_cross_entropy_loss_grad_test11) {
-    
+
     NDArray labels('c', {2,3,4}, nd4j::DataType::DOUBLE);
     NDArray logits('c', {2,3,4}, nd4j::DataType::DOUBLE);
     NDArray weights('c', {1,3,1}, nd4j::DataType::DOUBLE);
@@ -1780,8 +1780,8 @@ TEST_F(DeclarableOpsTests11, sigm_cross_entropy_loss_grad_test11) {
     auto results = op.execute({&logits, &weights, &labels}, {0.3}, {3});
 
     ASSERT_EQ(ND4J_STATUS_OK, results->status());
-        
-    auto *dLdw = results->at(1);    
+
+    auto *dLdw = results->at(1);
 
     ASSERT_TRUE(dLdwExp.isSameShape(dLdw));
     ASSERT_TRUE(dLdwExp.equalsTo(dLdw));
@@ -1791,11 +1791,11 @@ TEST_F(DeclarableOpsTests11, sigm_cross_entropy_loss_grad_test11) {
 
 ///////////////////////////////////////////////////////////////////
 TEST_F(DeclarableOpsTests11, sigm_cross_entropy_loss_grad_test12) {
-    
+
     NDArray labels('c', {2,3,4}, nd4j::DataType::DOUBLE);
     NDArray logits('c', {2,3,4}, nd4j::DataType::DOUBLE);
     NDArray weights('c', {2,3,4}, nd4j::DataType::DOUBLE);
-    
+
     NDArray dLdpExp('c', {2,3,4}, {0.     ,  0.     ,  0.     ,  0.     ,-0.07825, -0.0955 , -0.11275, -0.13   ,-0.14726, -0.16451, -0.18177, -0.19902,
                                    -0.21628, -0.23354, -0.25081, -0.26807,-0.28534, -0.30261, -0.31988, -0.33716,-0.35443, -0.37172, -0.389  , -0.40629});
     NDArray dLdwExp('c', {2,3,4}, {0.0361 ,  0.03677,  0.03466,  0.02977, 0.0221 ,  0.01165, -0.00158, -0.01759,-0.03638, -0.05795, -0.08231, -0.10944,
@@ -1810,15 +1810,15 @@ TEST_F(DeclarableOpsTests11, sigm_cross_entropy_loss_grad_test12) {
     weights.t<double>(2) = 0.;
     weights.t<double>(3) = 0.;
 
-    
+
     nd4j::ops::sigm_cross_entropy_loss_grad op;
     auto results = op.execute({&logits, &weights, &labels}, {0.3}, {3});
 
     ASSERT_EQ(ND4J_STATUS_OK, results->status());
 
-    auto *dLdp = results->at(0);       
+    auto *dLdp = results->at(0);
     auto *dLdw = results->at(1);
-    auto *dLdl = results->at(2);    
+    auto *dLdl = results->at(2);
 
     ASSERT_TRUE(dLdpExp.isSameShape(dLdp));
     ASSERT_TRUE(dLdpExp.equalsTo(dLdp));
@@ -1832,11 +1832,11 @@ TEST_F(DeclarableOpsTests11, sigm_cross_entropy_loss_grad_test12) {
 
 ///////////////////////////////////////////////////////////////////
 TEST_F(DeclarableOpsTests11, sigm_cross_entropy_loss_grad_test13) {
-    
+
     NDArray labels('c', {2,3,4}, nd4j::DataType::DOUBLE);
     NDArray logits('c', {2,3,4}, nd4j::DataType::DOUBLE);
     NDArray weights('c', {2,3,1}, nd4j::DataType::DOUBLE);
-    
+
     NDArray dLdpExp('c', {2,3,4}, {0.     ,  0.     ,  0.     ,  0.     , 0.     ,  0.     ,  0.     ,  0.     , 0.     ,  0.     ,  0.     ,  0.     ,
                                    -0.36047, -0.38924, -0.41801, -0.44679,-0.47556, -0.50435, -0.53314, -0.56193,-0.59072, -0.61953, -0.64833, -0.67715});
     NDArray dLdwExp('c', {2,3,1}, {0.22882, 0.02428,-0.4768 ,-1.27447,-2.36878,-3.75981,});
@@ -1847,16 +1847,16 @@ TEST_F(DeclarableOpsTests11, sigm_cross_entropy_loss_grad_test13) {
     weights.assign(0.5);
     weights.t<double>(0) = 0.;
     weights.t<double>(1) = 0.;
-    weights.t<double>(2) = 0.;    
-    
+    weights.t<double>(2) = 0.;
+
     nd4j::ops::sigm_cross_entropy_loss_grad op;
     auto results = op.execute({&logits, &weights, &labels}, {0.3}, {3});
 
     ASSERT_EQ(ND4J_STATUS_OK, results->status());
 
-    auto *dLdp = results->at(0);       
+    auto *dLdp = results->at(0);
     auto *dLdw = results->at(1);
-    auto *dLdl = results->at(2);    
+    auto *dLdl = results->at(2);
 
     ASSERT_TRUE(dLdpExp.isSameShape(dLdp));
     ASSERT_TRUE(dLdpExp.equalsTo(dLdp));
@@ -1940,61 +1940,61 @@ TEST_F(DeclarableOpsTests11, softmax_cross_entropy_loss_grad_test1) {
     NDArray labels('c', {2,4}, {0,0,1,0, 0,1,0,0}, nd4j::DataType::INT32);
     NDArray logits('c', {2,4}, nd4j::DataType::DOUBLE);
     NDArray weights('c', {2}, nd4j::DataType::DOUBLE);
-    
+
     NDArray dLdpExp('c', {2,4}, {0.1176,  0.1224, -0.3726,  0.1326, 0.1176, -0.3776,  0.1274,  0.1326});
     NDArray dLdwExp('c', {2}, {1.36729, 1.40729});
 
     logits.linspace(-0.08, 0.04);
-    weights.assign(0.5);    
+    weights.assign(0.5);
 
     nd4j::ops::softmax_cross_entropy_loss_grad op;
 
-    auto results = op.execute({&logits, &weights, &labels}, {0.}, {0});    
-    
+    auto results = op.execute({&logits, &weights, &labels}, {0.}, {0});
+
     ASSERT_EQ(ND4J_STATUS_OK, results->status());
 
-    auto *dLdp = results->at(0);       
+    auto *dLdp = results->at(0);
     auto *dLdw = results->at(1);
-    auto *dLdl = results->at(2);    
+    auto *dLdl = results->at(2);
 
     ASSERT_TRUE(dLdpExp.isSameShape(dLdp));
     ASSERT_TRUE(dLdpExp.equalsTo(dLdp));
     ASSERT_TRUE(dLdwExp.isSameShape(dLdw));
-    ASSERT_TRUE(dLdwExp.equalsTo(dLdw));    
+    ASSERT_TRUE(dLdwExp.equalsTo(dLdw));
 
     delete results;
 }
- 
+
 /////////////////////////////////////////////////////////////////
 TEST_F(DeclarableOpsTests11, softmax_cross_entropy_loss_grad_test2) {
 
     NDArray labels('c', {4}, {0,0,1,0}, nd4j::DataType::INT32);
     NDArray logits('c', {4}, nd4j::DataType::DOUBLE);
     NDArray weights('c', {1}, nd4j::DataType::DOUBLE);
-    
+
     NDArray dLdpExp('c', {4}, {0.125,  0.125, -0.375,  0.125});
     NDArray dLdwExp('c', {1}, {1.38629});
 
     logits = 2.;
-    weights.assign(0.5);    
+    weights.assign(0.5);
 
     nd4j::ops::softmax_cross_entropy_loss_grad op;
 
     auto results = op.execute({&logits, &weights, &labels}, {0.}, {1});
-    
+
     ASSERT_EQ(ND4J_STATUS_OK, results->status());
 
-    auto *dLdp = results->at(0);       
+    auto *dLdp = results->at(0);
     auto *dLdw = results->at(1);
-    auto *dLdl = results->at(2);    
+    auto *dLdl = results->at(2);
 
     ASSERT_TRUE(dLdpExp.isSameShape(dLdp));
     ASSERT_TRUE(dLdpExp.equalsTo(dLdp));
     ASSERT_TRUE(dLdwExp.isSameShape(dLdw));
-    ASSERT_TRUE(dLdwExp.equalsTo(dLdw));    
+    ASSERT_TRUE(dLdwExp.equalsTo(dLdw));
 
     delete results;
-}   
+}
 
 /////////////////////////////////////////////////////////////////
 TEST_F(DeclarableOpsTests11, softmax_cross_entropy_loss_grad_test3) {
@@ -2002,30 +2002,30 @@ TEST_F(DeclarableOpsTests11, softmax_cross_entropy_loss_grad_test3) {
     NDArray labels('c', {4}, {0,0,1,0}, nd4j::DataType::INT32);
     NDArray logits('c', {4}, nd4j::DataType::DOUBLE);
     NDArray weights('c', {}, {0}, nd4j::DataType::DOUBLE);
-    
+
     NDArray dLdpExp('c', {4}, {0.125,  0.125, -0.375,  0.125});
     NDArray dLdwExp('c', {}, {1.38629});
 
     logits = 2.;
-    weights.assign(0.5);    
+    weights.assign(0.5);
 
     nd4j::ops::softmax_cross_entropy_loss_grad op;
 
     auto results = op.execute({&logits, &weights, &labels}, {0.}, {1});
-    
+
     ASSERT_EQ(ND4J_STATUS_OK, results->status());
 
-    auto *dLdp = results->at(0);       
+    auto *dLdp = results->at(0);
     auto *dLdw = results->at(1);
-    auto *dLdl = results->at(2);    
+    auto *dLdl = results->at(2);
 
     ASSERT_TRUE(dLdpExp.isSameShape(dLdp));
     ASSERT_TRUE(dLdpExp.equalsTo(dLdp));
     ASSERT_TRUE(dLdwExp.isSameShape(dLdw));
-    ASSERT_TRUE(dLdwExp.equalsTo(dLdw));    
+    ASSERT_TRUE(dLdwExp.equalsTo(dLdw));
 
     delete results;
-}   
+}
 
 /////////////////////////////////////////////////////////////////
 TEST_F(DeclarableOpsTests11, softmax_cross_entropy_loss_grad_test4) {
@@ -2033,30 +2033,30 @@ TEST_F(DeclarableOpsTests11, softmax_cross_entropy_loss_grad_test4) {
     NDArray labels('c', {4}, {0,0,1,0}, nd4j::DataType::INT32);
     NDArray logits('c', {4}, nd4j::DataType::DOUBLE);
     NDArray weights('c', {}, {0}, nd4j::DataType::DOUBLE);
-    
+
     NDArray dLdpExp('c', {4}, {0.23521,  0.2448 , -0.7452 ,  0.26519});
     NDArray dLdwExp('c', {}, {0.});
 
     logits.linspace(-0.08, 0.04);
-    weights = 0.5;    
+    weights = 0.5;
 
     nd4j::ops::softmax_cross_entropy_loss_grad op;
 
     auto results = op.execute({&logits, &weights, &labels}, {0.}, {2});
-    
+
     ASSERT_EQ(ND4J_STATUS_OK, results->status());
 
-    auto *dLdp = results->at(0);       
+    auto *dLdp = results->at(0);
     auto *dLdw = results->at(1);
-    auto *dLdl = results->at(2);    
+    auto *dLdl = results->at(2);
 
     ASSERT_TRUE(dLdpExp.isSameShape(dLdp));
     ASSERT_TRUE(dLdpExp.equalsTo(dLdp));
     ASSERT_TRUE(dLdwExp.isSameShape(dLdw));
-    ASSERT_TRUE(dLdwExp.equalsTo(dLdw));    
+    ASSERT_TRUE(dLdwExp.equalsTo(dLdw));
 
     delete results;
-}   
+}
 
 /////////////////////////////////////////////////////////////////
 TEST_F(DeclarableOpsTests11, softmax_cross_entropy_loss_grad_test5) {
@@ -2064,30 +2064,30 @@ TEST_F(DeclarableOpsTests11, softmax_cross_entropy_loss_grad_test5) {
     NDArray labels('c', {4}, {0,0,1,0}, nd4j::DataType::INT32);
     NDArray logits('c', {4}, nd4j::DataType::DOUBLE);
     NDArray weights('c', {1}, nd4j::DataType::DOUBLE);
-    
+
     NDArray dLdpExp('c', {4}, {0.1176,  0.1224, -0.3726,  0.1326});
     NDArray dLdwExp('c', {1}, {1.36729});
 
     logits.linspace(-0.08, 0.04);
-    weights = 0.5;    
+    weights = 0.5;
 
     nd4j::ops::softmax_cross_entropy_loss_grad op;
 
     auto results = op.execute({&logits, &weights, &labels}, {0.}, {3});
-    
+
     ASSERT_EQ(ND4J_STATUS_OK, results->status());
 
-    auto *dLdp = results->at(0);       
+    auto *dLdp = results->at(0);
     auto *dLdw = results->at(1);
-    auto *dLdl = results->at(2);    
+    auto *dLdl = results->at(2);
 
     ASSERT_TRUE(dLdpExp.isSameShape(dLdp));
     ASSERT_TRUE(dLdpExp.equalsTo(dLdp));
     ASSERT_TRUE(dLdwExp.isSameShape(dLdw));
-    ASSERT_TRUE(dLdwExp.equalsTo(dLdw));    
+    ASSERT_TRUE(dLdwExp.equalsTo(dLdw));
 
     delete results;
-}   
+}
 
 /////////////////////////////////////////////////////////////////
 TEST_F(DeclarableOpsTests11, softmax_cross_entropy_loss_grad_test6) {
@@ -2095,7 +2095,7 @@ TEST_F(DeclarableOpsTests11, softmax_cross_entropy_loss_grad_test6) {
     NDArray labels('c', {2,4}, {0,0,1,0, 0,1,0,0}, nd4j::DataType::INT32);
     NDArray logits('c', {2,4}, nd4j::DataType::DOUBLE);
     NDArray weights('c', {2}, nd4j::DataType::DOUBLE);
-    
+
     NDArray dLdpExp('c', {2,4}, {0.0801,  0.0849, -0.2601,  0.0951, 0.0801, -0.2651,  0.0899,  0.0951});
     NDArray dLdwExp('c', {2}, {-0.014000, 0.014000});
 
@@ -2105,12 +2105,12 @@ TEST_F(DeclarableOpsTests11, softmax_cross_entropy_loss_grad_test6) {
     nd4j::ops::softmax_cross_entropy_loss_grad op;
 
     auto results = op.execute({&logits, &weights, &labels}, {0.3}, {2});
-    
+
     ASSERT_EQ(ND4J_STATUS_OK, results->status());
 
-    auto *dLdp = results->at(0);       
+    auto *dLdp = results->at(0);
     auto *dLdw = results->at(1);
-    auto *dLdl = results->at(2);    
+    auto *dLdl = results->at(2);
 
     ASSERT_TRUE(dLdpExp.isSameShape(dLdp));
     ASSERT_TRUE(dLdpExp.equalsTo(dLdp));
@@ -2126,27 +2126,27 @@ TEST_F(DeclarableOpsTests11, softmax_cross_entropy_loss_grad_test7) {
     NDArray labels('c', {2,3,4}, {1,0,0,0, 0,1,0,0, 0,0,1,0, 0,0,0,1, 1,0,0,0, 0,1,0,0}, nd4j::DataType::INT32);
     NDArray logits('c', {2,3,4}, nd4j::DataType::DOUBLE);
     NDArray weights('c', {1,3}, {0.5, 0., 1.5});
-    
+
     NDArray dLdpExp('c', {2,3,4}, {-0.0956 , 0.0306 , 0.03185, 0.03315, 0.,-0., 0., 0., 0.0882 , 0.0918 ,-0.27945, 0.09945,
                                    0.0294 , 0.0306 , 0.03185,-0.09185,-0., 0., 0., 0., 0.0882 ,-0.2832 , 0.09555, 0.09945});
     NDArray dLdwExp('c', {1,3}, {0.69365, 0.71365, 0.69365});
 
-    logits.linspace(-0.08, 0.04);    
+    logits.linspace(-0.08, 0.04);
 
     nd4j::ops::softmax_cross_entropy_loss_grad op;
 
     auto results = op.execute({&logits, &weights, &labels}, {0.}, {3});
-    
+
     ASSERT_EQ(ND4J_STATUS_OK, results->status());
 
-    auto *dLdp = results->at(0);       
+    auto *dLdp = results->at(0);
     auto *dLdw = results->at(1);
-    auto *dLdl = results->at(2);    
-    
+    auto *dLdl = results->at(2);
+
     ASSERT_TRUE(dLdpExp.isSameShape(dLdp));
     ASSERT_TRUE(dLdpExp.equalsTo(dLdp));
     ASSERT_TRUE(dLdwExp.isSameShape(dLdw));
-    ASSERT_TRUE(dLdwExp.equalsTo(dLdw));    
+    ASSERT_TRUE(dLdwExp.equalsTo(dLdw));
 
     delete results;
 }
@@ -2157,40 +2157,40 @@ TEST_F(DeclarableOpsTests11, softmax_cross_entropy_loss_grad_test8) {
     NDArray labels('c', {2,3,4,5}, {1,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,1,1,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,
                                     0,0,1,0,0,0,0,0,1,1,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,1,1,0,0,0,0,0,1,
                                     0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,1,1,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,1,0}, nd4j::DataType::INT32);
-                                    
+
     NDArray logits('c', {2,3,4,5}, nd4j::DataType::DOUBLE);
     NDArray weights('c', {1,1,4}, nd4j::DataType::DOUBLE);
-    
+
     NDArray dLdpExp('c', {2,3,4,5}, {-0.03399, 0.00799, 0.00832, 0.00866, 0.00901, 0.00768,-0.03367, 0.00832, 0.00866, 0.00901, 0.00768, 0.00799,-0.03335,
-                                    0.00866, 0.00901, 0.00768, 0.00799, 0.00832,-0.03301, 0.00901, 0.00768, 0.00799, 0.00832, 0.00866,-0.03265,-0.03399, 
-                                    0.00799, 0.00832, 0.00866, 0.00901, 0.00768,-0.03367, 0.00832, 0.00866, 0.00901, 0.00768, 0.00799,-0.03335, 0.00866, 
-                                    0.00901, 0.00768, 0.00799, 0.00832,-0.03301, 0.00901, 0.00768, 0.00799, 0.00832, 0.00866,-0.03265,-0.03399, 0.00799, 
-                                    0.00832, 0.00866, 0.00901, 0.00768,-0.03367, 0.00832, 0.00866, 0.00901, 0.00768, 0.00799,-0.03335, 0.00866, 0.00901, 
-                                    0.00768, 0.00799, 0.00832,-0.03301, 0.00901, 0.00768, 0.00799, 0.00832, 0.00866,-0.03265,-0.03399, 0.00799, 0.00832, 
-                                    0.00866, 0.00901, 0.00768,-0.03367, 0.00832, 0.00866, 0.00901, 0.00768, 0.00799,-0.03335, 0.00866, 0.00901, 0.00768, 
-                                    0.00799, 0.00832,-0.03301, 0.00901, 0.00768, 0.00799, 0.00832, 0.00866,-0.03265,-0.03399, 0.00799, 0.00832, 0.00866, 
+                                    0.00866, 0.00901, 0.00768, 0.00799, 0.00832,-0.03301, 0.00901, 0.00768, 0.00799, 0.00832, 0.00866,-0.03265,-0.03399,
+                                    0.00799, 0.00832, 0.00866, 0.00901, 0.00768,-0.03367, 0.00832, 0.00866, 0.00901, 0.00768, 0.00799,-0.03335, 0.00866,
+                                    0.00901, 0.00768, 0.00799, 0.00832,-0.03301, 0.00901, 0.00768, 0.00799, 0.00832, 0.00866,-0.03265,-0.03399, 0.00799,
+                                    0.00832, 0.00866, 0.00901, 0.00768,-0.03367, 0.00832, 0.00866, 0.00901, 0.00768, 0.00799,-0.03335, 0.00866, 0.00901,
+                                    0.00768, 0.00799, 0.00832,-0.03301, 0.00901, 0.00768, 0.00799, 0.00832, 0.00866,-0.03265,-0.03399, 0.00799, 0.00832,
+                                    0.00866, 0.00901, 0.00768,-0.03367, 0.00832, 0.00866, 0.00901, 0.00768, 0.00799,-0.03335, 0.00866, 0.00901, 0.00768,
+                                    0.00799, 0.00832,-0.03301, 0.00901, 0.00768, 0.00799, 0.00832, 0.00866,-0.03265,-0.03399, 0.00799, 0.00832, 0.00866,
                                     0.00901, 0.00768,-0.03367, 0.00832, 0.00866, 0.00901, 0.00768, 0.00799,-0.03335, 0.00866, 0.00901, 0.00768, 0.00799, 0.00832,-0.03301, 0.00901});
 
     NDArray dLdwExp('c', {1,1,4}, {0.005,  0.00167, -0.00167, -0.005});
-    logits.linspace(-0.08, 0.04);    
-    weights.assign(0.5);    
+    logits.linspace(-0.08, 0.04);
+    weights.assign(0.5);
 
     nd4j::ops::softmax_cross_entropy_loss_grad op;
 
     auto results = op.execute({&logits, &weights, &labels}, {0.}, {2});
-    
+
     ASSERT_EQ(ND4J_STATUS_OK, results->status());
 
-    auto *dLdp = results->at(0);       
+    auto *dLdp = results->at(0);
     auto *dLdw = results->at(1);
     auto *dLdl = results->at(2);
 
-    // dLdp->printIndexedBuffer(); 
+    // dLdp->printIndexedBuffer();
 
     // ASSERT_TRUE(dLdpExp.isSameShape(dLdp));
     // ASSERT_TRUE(dLdpExp.equalsTo(dLdp));
     ASSERT_TRUE(dLdwExp.isSameShape(dLdw));
-    ASSERT_TRUE(dLdwExp.equalsTo(dLdw));    
+    ASSERT_TRUE(dLdwExp.equalsTo(dLdw));
 
     delete results;
 }
@@ -2212,19 +2212,19 @@ TEST_F(DeclarableOpsTests11, softmaxCrossEntropyWithLogits_grad_test1) {
 
     NDArray labels('c', {2,3,4}, {1,0,0,0, 0,1,0,0, 0,0,1,0, 0,0,0,1, 1,0,0,0, 0,1,0,0});
     NDArray logits('c', {2,3,4}, nd4j::DataType::DOUBLE);
-    
+
     NDArray dLdpExp('c', {2,3,4}, {-0.76479, 0.2448, 0.2548, 0.26519, 0.23521,-0.7552, 0.2548, 0.26519, 0.23521, 0.2448,-0.7452, 0.26519,
                                    0.23521, 0.2448, 0.2548,-0.73481,-0.76479, 0.2448, 0.2548, 0.26519, 0.23521,-0.7552, 0.2548, 0.26519});
-    logits.linspace(-0.08, 0.04);    
+    logits.linspace(-0.08, 0.04);
 
     nd4j::ops::softmax_cross_entropy_loss_with_logits_grad op;
 
     auto results = op.execute({&logits, &labels}, {}, {});
-    
+
     ASSERT_EQ(ND4J_STATUS_OK, results->status());
 
-    auto *dLdp = results->at(0);    
-    
+    auto *dLdp = results->at(0);
+
     ASSERT_TRUE(dLdpExp.isSameShape(dLdp));
     ASSERT_TRUE(dLdpExp.equalsTo(dLdp));
 
@@ -2236,19 +2236,19 @@ TEST_F(DeclarableOpsTests11, softmaxCrossEntropyWithLogits_grad_test2) {
 
     NDArray labels('c', {2,3,4}, {1,0,0,0, 0,1,0,1, 0,0,1,0, 0,0,0,1, 1,0,1,0, 0,1,0,0});
     NDArray logits('c', {2,3,4}, nd4j::DataType::DOUBLE);
-    
+
     NDArray dLdpExp('c', {2,3,4}, {-0.71836,  0.28164,  0.28164,  0.28164, 0.33051, -0.66949,  0.33051, -0.66949, 0.38785,  0.38785, -0.61215,  0.38785,
                                     0.28164,  0.28164,  0.28164, -0.71836,-0.66949,  0.33051, -0.66949,  0.33051, 0.38785, -0.61215,  0.38785,  0.38785});
-    logits.linspace(-0.08, 0.04);    
+    logits.linspace(-0.08, 0.04);
 
     nd4j::ops::softmax_cross_entropy_loss_with_logits_grad op;
 
     auto results = op.execute({&logits, &labels}, {}, {1});
-    
+
     ASSERT_EQ(ND4J_STATUS_OK, results->status());
 
-    auto *dLdp = results->at(0);        
-    
+    auto *dLdp = results->at(0);
+
     ASSERT_TRUE(dLdpExp.isSameShape(dLdp));
     ASSERT_TRUE(dLdpExp.equalsTo(dLdp));
 
@@ -2260,18 +2260,18 @@ TEST_F(DeclarableOpsTests11, softmaxCrossEntropyWithLogits_grad_test3) {
 
     NDArray labels('c', {2,3}, {1,0,0, 0,1,1});
     NDArray logits('c', {2,3}, nd4j::DataType::DOUBLE);
-    
+
     NDArray dLdpExp('c', {2,3}, {-0.52996,  0.47004,  0.47004, 0.52996, -0.47004, -0.47004});
-    logits.linspace(-0.08, 0.04);    
+    logits.linspace(-0.08, 0.04);
 
     nd4j::ops::softmax_cross_entropy_loss_with_logits_grad op;
 
     auto results = op.execute({&logits, &labels}, {}, {0});
-    
+
     ASSERT_EQ(ND4J_STATUS_OK, results->status());
 
-    auto *dLdp = results->at(0);        
-    
+    auto *dLdp = results->at(0);
+
     ASSERT_TRUE(dLdpExp.isSameShape(dLdp));
     ASSERT_TRUE(dLdpExp.equalsTo(dLdp));
 
@@ -2283,17 +2283,17 @@ TEST_F(DeclarableOpsTests11, softmaxCrossEntropyWithLogits_grad_test4) {
 
     NDArray labels('c', {2,1}, {1,1});
     NDArray logits('c', {2,1}, {-0.04, 0.04});
-    
-    NDArray dLdpExp('c', {2,1}, {0., 0.});    
+
+    NDArray dLdpExp('c', {2,1}, {0., 0.});
 
     nd4j::ops::softmax_cross_entropy_loss_with_logits_grad op;
 
     auto results = op.execute({&logits, &labels}, {}, {1});
-    
+
     ASSERT_EQ(ND4J_STATUS_OK, results->status());
 
-    auto *dLdp = results->at(0);        
-    
+    auto *dLdp = results->at(0);
+
     ASSERT_TRUE(dLdpExp.isSameShape(dLdp));
     ASSERT_TRUE(dLdpExp.equalsTo(dLdp));
 
@@ -2305,17 +2305,17 @@ TEST_F(DeclarableOpsTests11, softmaxCrossEntropyWithLogits_grad_test5) {
 
     NDArray labels('c', {2,1}, {1,0});
     NDArray logits('c', {2,1}, {-0.04, 0.04});
-    
-    NDArray dLdpExp('c', {2,1}, {-0.51999, 0.51999});    
+
+    NDArray dLdpExp('c', {2,1}, {-0.51999, 0.51999});
 
     nd4j::ops::softmax_cross_entropy_loss_with_logits_grad op;
 
     auto results = op.execute({&logits, &labels}, {}, {0});
-    
+
     ASSERT_EQ(ND4J_STATUS_OK, results->status());
 
-    auto *dLdp = results->at(0);        
-    
+    auto *dLdp = results->at(0);
+
     ASSERT_TRUE(dLdpExp.isSameShape(dLdp));
     ASSERT_TRUE(dLdpExp.equalsTo(dLdp));
 
@@ -2327,17 +2327,17 @@ TEST_F(DeclarableOpsTests11, softmaxCrossEntropyWithLogits_grad_test6) {
 
     NDArray labels('c', {1,2}, {1,1});
     NDArray logits('c', {1,2}, {-0.04, 0.04});
-    
-    NDArray dLdpExp('c', {1,2}, {0, 0});    
+
+    NDArray dLdpExp('c', {1,2}, {0, 0});
 
     nd4j::ops::softmax_cross_entropy_loss_with_logits_grad op;
 
     auto results = op.execute({&logits, &labels}, {}, {0});
-    
+
     ASSERT_EQ(ND4J_STATUS_OK, results->status());
 
-    auto *dLdp = results->at(0);        
-    
+    auto *dLdp = results->at(0);
+
     ASSERT_TRUE(dLdpExp.isSameShape(dLdp));
     ASSERT_TRUE(dLdpExp.equalsTo(dLdp));
 
@@ -2349,17 +2349,17 @@ TEST_F(DeclarableOpsTests11, softmaxCrossEntropyWithLogits_grad_test7) {
 
     NDArray labels('c', {2}, {0,1});
     NDArray logits('c', {2}, {-0.04, 0.04});
-    
+
     NDArray dLdpExp('c', {2}, {0.48001, -0.48001});
 
     nd4j::ops::softmax_cross_entropy_loss_with_logits_grad op;
 
     auto results = op.execute({&logits, &labels}, {}, {0});
-    
+
     ASSERT_EQ(ND4J_STATUS_OK, results->status());
 
-    auto *dLdp = results->at(0);        
-    
+    auto *dLdp = results->at(0);
+
     ASSERT_TRUE(dLdpExp.isSameShape(dLdp));
     ASSERT_TRUE(dLdpExp.equalsTo(dLdp));
 
@@ -2371,17 +2371,17 @@ TEST_F(DeclarableOpsTests11, softmaxCrossEntropyWithLogits_grad_test8) {
 
     NDArray labels('c', {1}, {1});
     NDArray logits('c', {1}, {0.04});
-    
+
     NDArray dLdpExp('c', {1}, {0});
 
     nd4j::ops::softmax_cross_entropy_loss_with_logits_grad op;
 
     auto results = op.execute({&logits, &labels}, {}, {0});
-    
+
     ASSERT_EQ(ND4J_STATUS_OK, results->status());
 
-    auto *dLdp = results->at(0);        
-    
+    auto *dLdp = results->at(0);
+
     ASSERT_TRUE(dLdpExp.isSameShape(dLdp));
     ASSERT_TRUE(dLdpExp.equalsTo(dLdp));
 
@@ -2420,19 +2420,19 @@ TEST_F(DeclarableOpsTests11, sparseSoftmaxCrossEntropyWithLogits_grad_test1) {
 
     NDArray labels('c', {2}, {2,1}, nd4j::DataType::INT64);
     NDArray logits('c', {2,3}, nd4j::DataType::DOUBLE);
-    
+
     NDArray dLdpExp('c', {2,3}, {0.30061,  0.33222, -0.63283, 0.30061, -0.66778,  0.36717});
-    
-    logits.linspace(0.1, 0.1);     
+
+    logits.linspace(0.1, 0.1);
 
     nd4j::ops::sparse_softmax_cross_entropy_loss_with_logits_grad op;
 
     auto results = op.execute({&labels, &logits}, {}, {});
-    
+
     ASSERT_EQ(ND4J_STATUS_OK, results->status());
 
-    auto *dLdp = results->at(0);    
-    
+    auto *dLdp = results->at(0);
+
     ASSERT_TRUE(dLdpExp.isSameShape(dLdp));
     ASSERT_TRUE(dLdpExp.equalsTo(dLdp));
 
@@ -2444,19 +2444,19 @@ TEST_F(DeclarableOpsTests11, sparseSoftmaxCrossEntropyWithLogits_grad_test2) {
 
     NDArray labels('c', {2}, {0,1}, nd4j::DataType::INT64);
     NDArray logits('c', {2,3}, nd4j::DataType::DOUBLE);
-    
-    NDArray dLdpExp('c', {2,3}, {-0.69939,  0.33222,  0.36717, 0.30061, -0.66778,  0.36717});    
-    
+
+    NDArray dLdpExp('c', {2,3}, {-0.69939,  0.33222,  0.36717, 0.30061, -0.66778,  0.36717});
+
     logits.linspace(-0.1, 0.1);
 
     nd4j::ops::sparse_softmax_cross_entropy_loss_with_logits_grad op;
 
     auto results = op.execute({&labels, &logits}, {}, {});
-    
+
     ASSERT_EQ(ND4J_STATUS_OK, results->status());
 
-    auto *dLdp = results->at(0);    
-    
+    auto *dLdp = results->at(0);
+
     ASSERT_TRUE(dLdpExp.isSameShape(dLdp));
     ASSERT_TRUE(dLdpExp.equalsTo(dLdp));
 
@@ -2468,17 +2468,17 @@ TEST_F(DeclarableOpsTests11, sparseSoftmaxCrossEntropyWithLogits_grad_test3) {
 
     NDArray labels('c', {}, {1}, nd4j::DataType::INT64);
     NDArray logits('c', {2}, {-0.2, 0.3});
-    
+
     NDArray dLdpExp('c', {2}, {0.37754, -0.37754});
 
     nd4j::ops::sparse_softmax_cross_entropy_loss_with_logits_grad op;
 
     auto results = op.execute({&labels, &logits}, {}, {});
-    
+
     ASSERT_EQ(ND4J_STATUS_OK, results->status());
 
-    auto *dLdp = results->at(0);    
-    
+    auto *dLdp = results->at(0);
+
     ASSERT_TRUE(dLdpExp.isSameShape(dLdp));
     ASSERT_TRUE(dLdpExp.equalsTo(dLdp));
 
@@ -2490,7 +2490,7 @@ TEST_F(DeclarableOpsTests11, sparseSoftmaxCrossEntropyWithLogits_grad_test4) {
 
     NDArray labels('c', {2,3}, {0,1,1, 3,3,2}, nd4j::DataType::INT64);
     NDArray logits('c', {2,3,4}, nd4j::DataType::DOUBLE);
-    
+
     NDArray dLdpExp('c', {2,3,4}, {-0.78616,  0.23633,  0.26118,  0.28865, 0.21384, -0.76367,  0.26118,  0.28865, 0.21384, -0.76367,  0.26118,  0.28865,
                                   0.21384,  0.23633,  0.26118, -0.71135, 0.21384,  0.23633,  0.26118, -0.71135, 0.21384,  0.23633, -0.73882,  0.28865});
     logits.linspace(-0.5, 0.1);
@@ -2498,11 +2498,11 @@ TEST_F(DeclarableOpsTests11, sparseSoftmaxCrossEntropyWithLogits_grad_test4) {
     nd4j::ops::sparse_softmax_cross_entropy_loss_with_logits_grad op;
 
     auto results = op.execute({&labels, &logits}, {}, {});
-    
+
     ASSERT_EQ(ND4J_STATUS_OK, results->status());
 
-    auto *dLdp = results->at(0);    
-    
+    auto *dLdp = results->at(0);
+
     ASSERT_TRUE(dLdpExp.isSameShape(dLdp));
     ASSERT_TRUE(dLdpExp.equalsTo(dLdp));
 
@@ -2514,17 +2514,17 @@ TEST_F(DeclarableOpsTests11, sparseSoftmaxCrossEntropyWithLogits_grad_test5) {
 
     NDArray labels('c', {1,1}, {0}, nd4j::DataType::INT64);
     NDArray logits('c', {1,1,2}, {-0.3,0.2});
-    
-    NDArray dLdpExp('c', {1,1,2}, {-0.62246,  0.62246});    
+
+    NDArray dLdpExp('c', {1,1,2}, {-0.62246,  0.62246});
 
     nd4j::ops::sparse_softmax_cross_entropy_loss_with_logits_grad op;
 
     auto results = op.execute({&labels, &logits}, {}, {});
-    
+
     ASSERT_EQ(ND4J_STATUS_OK, results->status());
 
-    auto *dLdp = results->at(0);    
-    
+    auto *dLdp = results->at(0);
+
     ASSERT_TRUE(dLdpExp.isSameShape(dLdp));
     ASSERT_TRUE(dLdpExp.equalsTo(dLdp));
 
diff --git a/libnd4j/tests_cpu/layers_tests/DeclarableOpsTests4.cpp b/libnd4j/tests_cpu/layers_tests/DeclarableOpsTests4.cpp
index fd32f8a79..da5f5f75d 100644
--- a/libnd4j/tests_cpu/layers_tests/DeclarableOpsTests4.cpp
+++ b/libnd4j/tests_cpu/layers_tests/DeclarableOpsTests4.cpp
@@ -245,7 +245,7 @@ TYPED_TEST(TypedDeclarableOpsTests4, Test_Pooling_Parity_12) {
 
 TEST_F(DeclarableOpsTests4, Test_BiasAdd_NHWC_1) {
     auto x = NDArrayFactory::create<double>('c', {2, 3, 3, 2});
-    auto bias = NDArrayFactory::create<double>('c', {1, 2}, {1, 2});
+    auto bias = NDArrayFactory::create<double>('c', {2}, {1, 2});
     auto exp = NDArrayFactory::create<double>('c', {2, 3, 3, 2}, {1.f,  2.f,  1.f,  2.f,  1.f,  2.f,  1.f,  2.f,  1.f,  2.f,  1.f,  2.f,  1.f,  2.f,  1.f,  2.f,  1.f,  2.f, 1.f,  2.f,  1.f,  2.f,  1.f,  2.f,  1.f,  2.f,  1.f,  2.f,  1.f,  2.f,  1.f,  2.f,  1.f,  2.f,  1.f,  2.f});
 
     nd4j::ops::biasadd op;
@@ -263,11 +263,11 @@ TEST_F(DeclarableOpsTests4, Test_BiasAdd_NHWC_1) {
 
 TEST_F(DeclarableOpsTests4, Test_BiasAdd_NCHW_1) {
     auto x = NDArrayFactory::create<double>('c', {2, 2, 3, 3});
-    auto bias = NDArrayFactory::create<double>('c', {1, 2}, {1, 2});
-    auto exp = NDArrayFactory::create<double>('c', {2, 2, 3, 3}, {1.f,  2.f,  1.f,  2.f,  1.f,  2.f,  1.f,  2.f,  1.f,  2.f,  1.f,  2.f,  1.f,  2.f,  1.f,  2.f,  1.f,  2.f, 1.f,  2.f,  1.f,  2.f,  1.f,  2.f,  1.f,  2.f,  1.f,  2.f,  1.f,  2.f,  1.f,  2.f,  1.f,  2.f,  1.f,  2.f});
+    auto bias = NDArrayFactory::create<double>('c', {2}, {1, 2});
+    auto exp = NDArrayFactory::create<double>('c', {2, 2, 3, 3}, {1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2});
 
     nd4j::ops::biasadd op;
-    auto result = op.execute({&x, &bias}, {}, {}, {}, false, nd4j::DataType::DOUBLE);
+    auto result = op.execute({&x, &bias}, {}, {}, {true}, false, nd4j::DataType::DOUBLE);
 
     ASSERT_EQ(ND4J_STATUS_OK, result->status());
 
@@ -360,6 +360,43 @@ TEST_F(DeclarableOpsTests4, Test_FlattenTests_2) {
     delete result;
 }
 
+TEST_F(DeclarableOpsTests4, Test_FlattenTests_3) {
+    NDArray x('c', {2,2}, {1, 2, 3, 4}, nd4j::DataType::INT32);
+    NDArray y('f', {2,2}, nd4j::DataType::INT32);
+    NDArray exp('c', {8}, {1, 2, 3, 4, 1, 2, 3, 4}, nd4j::DataType::INT32);
+
+    y.assign(x);
+
+    nd4j::ops::flatten op;
+    auto result = op.execute({&x, &y}, {}, {'c'});
+    ASSERT_EQ(ND4J_STATUS_OK, result->status());
+
+    auto z = result->at(0);
+
+    ASSERT_TRUE(exp.equalsTo(z));
+
+    delete result;
+}
+
+TEST_F(DeclarableOpsTests4, Test_FlattenTests_4) {
+    NDArray x('c', {2,2}, {1, 2, 3, 4}, nd4j::DataType::INT32);
+    NDArray y('f', {2,2}, nd4j::DataType::INT32);
+    NDArray exp('c', {8}, {1, 3, 2, 4, 1, 3, 2, 4}, nd4j::DataType::INT32);
+
+    y.assign(x);
+
+    nd4j::ops::flatten op;
+    auto result = op.execute({&x, &y}, {}, {'f'});
+    ASSERT_EQ(ND4J_STATUS_OK, result->status());
+
+    auto z = result->at(0);
+    z->printIndexedBuffer();
+
+    ASSERT_TRUE(exp.equalsTo(z));
+
+    delete result;
+}
+
 TEST_F(DeclarableOpsTests4, Test_FloorTests_1) {
     auto x = NDArrayFactory::create<double>('c', {3, 3}, {1.5, 2.3, 3.4, 4.3, 5.9, 6.1, 7.2, 8.9, 9.7});
     auto exp = NDArrayFactory::create<double>('c', {3,3});
@@ -608,7 +645,7 @@ TEST_F(DeclarableOpsTests4, Test_BiasAdd_1) {
     auto exp = NDArrayFactory::create<double>('c', {2, 3}, {1, 2, 3, 1, 2, 3});
 
     nd4j::ops::biasadd op;
-    auto result = op.execute({&x, &row}, {}, {}, {}, false, nd4j::DataType::DOUBLE);
+    auto result = op.execute({&x, &row}, {}, {}, {true}, false, nd4j::DataType::DOUBLE);
 
     ASSERT_EQ(ND4J_STATUS_OK, result->status());
 
diff --git a/libnd4j/tests_cpu/layers_tests/DeclarableOpsTests6.cpp b/libnd4j/tests_cpu/layers_tests/DeclarableOpsTests6.cpp
index a5e808867..e6c692f5b 100644
--- a/libnd4j/tests_cpu/layers_tests/DeclarableOpsTests6.cpp
+++ b/libnd4j/tests_cpu/layers_tests/DeclarableOpsTests6.cpp
@@ -1610,21 +1610,8 @@ TEST_F(DeclarableOpsTests6, MatrixInverse_1) {
 ////////////////////////////////////////////////////////////////////////////////
 TEST_F(DeclarableOpsTests6, MatrixInverse_010) {
 
-    auto x = NDArrayFactory::create<float>('c', {1, 5, 5}, {
-            1.,  0.,  0.,  0.,  0.,
-            2.,  1.,  0.,  0.,  0.,
-            30.,  2.,  1.,  0.,  0.,
-            4.,  3.,  2.,  1.,  0.,
-            5.,  4.,  3.,  2.,  1.,
-    });
-
-    auto exp = NDArrayFactory::create<float>('c', {1, 5, 5}, {
-            1.0,  0.0,  0.0,  0.0, 0.,
-            -2.0,  1.0,   0.,   0., 0.,
-            -26.0, -2.0,    1,    0, 0.,
-            54.0,  1.0, -2.0,    1, 0.,
-            -27.0,  0.0,  1.0, -2.0, 1.
-    });
+    auto x = NDArrayFactory::create<float>('c', {1, 5, 5}, {1.,  0.,  0.,  0.,  0.,2.,  1.,  0.,  0.,  0.,30.,  2.,  1.,  0.,  0.,4.,  3.,  2.,  1.,  0.,5.,  4.,  3.,  2.,  1.,});
+    auto exp = NDArrayFactory::create<float>('c', {1, 5, 5}, {1.0,  0.0,  0.0,  0.0, 0.,-2.0,  1.0,   0.,   0., 0.,-26.0, -2.0,    1,    0, 0.,54.0,  1.0, -2.0,    1, 0.,-27.0,  0.0,  1.0, -2.0, 1.});
 
     nd4j::ops::matrix_inverse op;
     auto result = op.execute({&x}, {}, {}, {}, false, nd4j::DataType::FLOAT32);
@@ -1632,8 +1619,6 @@ TEST_F(DeclarableOpsTests6, MatrixInverse_010) {
     ASSERT_EQ(ND4J_STATUS_OK, result->status());
 
     auto z = result->at(0);
-//    z->printIndexedBuffer("010 Output ");
-//    exp.printIndexedBuffer("010 Expected ");
 
     ASSERT_TRUE(exp.isSameShape(z));
     ASSERT_TRUE(exp.equalsTo(z));
@@ -1644,24 +1629,9 @@ TEST_F(DeclarableOpsTests6, MatrixInverse_010) {
 ////////////////////////////////////////////////////////////////////////////////
 TEST_F(DeclarableOpsTests6, MatrixInverse_01) {
 
-    auto x = NDArrayFactory::create<float>('c', {1, 5, 5}, {
-            2.,  4., 60.,  8., 10.,
-            0.,  1.,  2.,  3.,  4.,
-            0.,  0.,  2.,  4.,  6.,
-            0.,  0.,  0.,  1.,  2.,
-            0.,  0.,  0.,  0.,  4.
-
-    });
-
-    auto exp = NDArrayFactory::create<float>('c', {1, 5, 5}, {
-            0.5, -2.0, -13.0, 54.0, -6.75,
-            0.0,  1.0,  -1.0,  1.0,   0.0,
-            0,    0,   0.5, -2.0,  0.25,
-            0,    0,     0,  1.0,  -0.5,
-            0,    0,     0,    0,  0.25
-
-    });
+    auto x = NDArrayFactory::create<float>('c', {1, 5, 5}, {2.,  4., 60.,  8., 10., 0.,  1.,  2.,  3.,  4., 0.,  0.,  2.,  4.,  6., 0.,  0.,  0.,  1.,  2., 0.,  0.,  0.,  0.,  4. });
 
+    auto exp = NDArrayFactory::create<float>('c', {1, 5, 5}, {0.5, -2.0, -13.0, 54.0, -6.75, 0.0,  1.0,  -1.0,  1.0,   0.0, 0,    0,   0.5, -2.0,  0.25, 0,    0,     0,  1.0,  -0.5, 0,    0,     0,    0,  0.25 });
     nd4j::ops::matrix_inverse op;
     auto result = op.execute({&x}, {}, {}, {}, false, nd4j::DataType::FLOAT32);
 
@@ -1680,21 +1650,8 @@ TEST_F(DeclarableOpsTests6, MatrixInverse_01) {
 ////////////////////////////////////////////////////////////////////////////////
 TEST_F(DeclarableOpsTests6, MatrixInverse_02) {
 
-    auto x = NDArrayFactory::create<float>('c', {1, 5, 5}, {
-            1.,  0.,  0.,  0.,  0.,
-            2.,  1.,  0.,  0.,  0.,
-            30.,  2.,  1.,  0.,  0.,
-            4.,  3.,  2.,  1.,  0.,
-            5.,  4.,  3.,  2.,  1.
-    });
-
-    auto exp = NDArrayFactory::create<float>('c', {1, 5, 5}, {
-            1.0,  0.0,  0.0,  0.0, 0.,
-            -2.0,  1.0,   0.,   0., 0.,
-            -26.0, -2.0,    1,    0, 0.,
-            54.0,  1.0, -2.0,    1, 0.,
-            -27.0,  0.0,  1.0, -2.0, 1.
-    });
+    auto x = NDArrayFactory::create<float>('c', {1, 5, 5}, {1.,  0.,  0.,  0.,  0., 2.,  1.,  0.,  0.,  0., 30.,  2.,  1.,  0.,  0., 4.,  3.,  2.,  1.,  0., 5.,  4.,  3.,  2.,  1. });
+    auto exp = NDArrayFactory::create<float>('c', {1, 5, 5}, {1.0,  0.0,  0.0,  0.0, 0., -2.0,  1.0,   0.,   0., 0., -26.0, -2.0,    1,    0, 0., 54.0,  1.0, -2.0,    1, 0., -27.0,  0.0,  1.0, -2.0, 1. });
 
     nd4j::ops::matrix_inverse op;
     auto result = op.execute({&x}, {}, {}, {}, false, nd4j::DataType::FLOAT32);
diff --git a/libnd4j/tests_cpu/layers_tests/JavaInteropTests.cpp b/libnd4j/tests_cpu/layers_tests/JavaInteropTests.cpp
index d0d67000b..21af8e380 100644
--- a/libnd4j/tests_cpu/layers_tests/JavaInteropTests.cpp
+++ b/libnd4j/tests_cpu/layers_tests/JavaInteropTests.cpp
@@ -169,7 +169,7 @@ TEST_F(JavaInteropTests, TestSconv2d_1) {
     auto input = NDArrayFactory::create<float>('c', {3, 3, 8, 8});
     auto weightsD = NDArrayFactory::create<float>('c', {1, 3, 1, 1});
     auto weightsP = NDArrayFactory::create<float>('c', {2, 3, 1, 1});
-    auto bias = NDArrayFactory::create<float>('c', {1, 2});
+    auto bias = NDArrayFactory::create<float>('c', {2});
     auto output = NDArrayFactory::create<float>('c', {3, 2, 8, 8});
     output.assign(0.0);
 
diff --git a/libnd4j/tests_cpu/layers_tests/NDArrayTests2.cpp b/libnd4j/tests_cpu/layers_tests/NDArrayTests2.cpp
index 32ff23847..9f9937368 100644
--- a/libnd4j/tests_cpu/layers_tests/NDArrayTests2.cpp
+++ b/libnd4j/tests_cpu/layers_tests/NDArrayTests2.cpp
@@ -1259,7 +1259,7 @@ TEST_F(NDArrayTest2, reduce_1) {
             for (int x = 0; x < 4; x++) {
                 for (int y = 0; y < 4; y++) {
                     Nd4jLong indices[] = {0, 0, x, y, i, j};
-                    Nd4jLong offset = shape::getOffset(0, arr6.shapeOf(), arr6.stridesOf(), indices, arr6.rankOf());
+                    Nd4jLong offset = shape::getOffset(arr6.getShapeInfo(), indices);
                     sum += ((double*)arr6.getBuffer())[offset];
                 }
             }
diff --git a/libnd4j/tests_cpu/layers_tests/OneOffTests.cpp b/libnd4j/tests_cpu/layers_tests/OneOffTests.cpp
index 5005808bf..05f823e4a 100644
--- a/libnd4j/tests_cpu/layers_tests/OneOffTests.cpp
+++ b/libnd4j/tests_cpu/layers_tests/OneOffTests.cpp
@@ -159,8 +159,6 @@ TEST_F(OneOffTests, test_conv2d_nhwc_failed_1) {
     auto z = graph->getVariableSpace()->getVariable(9)->getNDArray();
     ASSERT_TRUE(z != nullptr);
 
-    // z->printIndexedBuffer("z");
-
     ASSERT_EQ(e, *z);
 
     delete graph;
diff --git a/libnd4j/tests_cpu/layers_tests/ParityOpsTests.cpp b/libnd4j/tests_cpu/layers_tests/ParityOpsTests.cpp
index b6981a5c3..0254d1877 100644
--- a/libnd4j/tests_cpu/layers_tests/ParityOpsTests.cpp
+++ b/libnd4j/tests_cpu/layers_tests/ParityOpsTests.cpp
@@ -683,7 +683,7 @@ TEST_F(ParityOpsTests, Test_Reshape_TF_1) {
 TEST_F(ParityOpsTests, Test_Bias_Add_1) {
     auto x = NDArrayFactory::create<float>('c', {10, 5});
     x.assign(0.0);
-    auto bias = NDArrayFactory::create<float>('c', {1, 5}, {1, 2, 3, 4, 5});
+    auto bias = NDArrayFactory::create<float>('c', {5}, {1, 2, 3, 4, 5});
     nd4j::ops::biasadd op;
 
     auto result = op.execute({&x, &bias}, {}, {});
@@ -691,7 +691,6 @@ TEST_F(ParityOpsTests, Test_Bias_Add_1) {
 
     auto z = result->at(0);
 
-
     auto tads = z->allTensorsAlongDimension({1});
     for (int e = 0; e < tads->size(); e++) {
         ASSERT_TRUE(bias.equalsTo(tads->at(e)));
diff --git a/libnd4j/tests_cpu/layers_tests/ShapeTests.cpp b/libnd4j/tests_cpu/layers_tests/ShapeTests.cpp
index 98b9cd026..071c33fab 100644
--- a/libnd4j/tests_cpu/layers_tests/ShapeTests.cpp
+++ b/libnd4j/tests_cpu/layers_tests/ShapeTests.cpp
@@ -67,7 +67,7 @@ TEST_F(ShapeTests, Test_ShapeEquality_1) {
     Nd4jLong shape[] = {4, 2, 3, 4, 5, 60, 20, 5, 1, 0, -1, 102};
     Nd4jLong shape_GOOD[] = {4, 2, 3, 4, 5, 60, 20, 5, 1, 0, 1, 99};
     Nd4jLong shape_BAD[] = {4, 3, 3, 4, 5, 60, 20, 5, 1, 0, -1, 102};
-    
+
 
     ASSERT_TRUE(shape::equalsSoft(shape, shape_GOOD));
     ASSERT_FALSE(shape::equalsSoft(shape, shape_BAD));
@@ -77,7 +77,7 @@ TEST_F(ShapeTests, Test_ShapeEquality_2) {
     Nd4jLong shape[] = {4, 2, 3, 4, 5, 60, 20, 5, 1, 0, -1, 102};
     Nd4jLong shape_GOOD[] = {4, 2, 3, 4, 5, 60, 20, 5, 1, 0, -1, 102};
     Nd4jLong shape_BAD[] = {4, 2, 3, 4, 5, 60, 20, 5, 1, 0, -1, 99};
-    
+
 
     ASSERT_TRUE(shape::equalsStrict(shape, shape_GOOD));
     ASSERT_FALSE(shape::equalsStrict(shape, shape_BAD));
@@ -86,45 +86,24 @@ TEST_F(ShapeTests, Test_ShapeEquality_2) {
 TEST_F(ShapeTests, Test_Ind2SubC_1) {
     Nd4jLong shape[] = {3, 5};
     Nd4jLong c0[2];
-    shape::index2coords(2, shape, 0, c0);
+    shape::index2coords(0, 2, shape, c0);
 
     ASSERT_EQ(0, c0[0]);
     ASSERT_EQ(0, c0[1]);
 
     Nd4jLong c1[2];
-    shape::index2coords(2, shape, 1, c1);
+    shape::index2coords(1, 2, shape, c1);
 
     ASSERT_EQ(0, c1[0]);
     ASSERT_EQ(1, c1[1]);
 
     Nd4jLong c6[2];
-    shape::index2coords(2, shape, 5, c6);
+    shape::index2coords(5, 2, shape, c6);
 
     ASSERT_EQ(1, c6[0]);
-    ASSERT_EQ(0, c6[1]);    
+    ASSERT_EQ(0, c6[1]);
 }
 
-TEST_F(ShapeTests, Test_Ind2Sub_1) {
-    Nd4jLong shape[] = {3, 5};
-    
-    Nd4jLong c0[2];
-    shape::index2coords(2, shape, 0, c0, 'f');
-
-    ASSERT_EQ(0, c0[0]);
-    ASSERT_EQ(0, c0[1]);
-
-    Nd4jLong c1[2];
-    shape::index2coords(2, shape, 1, c1, 'f');
-
-    ASSERT_EQ(1, c1[0]);
-    ASSERT_EQ(0, c1[1]);
-
-    Nd4jLong c6[2];
-    shape::index2coords(2, shape, 5, c6, 'f');
-
-    ASSERT_EQ(2, c6[0]);
-    ASSERT_EQ(1, c6[1]);    
-}
 
 TEST_F(ShapeTests, Test_ShapeDetector_1) {
     Nd4jLong shape[] = {2, 5, 3, 3, 1, 0, 1, 99};
diff --git a/libnd4j/windows.md b/libnd4j/windows.md
index 015c9a884..884b2c3ee 100644
--- a/libnd4j/windows.md
+++ b/libnd4j/windows.md
@@ -13,7 +13,7 @@ You will also need to setup your PATH environment variable to include `C:\msys64
 
 For cpu, we recommend openblas. We will be adding instructions for mkl and other cpu implementations later.
 
-Send us a pull request or [file an issue](https://github.com/deeplearning4j/libnd4j/issues) if you have something in particular you are looking for.
+Send us a pull request or [file an issue](https://github.com/eclipse/deeplearning4j/issues) if you have something in particular you are looking for.
 
 ## Building libnd4j
 
@@ -67,7 +67,7 @@ While still in the `libnd4j` folder, run:
 
     If you want to use Control Panel for that: if you have libnd4j path looking like 'c:\Users\username\libnd4j' set LIBND4J_HOME to '/Users/username/libnd4j'
 
-Now leave the libnd4j directory and clone the [nd4j repository](https://github.com/deeplearning4j/nd4j). Run the following to compile nd4j with support for both the native cpu backend as well as the cuda backend:
+Now leave the libnd4j directory and clone the [repository](https://github.com/eclipse/deeplearning4j). Run the following to compile nd4j with support for both the native cpu backend as well as the cuda backend:
 
 ```bash
 mvn clean install -DskipTests -Dmaven.javadoc.skip=true
diff --git a/nd4j/.github/CONTRIBUTING.md b/nd4j/.github/CONTRIBUTING.md
deleted file mode 100644
index add412618..000000000
--- a/nd4j/.github/CONTRIBUTING.md
+++ /dev/null
@@ -1,15 +0,0 @@
-## Contribute
-
-1. Check for open issues, or open a new issue to start a discussion around a feature idea or a bug.
-2. If you feel uncomfortable or uncertain about an issue or your changes, feel free to contact us on Gitter using the link above.
-3. Fork [the repository](https://github.com/deeplearning4j/nd4j.git) on GitHub to start making your changes to the **master** branch (or branch off of it).
-4. Write a test, which shows that the bug was fixed or that the feature works as expected.
-5. Note the repository follows
-   the [Google Java style](https://google.github.io/styleguide/javaguide.html)
-   with two modifications: 120-char column wrap and 4-spaces indentation. You
-   can format your code to this format by typing `mvn formatter:format` in the
-   subproject you work on, by using the `contrib/formatter.xml` at the root of
-   the repository to configure the Eclipse formatter, or by [using the INtellij
-   plugin](https://github.com/HPI-Information-Systems/Metanome/wiki/Installing-the-google-styleguide-settings-in-intellij-and-eclipse).
-
-6. Send a pull request, and bug us on Gitter until it gets merged and published.
diff --git a/nd4j/.github/ISSUE_TEMPLATE.md b/nd4j/.github/ISSUE_TEMPLATE.md
deleted file mode 100644
index 9dd585d22..000000000
--- a/nd4j/.github/ISSUE_TEMPLATE.md
+++ /dev/null
@@ -1,19 +0,0 @@
-#### Issue Description
-
-Please describe your issue, along with:
-- expected behavior
-- encountered behavior
-
-#### Version Information
-
-Please indicate relevant versions, including, if relevant:
-
-* Deeplearning4j version
-* platform information (OS, etc)
-* CUDA version, if used
-* NVIDIA driver version, if in use
-
-#### Contributing
-
-If you'd like to help us fix the issue by contributing some code, but would
-like guidance or help in doing so, please mention it!
diff --git a/nd4j/.github/PULL_REQUEST_TEMPLATE.md b/nd4j/.github/PULL_REQUEST_TEMPLATE.md
deleted file mode 100644
index 1f92bacd6..000000000
--- a/nd4j/.github/PULL_REQUEST_TEMPLATE.md
+++ /dev/null
@@ -1,10 +0,0 @@
-## What changes were proposed in this pull request?
-
-(Please fill in changes proposed in this fix)
-
-## How was this patch tested?
-
-(Please explain how this patch was tested. E.g. unit tests, integration tests, manual tests)
-
-Please review
-https://github.com/deeplearning4j/deeplearning4j/blob/master/CONTRIBUTING.md before opening a pull request.
diff --git a/nd4j/README.md b/nd4j/README.md
index 17681d07e..f26adea75 100644
--- a/nd4j/README.md
+++ b/nd4j/README.md
@@ -12,7 +12,7 @@ It is meant to be used in production environments rather than as a research tool
 Please search for the latest version on search.maven.org.
 
 Or use the versions displayed in:
-https://github.com/deeplearning4j/dl4j-0.4-examples/blob/master/pom.xml
+https://github.com/eclipse/deeplearning4j-examples/blob/master/pom.xml
 
 
 ---
@@ -51,7 +51,7 @@ https://deeplearning4j.org/buildinglocally
 
 1. Check for open issues, or open a new issue to start a discussion around a feature idea or a bug.
 2. If you feel uncomfortable or uncertain about an issue or your changes, feel free to contact us on Gitter using the link above.
-3. Fork [the repository](https://github.com/deeplearning4j/nd4j.git) on GitHub to start making your changes to the **master** branch (or branch off of it).
+3. Fork [the repository](https://github.com/eclipse/deeplearning4j.git) on GitHub to start making your changes to the **master** branch (or branch off of it).
 4. Write a test, which shows that the bug was fixed or that the feature works as expected.
 5. Note the repository follows
    the [Google Java style](https://google.github.io/styleguide/javaguide.html)
diff --git a/nd4j/RaspberryPi.md b/nd4j/RaspberryPi.md
index 184f43f98..6aee42014 100644
--- a/nd4j/RaspberryPi.md
+++ b/nd4j/RaspberryPi.md
@@ -16,7 +16,7 @@ Please follow following instructions to build nd4j for raspberry PI:
 
     ```
     $ cd $HOME
-    $ git clone https://github.com/deeplearning4j/deeplearning4j.git
+    $ git clone https://github.com/eclipse/deeplearning4j.git
     ```
 
 3. build libnd4j:
diff --git a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/samediff/SameDiff.java b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/samediff/SameDiff.java
index 1821a30a0..625912f50 100644
--- a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/samediff/SameDiff.java
+++ b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/samediff/SameDiff.java
@@ -16,21 +16,58 @@
 
 package org.nd4j.autodiff.samediff;
 
-import org.nd4j.shade.guava.base.Predicates;
-import org.nd4j.shade.guava.collect.HashBasedTable;
-import org.nd4j.shade.guava.collect.Maps;
-import org.nd4j.shade.guava.collect.Table;
-import org.nd4j.shade.guava.primitives.Ints;
+import static org.nd4j.autodiff.util.TrainingUtils.stackOutputs;
+
 import com.google.flatbuffers.FlatBufferBuilder;
-import lombok.*;
+import java.io.BufferedInputStream;
+import java.io.BufferedOutputStream;
+import java.io.DataOutputStream;
+import java.io.File;
+import java.io.FileInputStream;
+import java.io.FileOutputStream;
+import java.io.IOException;
+import java.io.InputStream;
+import java.io.OutputStream;
+import java.lang.reflect.Method;
+import java.nio.ByteBuffer;
+import java.util.ArrayList;
+import java.util.Arrays;
+import java.util.Collection;
+import java.util.Collections;
+import java.util.HashMap;
+import java.util.HashSet;
+import java.util.IdentityHashMap;
+import java.util.LinkedHashMap;
+import java.util.LinkedHashSet;
+import java.util.LinkedList;
+import java.util.List;
+import java.util.Map;
+import java.util.Queue;
+import java.util.Set;
+import java.util.Stack;
+import java.util.UUID;
+import java.util.concurrent.ConcurrentHashMap;
+import java.util.concurrent.atomic.AtomicInteger;
+import java.util.regex.Matcher;
+import java.util.regex.Pattern;
+import lombok.AllArgsConstructor;
+import lombok.Builder;
+import lombok.Getter;
+import lombok.NonNull;
+import lombok.Setter;
 import lombok.extern.slf4j.Slf4j;
+import lombok.val;
 import org.apache.commons.io.IOUtils;
 import org.apache.commons.lang3.ArrayUtils;
 import org.nd4j.autodiff.execution.conf.ExecutorConfiguration;
 import org.nd4j.autodiff.execution.conf.OutputMode;
 import org.nd4j.autodiff.functions.DifferentialFunction;
 import org.nd4j.autodiff.functions.DifferentialFunctionFactory;
-import org.nd4j.autodiff.listeners.*;
+import org.nd4j.autodiff.listeners.At;
+import org.nd4j.autodiff.listeners.Listener;
+import org.nd4j.autodiff.listeners.ListenerResponse;
+import org.nd4j.autodiff.listeners.Loss;
+import org.nd4j.autodiff.listeners.Operation;
 import org.nd4j.autodiff.listeners.impl.HistoryListener;
 import org.nd4j.autodiff.listeners.records.History;
 import org.nd4j.autodiff.listeners.records.LossCurve;
@@ -38,14 +75,34 @@ import org.nd4j.autodiff.samediff.config.BatchOutputConfig;
 import org.nd4j.autodiff.samediff.config.EvaluationConfig;
 import org.nd4j.autodiff.samediff.config.FitConfig;
 import org.nd4j.autodiff.samediff.config.OutputConfig;
-import org.nd4j.autodiff.samediff.internal.*;
-import org.nd4j.autodiff.samediff.ops.*;
+import org.nd4j.autodiff.samediff.internal.AbstractSession;
+import org.nd4j.autodiff.samediff.internal.DataTypesSession;
+import org.nd4j.autodiff.samediff.internal.InferenceSession;
+import org.nd4j.autodiff.samediff.internal.SameDiffOp;
+import org.nd4j.autodiff.samediff.internal.Variable;
+import org.nd4j.autodiff.samediff.ops.SDBaseOps;
+import org.nd4j.autodiff.samediff.ops.SDBitwise;
+import org.nd4j.autodiff.samediff.ops.SDCNN;
+import org.nd4j.autodiff.samediff.ops.SDImage;
+import org.nd4j.autodiff.samediff.ops.SDLoss;
+import org.nd4j.autodiff.samediff.ops.SDMath;
+import org.nd4j.autodiff.samediff.ops.SDNN;
+import org.nd4j.autodiff.samediff.ops.SDRNN;
+import org.nd4j.autodiff.samediff.ops.SDRandom;
 import org.nd4j.autodiff.samediff.serde.FlatBuffersMapper;
 import org.nd4j.base.Preconditions;
 import org.nd4j.evaluation.IEvaluation;
 import org.nd4j.evaluation.classification.Evaluation;
 import org.nd4j.evaluation.classification.ROC;
-import org.nd4j.graph.*;
+import org.nd4j.graph.ExecutionMode;
+import org.nd4j.graph.FlatArray;
+import org.nd4j.graph.FlatConfiguration;
+import org.nd4j.graph.FlatGraph;
+import org.nd4j.graph.FlatNode;
+import org.nd4j.graph.FlatVariable;
+import org.nd4j.graph.IntPair;
+import org.nd4j.graph.OpType;
+import org.nd4j.graph.UpdaterState;
 import org.nd4j.imports.graphmapper.tf.TFGraphMapper;
 import org.nd4j.linalg.api.buffer.DataType;
 import org.nd4j.linalg.api.memory.MemoryWorkspace;
@@ -84,23 +141,17 @@ import org.nd4j.linalg.primitives.Pair;
 import org.nd4j.linalg.util.ArrayUtil;
 import org.nd4j.linalg.util.DeviceLocalNDArray;
 import org.nd4j.linalg.util.ND4JFileUtils;
+import org.nd4j.shade.guava.base.Predicates;
+import org.nd4j.shade.guava.collect.HashBasedTable;
+import org.nd4j.shade.guava.collect.Maps;
+import org.nd4j.shade.guava.collect.Table;
+import org.nd4j.shade.guava.primitives.Ints;
 import org.nd4j.weightinit.WeightInitScheme;
 import org.nd4j.weightinit.impl.ConstantInitScheme;
 import org.nd4j.weightinit.impl.NDArraySupplierInitScheme;
 import org.nd4j.weightinit.impl.ZeroInitScheme;
 import org.tensorflow.framework.GraphDef;
 
-import java.io.*;
-import java.lang.reflect.Method;
-import java.nio.ByteBuffer;
-import java.util.*;
-import java.util.concurrent.ConcurrentHashMap;
-import java.util.concurrent.atomic.AtomicInteger;
-import java.util.regex.Matcher;
-import java.util.regex.Pattern;
-
-import static org.nd4j.autodiff.util.TrainingUtils.stackOutputs;
-
 /**
  * SameDiff is the entrypoint for ND4J's automatic differentiation functionality.
  * <p>
@@ -2064,8 +2115,7 @@ public class SameDiff extends SDBaseOps {
 
         List<Listener> activeListeners = new ArrayList<>();
 
-        if (!history.evaluations().isEmpty())
-            activeListeners.add(history);
+        activeListeners.add(history);
 
         for (Listener l : this.listeners)
             if (l.isActive(Operation.TRAINING))
@@ -2102,6 +2152,9 @@ public class SameDiff extends SDBaseOps {
             requiredVars.addAll(l.requiredVariables(this).trainingVariables());
         }
 
+        ArrayList<Listener> listenersWitHistory = new ArrayList<>(listeners);
+        listenersWitHistory.add(history);
+
         for (int i = 0; i < numEpochs; i++) {
 
             if (incrementEpochCount && hasListeners) {
@@ -2236,53 +2289,52 @@ public class SameDiff extends SDBaseOps {
                     }
                 }
 
+                double[] d = new double[lossVariables.size() + regScore.size()];
+                List<String> lossVars;
+                if (regScore.size() > 0) {
+                    lossVars = new ArrayList<>(lossVariables.size() + regScore.size());
+                    lossVars.addAll(lossVariables);
+                    int s = regScore.size();
+                    //Collect regularization losses
+                    for (Map.Entry<Class<?>, AtomicDouble> entry : regScore.entrySet()) {
+                        lossVars.add(entry.getKey().getSimpleName());
+                        d[s] = entry.getValue().get();
+                    }
+                } else {
+                    lossVars = lossVariables;
+                }
+
+                //Collect the losses...
+                SameDiff gradFn = sameDiffFunctionInstances.get(GRAD_FN_KEY);
+                int count = 0;
+                for (String s : lossVariables) {
+                    INDArray arr = gradFn.getArrForVarName(s);
+                    double l = arr.isScalar() ? arr.getDouble(0) : arr.sumNumber().doubleValue();
+                    d[count++] = l;
+                }
+
+                Loss loss = new Loss(lossVars, d);
+
+                if (lossNames == null) {
+                    lossNames = lossVars;
+                } else {
+                    Preconditions.checkState(lossNames.equals(lossVars),
+                            "Loss names mismatch, expected: %s, got: %s", lossNames, lossVars);
+                }
+
+                if (lossSums == null) {
+                    lossSums = d;
+                } else {
+                    Preconditions.checkState(lossNames.equals(lossVars),
+                            "Loss size mismatch, expected: %s, got: %s", lossSums.length, d.length);
+
+                    for (int j = 0; j < lossSums.length; j++) {
+                        lossSums[j] += d[j];
+                    }
+                }
+                lossCount++;
+
                 if (hasListeners) {
-                    double[] d = new double[lossVariables.size() + regScore.size()];
-                    List<String> lossVars;
-                    if (regScore.size() > 0) {
-                        lossVars = new ArrayList<>(lossVariables.size() + regScore.size());
-                        lossVars.addAll(lossVariables);
-                        int s = regScore.size();
-                        //Collect regularization losses
-                        for (Map.Entry<Class<?>, AtomicDouble> entry : regScore.entrySet()) {
-                            lossVars.add(entry.getKey().getSimpleName());
-                            d[s] = entry.getValue().get();
-                        }
-                    } else {
-                        lossVars = lossVariables;
-                    }
-
-
-                    //Collect the losses...
-                    SameDiff gradFn = sameDiffFunctionInstances.get(GRAD_FN_KEY);
-                    int count = 0;
-                    for (String s : lossVariables) {
-                        INDArray arr = gradFn.getArrForVarName(s);
-                        double l = arr.isScalar() ? arr.getDouble(0) : arr.sumNumber().doubleValue();
-                        d[count++] = l;
-                    }
-
-                    Loss loss = new Loss(lossVars, d);
-
-                    if (lossNames == null) {
-                        lossNames = lossVars;
-                    } else {
-                        Preconditions.checkState(lossNames.equals(lossVars),
-                                "Loss names mismatch, expected: %s, got: %s", lossNames, lossVars);
-                    }
-
-                    if (lossSums == null) {
-                        lossSums = d;
-                    } else {
-                        Preconditions.checkState(lossNames.equals(lossVars),
-                                "Loss size mismatch, expected: %s, got: %s", lossSums.length, d.length);
-
-                        for (int j = 0; j < lossSums.length; j++) {
-                            lossSums[j] += d[j];
-                        }
-                    }
-                    lossCount++;
-
                     for (Listener l : activeListeners) {
                         l.iterationDone(this, at, ds, loss);
                     }
@@ -2294,7 +2346,7 @@ public class SameDiff extends SDBaseOps {
 
             long epochTime = System.currentTimeMillis() - epochStartTime;
 
-            if (incrementEpochCount && hasListeners) {
+            if (incrementEpochCount) {
                 for (int j = 0; j < lossSums.length; j++)
                     lossSums[j] /= lossCount;
 
@@ -2341,7 +2393,7 @@ public class SameDiff extends SDBaseOps {
 
                         long validationStart = System.currentTimeMillis();
                         outputHelper(validationData, new At(at.epoch(), 0, 0, 0, Operation.TRAINING_VALIDATION),
-                                listeners);
+                                listenersWitHistory);
 
                         long validationTime = System.currentTimeMillis() - validationStart;
 
@@ -2958,7 +3010,7 @@ public class SameDiff extends SDBaseOps {
 
         List<String> neededOutputs;
 
-        if (outputs != null) {
+        if (outputs != null && outputs.length != 0) {
             neededOutputs = Arrays.asList(outputs);
         } else {
             neededOutputs = outputs();
@@ -3367,7 +3419,11 @@ public class SameDiff extends SDBaseOps {
      */
     public SDVariable var(@NonNull String name, @NonNull VariableType variableType, WeightInitScheme weightInitScheme,
                           org.nd4j.linalg.api.buffer.DataType dataType, long... shape) {
-
+        if(shape != null) {
+            for (long l : shape) {
+                Preconditions.checkArgument(l != 0, "Cannot create variable with a shape that contains zeros (empty array shape) - got shape %s", shape);
+            }
+        }
 
         if (name == null || name.length() < 1)
             name = getNewVarName();
@@ -3582,7 +3638,7 @@ public class SameDiff extends SDBaseOps {
         Preconditions.checkState(arr.dataType().isFPType(), "Cannot create variable with non-floating point type:" +
                 " provided array has datatype %s. Variables must be floating point type to be trainable by backpropagation.\n" +
                 "For non floating point types, these should be created as placeholders or constants instead.", arr.dataType());
-
+        Preconditions.checkArgument(!arr.isEmpty(), "Empty arrays cannot be used when creating variables. Array shape: %ndShape", arr);
 
         if (name == null || name.length() < 1)
             name = getNewVarName();
@@ -4614,8 +4670,8 @@ public class SameDiff extends SDBaseOps {
             }
         }
 
-        //Also add loss values - we need these so we can report them to listeners...
-        if (!listeners.isEmpty()) {
+        //Also add loss values - we need these so we can report them to listeners or loss curves...
+        if (!activeListeners.isEmpty() || op == Operation.TRAINING) {
             varGradNames.addAll(lossVariables);
         }
 
diff --git a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/samediff/config/OutputConfig.java b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/samediff/config/OutputConfig.java
index e56ab5988..376430f54 100644
--- a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/samediff/config/OutputConfig.java
+++ b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/samediff/config/OutputConfig.java
@@ -31,6 +31,9 @@ import org.nd4j.autodiff.util.TrainingUtils;
 import org.nd4j.base.Preconditions;
 import org.nd4j.linalg.api.ndarray.INDArray;
 import org.nd4j.linalg.dataset.adapter.MultiDataSetIteratorAdapter;
+import org.nd4j.linalg.dataset.adapter.SingletonMultiDataSetIterator;
+import org.nd4j.linalg.dataset.api.DataSet;
+import org.nd4j.linalg.dataset.api.MultiDataSet;
 import org.nd4j.linalg.dataset.api.iterator.DataSetIterator;
 import org.nd4j.linalg.dataset.api.iterator.MultiDataSetIterator;
 
@@ -94,6 +97,20 @@ public class OutputConfig {
         return this;
     }
 
+    /**
+     * Set the data to use as input.
+     */
+    public OutputConfig data(@NonNull DataSet data){
+        return data(new SingletonMultiDataSetIterator(data.toMultiDataSet()));
+    }
+
+    /**
+     * Set the data to use as input.
+     */
+    public OutputConfig data(@NonNull MultiDataSet data){
+        return data(new SingletonMultiDataSetIterator(data));
+    }
+
     /**
      * Add listeners for this operation
      */
diff --git a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/validation/OpValidation.java b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/validation/OpValidation.java
index 541b0a545..f52fbc2d9 100644
--- a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/validation/OpValidation.java
+++ b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/validation/OpValidation.java
@@ -16,6 +16,10 @@
 
 package org.nd4j.autodiff.validation;
 
+import org.nd4j.linalg.api.ops.impl.indexaccum.custom.ArgMax;
+import org.nd4j.linalg.api.ops.impl.indexaccum.custom.ArgMin;
+import org.nd4j.linalg.api.ops.impl.reduce.HashCode;
+import org.nd4j.linalg.api.ops.impl.scalar.RectifiedLinearDerivative;
 import org.nd4j.shade.guava.collect.ImmutableSet;
 import org.nd4j.shade.guava.reflect.ClassPath;
 import lombok.extern.slf4j.Slf4j;
@@ -253,7 +257,7 @@ public class OpValidation {
     public static void checkDeserializedEquality(SameDiff original, ByteBuffer bbSerialized, TestCase tc) {
         SameDiff deserialized;
         try{
-           deserialized = SameDiff.fromFlatBuffers(bbSerialized);
+            deserialized = SameDiff.fromFlatBuffers(bbSerialized);
         } catch (IOException e){
             throw new RuntimeException("IOException deserializing from FlatBuffers", e);
         }
@@ -900,6 +904,7 @@ public class OpValidation {
                 TanhDerivative.class,
                 org.nd4j.linalg.api.ops.impl.transforms.strict.TanhDerivative.class,
                 PowDerivative.class,
+                org.nd4j.linalg.api.ops.impl.scalar.RectifiedLinearDerivative.class,
                 org.nd4j.linalg.api.ops.impl.transforms.gradient.CubeBp.class,
                 org.nd4j.linalg.api.ops.impl.transforms.gradient.EluBp.class,
                 org.nd4j.linalg.api.ops.impl.transforms.gradient.HardSigmoidBp.class,
@@ -911,6 +916,8 @@ public class OpValidation {
                 org.nd4j.linalg.api.ops.impl.transforms.gradient.SoftPlusBp.class,
                 org.nd4j.linalg.api.ops.impl.transforms.gradient.SoftSignBp.class,
                 org.nd4j.linalg.api.ops.impl.transforms.gradient.ThresholdReluBp.class,
+                org.nd4j.linalg.api.ops.impl.transforms.pairwise.arithmetic.bp.ModBpOp.class,
+
 
                 BiasAddGrad.class,
                 ConcatBp.class,
@@ -976,7 +983,8 @@ public class OpValidation {
                 BarnesHutSymmetrize.class,
                 SpTreeCell.class,
                 CbowRound.class,
-                SkipGramRound.class
+                SkipGramRound.class,
+                HashCode.class
         );
 
         return new HashSet<>(list);
@@ -1026,11 +1034,21 @@ public class OpValidation {
                 IMax.class,
                 IMin.class,
                 LastIndex.class,
+                ArgMax.class,
+                ArgMin.class,
 
                 //Exclude ops that output integer types only:
                 Shape.class,
                 ShapeN.class,
                 SizeAt.class,
+                BroadcastDynamicShape.class,
+                ReductionShape.class,
+                ShiftBits.class,
+                RShiftBits.class,
+                BitsHammingDistance.class,
+                CyclicShiftBits.class,
+                CyclicRShiftBits.class,
+
 
                 //Exclude Random ops
                 RandomStandardNormal.class,
@@ -1209,7 +1227,7 @@ public class OpValidation {
                 "to_int64",
                 "to_uint32",
                 "to_uint64"
-                );
+        );
 
         return out;
     }
diff --git a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/imports/graphmapper/BaseGraphMapper.java b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/imports/graphmapper/BaseGraphMapper.java
index fe252aeeb..95f238973 100644
--- a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/imports/graphmapper/BaseGraphMapper.java
+++ b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/imports/graphmapper/BaseGraphMapper.java
@@ -16,6 +16,7 @@
 
 package org.nd4j.imports.graphmapper;
 
+import org.nd4j.linalg.util.ArrayUtil;
 import org.nd4j.shade.protobuf.Message;
 import org.nd4j.shade.protobuf.TextFormat;
 import lombok.extern.slf4j.Slf4j;
@@ -225,8 +226,8 @@ public abstract class BaseGraphMapper<GRAPH_TYPE, NODE_TYPE, ATTR_TYPE, TENSOR_T
                 //TODO work out which!
 
                 SDVariable v;
-                if(shape == null){
-                    //No shape -> probably not a variable...
+                if(shape == null || ArrayUtil.contains(shape, 0)){
+                    //No shape, or 0 in shape -> probably not a variable...
                     v = diff.var(entry.getKey(), VariableType.ARRAY, null, dt, (long[])null);
                 } else {
                     v = diff.var(entry.getKey(), dt, shape);
diff --git a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ndarray/BaseNDArray.java b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ndarray/BaseNDArray.java
index 126ba2466..9904168de 100644
--- a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ndarray/BaseNDArray.java
+++ b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ndarray/BaseNDArray.java
@@ -1724,12 +1724,6 @@ public abstract class BaseNDArray implements INDArray, Iterable {
         return z;
     }
 
-    /**
-     * Returns the elements at the specified indices
-     *
-     * @param indices the indices to getScalar
-     * @return the array with the specified elements
-     */
     @Override
     public int getInt(int... indices) {
         return (int) getDouble(indices);
@@ -1761,12 +1755,6 @@ public abstract class BaseNDArray implements INDArray, Iterable {
         return Shape.getLong(this, indices);
     }
 
-    /**
-     * Returns the elements at the specified indices
-     *
-     * @param indices the indices to get
-     * @return the array with the specified elements
-     */
     @Override
     public double getDouble(int... indices) {
         autoProcessScalarCall();
@@ -1815,12 +1803,6 @@ public abstract class BaseNDArray implements INDArray, Iterable {
         return Shape.getDouble(this, indices);
     }
 
-    /**
-     * Returns the elements at the specified indices
-     *
-     * @param indices the indices to get
-     * @return the array with the specified elements
-     */
     @Override
     public float getFloat(int... indices) {
         return (float) getDouble(indices);
@@ -1831,9 +1813,6 @@ public abstract class BaseNDArray implements INDArray, Iterable {
         return (float) getDouble(indices);
     }
 
-    /**
-     * Test whether a matrix is scalar.
-     */
     @Override
     public boolean isScalar() {
         if (isEmpty())
@@ -2000,11 +1979,6 @@ public abstract class BaseNDArray implements INDArray, Iterable {
         }
     }
 
-    /**
-     * Returns true if this ndarray is 2d
-     *
-     * @return true if the element is a matrix, false otherwise
-     */
     public boolean isMatrix() {
         return rank() == 2;
     }
@@ -2216,14 +2190,6 @@ public abstract class BaseNDArray implements INDArray, Iterable {
         return this;
     }
 
-    /**
-     * Mainly here for people coming from numpy.
-     * This is equivalent to a call to permute
-     *
-     * @param dimension the dimension to swap
-     * @param with      the one to swap it with
-     * @return the swapped axes view
-     */
     @Override
     public INDArray swapAxes(int dimension, int with) {
         int[] shape = ArrayUtil.range(0, shape().length);
@@ -2263,12 +2229,6 @@ public abstract class BaseNDArray implements INDArray, Iterable {
         this.data = data;
     }
 
-    /**
-     * Number of slices: aka shape[0]
-     *
-     * @return the number of slices
-     * for this nd array
-     */
     @Override
     public long slices() {
         return size(0);
@@ -3377,18 +3337,6 @@ public abstract class BaseNDArray implements INDArray, Iterable {
         return Shape.offset(jvmShapeInfo.javaShapeInformation) + (idx);
     }
 
-
-
-    /**
-     * Returns the specified slice of this matrix.
-     * In matlab, this would be equivalent to (given a 2 x 2 x 2):
-     * A(:,:,x) where x is the slice you want to return.
-     * <p/>
-     * The slice is always relative to the final dimension of the matrix.
-     *
-     * @param slice the slice to return
-     * @return the specified slice of this matrix
-     */
     @Override
     public INDArray slice(long slice) {
         Nd4j.getCompressor().autoDecompress(this);
@@ -3425,8 +3373,6 @@ public abstract class BaseNDArray implements INDArray, Iterable {
         return Nd4j.scalar(d);
     }
 
-
-
     @Override
     public int getTrailingOnes() {
         int numLeadingOnes = 0;
@@ -3438,8 +3384,6 @@ public abstract class BaseNDArray implements INDArray, Iterable {
         return numLeadingOnes;
     }
 
-
-
     @Override
     public int getLeadingOnes() {
         int numLeadingOnes = 0;
@@ -3451,16 +3395,6 @@ public abstract class BaseNDArray implements INDArray, Iterable {
         return numLeadingOnes;
     }
 
-
-
-    /**
-     * Returns the slice of this from the specified dimension
-     *
-     * @param slice     the dimension to return from
-     * @param dimension the dimension of the slice to return
-     * @return the slice of this matrix from the specified dimension
-     * and dimension
-     */
     @Override
     public INDArray slice(long slice, int dimension) {
         Nd4j.getCompressor().autoDecompress(this);
@@ -3490,12 +3424,6 @@ public abstract class BaseNDArray implements INDArray, Iterable {
 
     }
 
-    /**
-     * Fetch a particular number on a multi dimensional scale.
-     *
-     * @param indexes the indexes to get a number from
-     * @return the number at the specified indices
-     */
     @Override
     public INDArray getScalar(int[] indexes) {
         if (indexes.length > rank())
@@ -3717,9 +3645,6 @@ public abstract class BaseNDArray implements INDArray, Iterable {
         return (float) getDouble(i, j);
     }
 
-    /**
-     * Return transposed copy of this matrix.
-     */
     @Override
     public INDArray transpose() {
         Preconditions.checkState(rank() >= 2, "Can't transpose array with rank < 2: array shape %ndShape", this);
@@ -3727,7 +3652,6 @@ public abstract class BaseNDArray implements INDArray, Iterable {
         return permute(ArrayUtil.reverseCopy(ArrayUtil.range(0, rank())));
     }
 
-
     /**
      *
      * Return transposed version of this matrix.
@@ -4047,12 +3971,6 @@ public abstract class BaseNDArray implements INDArray, Iterable {
         return norm2(false, dimension);
     }
 
-    /**
-     * Number of columns (shape[1]), throws an exception when
-     * called when not 2d
-     *
-     * @return the number of columns in the array (only 2d)
-     */
     @Override
     public int columns() {
         // FIXME: int cast
@@ -4068,13 +3986,6 @@ public abstract class BaseNDArray implements INDArray, Iterable {
 
     }
 
-    /**
-     * Returns the number of rows
-     * in the array (only 2d) throws an exception when
-     * called when not 2d
-     *
-     * @return the number of rows in the matrix
-     */
     @Override
     public int rows() {
         // FIXME:
@@ -4089,12 +4000,6 @@ public abstract class BaseNDArray implements INDArray, Iterable {
         throw new IllegalStateException("Rank is " + rank() + " rows() call is not valid");
     }
 
-
-    /**
-     * Flattens the array for linear indexing
-     *
-     * @return the flattened version of this array
-     */
     @Override
     public INDArray ravel(char ordering) {
         Nd4j.getCompressor().autoDecompress(this);
@@ -4104,11 +4009,6 @@ public abstract class BaseNDArray implements INDArray, Iterable {
         return dup(ordering).reshape(ordering, length());
     }
 
-    /**
-     * Flattens the array for linear indexing
-     *
-     * @return the flattened version of this array
-     */
     @Override
     public INDArray ravel() {
         return reshape(length());
@@ -4125,22 +4025,11 @@ public abstract class BaseNDArray implements INDArray, Iterable {
         }
     }
 
-    /**
-     * Reshape the matrix. Number of elements must not change.
-     *
-     * @param newRows
-     * @param newColumns
-     */
     @Override
     public INDArray reshape(long newRows, long newColumns) {
         return reshape(new long[] {newRows, newColumns});
     }
 
-    /**
-     * Get the specified column
-     *
-     * @param c
-     */
     @Override
     public INDArray getColumn(long c) {
         Nd4j.getCompressor().autoDecompress(this);
@@ -4383,11 +4272,6 @@ public abstract class BaseNDArray implements INDArray, Iterable {
         return create(new int[] {rows, length});
     }
 
-    /**
-     * Get a copy of a row.
-     *
-     * @param r the row to get
-     */
     @Override
     public INDArray getRow(long r) {
         if (isRowVector() && r == 0)
@@ -4409,14 +4293,6 @@ public abstract class BaseNDArray implements INDArray, Iterable {
         return row.reshape(1, row.length());
     }
 
-
-    /**
-     * This method allows you to compare INDArray against other INDArray, with variable eps
-     *
-     * @param o
-     * @param eps
-     * @return
-     */
     public boolean equalsWithEps(Object o, double eps) {
         Nd4j.getCompressor().autoDecompress(this);
 
@@ -4563,11 +4439,6 @@ public abstract class BaseNDArray implements INDArray, Iterable {
         return shapeInformation.asNioLong();
     }
 
-    /**
-     * Returns the shape(dimensions) of this array
-     *
-     * @return the shape of this matrix
-     */
     public long[] shape() {
         return jvmShapeInfo.shape;
     }
@@ -4577,11 +4448,6 @@ public abstract class BaseNDArray implements INDArray, Iterable {
         return Shape.shapeToString(this);
     }
 
-    /**
-     * Returns the stride(indices along the linear index for which each slice is accessed) of this array
-     *
-     * @return the stride of this array
-     */
     @Override
     public long[] stride() {
         return jvmShapeInfo.stride;
@@ -4598,13 +4464,6 @@ public abstract class BaseNDArray implements INDArray, Iterable {
         return jvmShapeInfo.order;
     }
 
-    /**
-     * Returns the size of this array
-     * along a particular dimension
-     *
-     * @param dimension the dimension to return from
-     * @return the shape of the specified dimension
-     */
     @Override
     public long size(int dimension) {
         if (dimension < 0)
@@ -4629,11 +4488,6 @@ public abstract class BaseNDArray implements INDArray, Iterable {
         return jvmShapeInfo.rank;
     }
 
-    /**
-     * Returns the total number of elements in the ndarray
-     *
-     * @return the number of elements in the ndarray
-     */
     @Override
     public long length() {
         if (isEmpty())
@@ -4745,12 +4599,6 @@ public abstract class BaseNDArray implements INDArray, Iterable {
 
     }
 
-    /**
-     * Broadcasts this ndarray to be the specified shape
-     *
-     * @param shape the new shape of this ndarray
-     * @return the broadcasted ndarray
-     */
     @Override
     public INDArray broadcast(long... shape) {
       return broadcast(Nd4j.createUninitialized(this.dataType(), shape, this.ordering()));
@@ -4877,12 +4725,6 @@ public abstract class BaseNDArray implements INDArray, Iterable {
 
     }
 
-    /**
-     * See: http://www.mathworks.com/help/matlab/ref/permute.html
-     *
-     * @param rearrange the dimensions to swap to
-     * @return the newly permuted array
-     */
     @Override
     public INDArray permute(int... rearrange) {
         Preconditions.checkArgument(rearrange.length == rank(), "Incorrect number of arguments for permute function:" +
@@ -4911,14 +4753,6 @@ public abstract class BaseNDArray implements INDArray, Iterable {
         return value;
     }
 
-    /**
-     * An <b>in-place</b> version of permute. The array  shape information (shape, strides)
-     * is modified by this operation (but not the data itself)
-     * See: http://www.mathworks.com/help/matlab/ref/permute.html
-     *
-     * @param rearrange the dimensions to swap to
-     * @return the current array
-     */
     @Override
     public INDArray permutei(int... rearrange) {
         Preconditions.checkArgument(rearrange.length == rank(), "Incorrect number of arguments for permute function:" +
@@ -5047,17 +4881,11 @@ public abstract class BaseNDArray implements INDArray, Iterable {
         return isMatrix() && rows() == columns();
     }
 
-    /**
-     * Checks whether the matrix is a row vector.
-     */
     @Override
     public boolean isRowVector() {
         return (rank() == 2 && rows() == 1) && length() > 1 || rank() == 1 && length() > 1;
     }
 
-    /**
-     * Checks whether the matrix is a column vector.
-     */
     @Override
     public boolean isColumnVector() {
         return rank() == 2 && columns() == 1 && length() > 1;
@@ -5112,12 +4940,6 @@ public abstract class BaseNDArray implements INDArray, Iterable {
         return toString(Long.MAX_VALUE, false, -1 * dataType().precision());
     }
 
-    /**
-     * Returns a scalar (individual element)
-     * of a scalar ndarray
-     *
-     * @return the individual item in this ndarray
-     */
     @Override
     public Object element() {
 
@@ -5236,11 +5058,6 @@ public abstract class BaseNDArray implements INDArray, Iterable {
         return new FirstAxisIterator(this);
     }
 
-    /**
-     * Returns the start of where the ndarray is for the original data buffer
-     *
-     * @return
-     */
     @Override
     public long originalOffset() {
         if (data().originalOffset() >= Integer.MAX_VALUE)
@@ -5294,24 +5111,11 @@ public abstract class BaseNDArray implements INDArray, Iterable {
         data().read(s, headerData.getLeft(), headerData.getMiddle(), headerData.getRight());
     }
 
-
-    /**
-     * This method returns index of highest value along specified dimension(s)
-     *
-     * @param dimension
-     * @return
-     */
     @Override
     public INDArray argMax(int... dimension) {
         return Nd4j.argMax(this, dimension);
     }
 
-
-    /**
-     * This method returns True, if this INDArray instance is attached to some Workspace. False otherwise.
-     *
-     * @return
-     */
     @Override
     public boolean isAttached() {
         if (isEmpty())
@@ -5324,13 +5128,6 @@ public abstract class BaseNDArray implements INDArray, Iterable {
                 (data.originalDataBuffer() != null && data.originalDataBuffer().isAttached());
     }
 
-    /**
-     * This method checks, if given attached INDArray is still in scope of its parent Workspace
-     * <p>
-     * PLEASE NOTE: if this INDArray isn't attached to any Workspace, this method will return true
-     *
-     * @return
-     */
     @Override
     public boolean isInScope() {
         if (!isAttached())
@@ -5339,13 +5136,6 @@ public abstract class BaseNDArray implements INDArray, Iterable {
         return data.isInScope();
     }
 
-    /**
-     * This metod detaches INDArray from Workspace, returning copy. Basically it's dup() into new memory chunk.
-     * <p>
-     * PLEASE NOTE: If this INDArray instance is NOT attached - it will be returned unmodified.
-     *
-     * @return
-     */
     @Override
     public INDArray detach() {
         if (!isAttached())
@@ -5402,14 +5192,6 @@ public abstract class BaseNDArray implements INDArray, Iterable {
         }
     }
 
-    /**
-     * This method detaches INDArray from current Workspace, and attaches it to Workspace above, if any.
-     * <p>
-     * PLEASE NOTE: If this INDArray instance is NOT attached - it will be returned unmodified.
-     * PLEASE NOTE: If current Workspace is the top-tier one, effect will be equal to detach() call - detached copy will be returned
-     *
-     * @return
-     */
     @Override
     public INDArray leverage() {
         WorkspaceUtils.assertValidArray(this, "Cannot leverage INDArray to new workspace");
@@ -5453,33 +5235,11 @@ public abstract class BaseNDArray implements INDArray, Iterable {
         }
     }
 
-    /**
-     * This method detaches INDArray from current Workspace, and attaches it to Workspace with a given Id
-     *
-     * PLEASE NOTE: If this INDArray instance is NOT attached - it will be returned unmodified.
-     * PLEASE NOTE: If Workspace with target Id wasn't created before - this array will be returned unmodified.
-     * PLEASE NOTE: If target workspace is the current one - this array will be returned unmodified.
-     *
-     * @param id
-     * @return
-     */
     @Override
     public INDArray leverageTo(String id) {
         return leverageTo(id, false);
     }
 
-    /**
-     * This method detaches INDArray from current Workspace, and attaches it to Workspace with a given Id.
-     * If enforceExistence == true, and no workspace with the specified ID exists, then an {@link Nd4jNoSuchWorkspaceException}
-     * is thrown. Otherwise, if enforceExistance == false and no workspace with the specified ID exists, then the current
-     * INDArray is returned unmodified (same as {@link #leverage()}
-     *
-     * @param id ID of the workspace to leverage to
-     * @param enforceExistence If true, and the specified workspace does not exist: an {@link Nd4jNoSuchWorkspaceException}
-     *                         will be thrown.
-     * @return The INDArray, leveraged to the specified workspace
-     * @see #leverageTo(String)
-     */
     @Override
     public INDArray leverageTo(String id, boolean enforceExistence) throws Nd4jNoSuchWorkspaceException {
         WorkspaceUtils.assertValidArray(this, "Cannot leverage INDArray to new workspace");
@@ -5518,16 +5278,6 @@ public abstract class BaseNDArray implements INDArray, Iterable {
         return copy;
     }
 
-    /**
-     * This method detaches INDArray from current Workspace, and attaches it to Workspace with a given Id, if a workspace
-     * with the given ID is open and active.
-     *
-     * If the workspace does not exist, or is not active, the array is detached from any workspaces.
-     *
-     * @param id ID of the workspace to leverage to
-     * @return The INDArray, leveraged to the specified workspace (if it exists and is active) otherwise the detached array
-     * @see #leverageTo(String)
-     */
     public INDArray leverageOrDetach(String id){
         if(!isAttached()){
             return this;
@@ -5539,30 +5289,11 @@ public abstract class BaseNDArray implements INDArray, Iterable {
         return leverageTo(id);
     }
 
-    /**
-     * This method pulls this INDArray into current Workspace.
-     *
-     * PLEASE NOTE: If there's no current Workspace - INDArray returned as is
-     *
-     * @return Migrated INDArray or <i>this</i> if no current workspace
-     * @see #migrate(boolean)
-     */
     @Override
     public INDArray migrate() {
         return migrate(false);
     }
 
-    /**
-     * This method pulls this INDArray into current Workspace, or optionally detaches if no workspace is present.<br>
-     * That is:<br>
-     * If current workspace is present/active, INDArray is migrated to it.<br>
-     * If no current workspace is present/active, one of two things occur:
-     * 1. If detachOnNoWs arg is true: if there is no current workspace, INDArray is detached
-     * 2. If detachOnNoWs arg is false: this INDArray is returned as-is (no-op) - equivalent to {@link #migrate()}
-     *
-     * @param detachOnNoWs If true: detach on no WS. If false and no workspace: return this.
-     * @return Migrated INDArray
-     */
     @Override
     public INDArray migrate(boolean detachOnNoWs){
         WorkspaceUtils.assertValidArray(this, "Cannot leverage INDArray to new workspace");
@@ -5778,17 +5509,11 @@ public abstract class BaseNDArray implements INDArray, Iterable {
             throw new IllegalStateException("Unknown dataType: [" + type + "]");
     }
 
-    /**
-     * This method returns true if this INDArray is special case: no-value INDArray
-     *
-     * @return
-     */
     @Override
     public boolean isEmpty() {
         return Shape.isEmpty(jvmShapeInfo.javaShapeInformation);
     }
 
-
     @Override
     public long[] shapeInfoJava() {
         return jvmShapeInfo.javaShapeInformation;
@@ -5880,8 +5605,6 @@ public abstract class BaseNDArray implements INDArray, Iterable {
             throw new IllegalStateException("Cannot perform operation " + opName + " on empty array with datatype " + dataType());
     }
 
-
-
     @Override
     public boolean closeable() {
         if (released || isAttached())
diff --git a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ndarray/BaseSparseNDArray.java b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ndarray/BaseSparseNDArray.java
index c9c5cab37..ebe768cac 100644
--- a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ndarray/BaseSparseNDArray.java
+++ b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ndarray/BaseSparseNDArray.java
@@ -1296,7 +1296,7 @@ public abstract class BaseSparseNDArray implements ISparseNDArray {
     }
 
     @Override
-    public float getFloat(long[] indices) {
+    public float getFloat(long... indices) {
         return 0;
     }
 
@@ -1321,7 +1321,7 @@ public abstract class BaseSparseNDArray implements ISparseNDArray {
     }
 
     @Override
-    public float getFloat(int[] indices) {
+    public float getFloat(int... indices) {
         return 0;
     }
 
@@ -1514,23 +1514,16 @@ public abstract class BaseSparseNDArray implements ISparseNDArray {
         return isMatrix() && rows() == columns();
     }
 
-    /**
-     * Checks whether the matrix is a row vector.
-     */
     @Override
     public boolean isRowVector() {
         return rank == 2 && rows == 1;
     }
 
-    /**
-     * Checks whether the matrix is a column vector.
-     */
     @Override
     public boolean isColumnVector() {
         return rank == 2 && columns == 1;
     }
 
-
     @Override
     public boolean isMatrix() {
         if (isMatrix != null)
diff --git a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ndarray/BaseSparseNDArrayCOO.java b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ndarray/BaseSparseNDArrayCOO.java
index 85a7ec5ce..b50200dbb 100644
--- a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ndarray/BaseSparseNDArrayCOO.java
+++ b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ndarray/BaseSparseNDArrayCOO.java
@@ -683,12 +683,12 @@ public class BaseSparseNDArrayCOO extends BaseSparseNDArray {
     }
 
     @Override
-    public float getFloat(int[] indices) {
+    public float getFloat(int... indices) {
         return (float) getDouble(indices);
     }
 
     @Override
-    public float getFloat(long[] indices) {
+    public float getFloat(long... indices) {
         return 0;
     }
 
@@ -860,11 +860,6 @@ public class BaseSparseNDArrayCOO extends BaseSparseNDArray {
         return Nd4j.createBuffer(Doubles.toArray(val));
     }
 
-    /**
-     * Returns the indices of non-zero element of the vector
-     *
-     * @return indices in Databuffer
-     * */
     @Override
     public DataBuffer getVectorCoordinates() {
         int idx;
@@ -884,10 +879,6 @@ public class BaseSparseNDArrayCOO extends BaseSparseNDArray {
         return Nd4j.createBuffer(temp);
     }
 
-    /**
-     * Converts the sparse ndarray into a dense one
-     * @return a dense ndarray
-     */
     @Override
     public INDArray toDense() {
         // TODO support view conversion
@@ -1131,11 +1122,6 @@ public class BaseSparseNDArrayCOO extends BaseSparseNDArray {
         return null;
     }
 
-    /**
-     * This method returns true if this INDArray is special case: no-value INDArray
-     *
-     * @return
-     */
     @Override
     public boolean isEmpty() {
         return false;
diff --git a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ndarray/INDArray.java b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ndarray/INDArray.java
index f9f04cc43..23e7f2fcc 100644
--- a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ndarray/INDArray.java
+++ b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ndarray/INDArray.java
@@ -1912,6 +1912,9 @@ public interface INDArray extends Serializable, AutoCloseable {
      */
     INDArray getScalar(int... indices);
 
+    /**
+     * See {@link #getScalar(int[])}
+     */
     INDArray getScalar(long... indices);
 
     /**
@@ -1921,12 +1924,32 @@ public interface INDArray extends Serializable, AutoCloseable {
      */
     int getInt(int... indices);
 
+    /**
+     * Get a long value at the specified index.
+     * @param index Index to get the integer at.
+     * @return long value at the specified index
+     */
     long getLong(long index);
 
+    /**
+     * Get a long value at the specified indices.
+     * @param indices Indices to get the double at. Number of indices must match the array rank.
+     * @return long value at the specified index
+     */
     long getLong(long... indices);
 
+    /**
+     * Get the numeric value at the specified index.
+     * @param index index to retreive.
+     * @return numeric value at the specified index.
+     */
     Number getNumber(long index);
 
+    /**
+     * Get a numeric value at the specified indices.
+     * @param indices Indices to get the value from. Number of indices must match the array rank.
+     * @return Numeric value at the specified index
+     */
     Number getNumber(long... indices);
 
     /**
@@ -1936,6 +1959,9 @@ public interface INDArray extends Serializable, AutoCloseable {
      */
     double getDouble(int... indices);
 
+    /**
+     * See {@link #getDouble(int[])}
+     */
     double getDouble(long... indices);
 
     /**
@@ -1944,10 +1970,12 @@ public interface INDArray extends Serializable, AutoCloseable {
      * @param indices the indices to getScalar
      * @return the array with the specified elements
      */
-    float getFloat(int[] indices);
-
-    float getFloat(long[] indices);
+    float getFloat(int... indices);
 
+    /**
+     * See {@link #getFloat(int...)}
+     */
+    float getFloat(long... indices);
 
     /**
      * Get the double value at the specified linear index in the array
@@ -1962,7 +1990,7 @@ public interface INDArray extends Serializable, AutoCloseable {
      *
      * @param i Dimension 0 (row) index
      * @param j Dimension 1 (column) index
-     * @return
+     * @return double value at the specified indices
      */
     double getDouble(long i, long j);
 
@@ -1984,7 +2012,6 @@ public interface INDArray extends Serializable, AutoCloseable {
      */
     float getFloat(long i, long j);
 
-
     /**
      * Returns a copy of this ndarray
      *
@@ -2007,7 +2034,6 @@ public interface INDArray extends Serializable, AutoCloseable {
      */
     INDArray ravel();
 
-
     /**
      * Returns a flattened version (row vector) of this ndarray
      *
@@ -2015,10 +2041,9 @@ public interface INDArray extends Serializable, AutoCloseable {
      */
     INDArray ravel(char order);
 
-
     /**
-     *
-     * @param data
+     * Set the data for this ndarray.
+     * @param data new value for the ndarray data.
      */
     void setData(DataBuffer data);
 
@@ -2046,15 +2071,15 @@ public interface INDArray extends Serializable, AutoCloseable {
     int getLeadingOnes();
 
     /**
-     * Returns the specified slice of this ndarray
+     * Returns the slice of this from the specified dimension
      *
-     * @param i         the index of the slice to return
-     * @param dimension the dimension to return the slice for
-     * @return the specified slice of this ndarray
+     * @param i the index of the slice to return
+     * @param dimension the dimension of the slice to return
+     * @return the slice of this matrix from the specified dimension
+     * and dimension
      */
     INDArray slice(long i, int dimension);
 
-
     /**
      * Returns the specified slice of this ndarray
      *
@@ -2063,23 +2088,21 @@ public interface INDArray extends Serializable, AutoCloseable {
      */
     INDArray slice(long i);
 
-
     /**
-     * Returns the start of where the ndarray is
-     * for the underlying data
+     * Returns the start of where the ndarray is for the underlying data
      *
      * @return the starting offset
      */
     long offset();
 
-
+    // TODO: Unused untested method.
     /**
      * Returns the start of where the ndarray is for the original data buffer
-     * @return
+     *
+     * @return original offset.
      */
     long originalOffset();
 
-
     /**
      * Reshapes the ndarray (can't change the length of the ndarray). Typically this will be a view, unless reshaping
      * without copying is impossible.
@@ -2110,7 +2133,6 @@ public interface INDArray extends Serializable, AutoCloseable {
      */
     INDArray reshape(char order, boolean enforceView, long... newShape);
 
-
     /**
      * Reshapes the ndarray (can't change the length of the ndarray). Typically this will be a view, unless reshaping
      * without copying is impossible.
@@ -2121,7 +2143,6 @@ public interface INDArray extends Serializable, AutoCloseable {
      */
     INDArray reshape(char order, int rows, int columns);
 
-
     /**
      * Reshapes the ndarray (can't change the length of the ndarray). Typically this will be a view, unless reshaping
      * without copying is impossible.
@@ -2131,6 +2152,9 @@ public interface INDArray extends Serializable, AutoCloseable {
      */
     INDArray reshape(long... newShape);
 
+    /**
+     * See {@link #reshape(long[])}
+     */
     INDArray reshape(int[] shape);
 
     /**
@@ -2150,7 +2174,6 @@ public interface INDArray extends Serializable, AutoCloseable {
      */
     INDArray transpose();
 
-
     /**
      * Flip the rows and columns of a matrix, in-place
      *
@@ -2216,8 +2239,11 @@ public interface INDArray extends Serializable, AutoCloseable {
      * @return the newly permuted array
      */
     INDArray dimShuffle(Object[] rearrange, int[] newOrder, boolean[] broadCastable);
-    INDArray dimShuffle(Object[] rearrange, long[] newOrder, boolean[] broadCastable);
 
+    /**
+     * See {@link #dimShuffle(Object[], int[], boolean[])
+     */
+    INDArray dimShuffle(Object[] rearrange, long[] newOrder, boolean[] broadCastable);
 
     /**
      * Returns the specified column.
@@ -2336,7 +2362,6 @@ public interface INDArray extends Serializable, AutoCloseable {
      */
     boolean isScalar();
 
-
     /**
      * Returns the shape of this ndarray
      *
@@ -2346,7 +2371,7 @@ public interface INDArray extends Serializable, AutoCloseable {
 
     /**
      * Returns shape descriptor of this ndarray
-     * @return
+     * @return shape descriptor
      */
     LongShapeDescriptor shapeDescriptor();
 
@@ -2386,7 +2411,6 @@ public interface INDArray extends Serializable, AutoCloseable {
      */
     INDArray broadcast(long... shape);
 
-
     /**
      * Broadcasts this ndarray to be the specified shape
      *
@@ -2394,7 +2418,6 @@ public interface INDArray extends Serializable, AutoCloseable {
      */
     INDArray broadcast(INDArray result);
 
-
     /**
      * Returns a scalar (individual element)
      * of a scalar ndarray
@@ -2413,9 +2436,9 @@ public interface INDArray extends Serializable, AutoCloseable {
     /**
      * This method checks 2 INDArrays equality with given eps
      *
-     * @param o
+     * @param o   INDArray to compare against.
      * @param eps Epsilon value to use for the quality operation
-     * @return
+     * @return True if ndarrays are equal within eps.
      */
     boolean equalsWithEps(Object o, double eps);
 
@@ -2448,7 +2471,7 @@ public interface INDArray extends Serializable, AutoCloseable {
     /**
      * Remainder operator
      * @param denominator the denominator
-     * @return
+     * @return remainder
      */
     INDArray remainder(INDArray denominator);
 
@@ -2456,75 +2479,86 @@ public interface INDArray extends Serializable, AutoCloseable {
      * Remainder operator
      * @param denominator the denominator
      * @param result the result array to put this in
-     * @return
+     * @return Remainder
      */
     INDArray remainder(INDArray denominator, INDArray result);
 
     /**
-     * The scalar denominator
+     * The scalar remainder
      * @param denominator the denominator as a scalar
-     * @return
+     * @return Remainder
      */
     INDArray remainder(Number denominator);
 
     /**
-     *
-     * @param denominator
-     * @param result
-     * @return
+     * The scalar remainder
+     * @param denominator the denominator as a scalar
+     * @param result the result array to put this in
+     * @return Remainder
      */
     INDArray remainder(Number denominator, INDArray result);
 
+    // TODO: Unused untested method.
     /**
      * In place remainder
-     * @param denominator
-     * @return
+     * @param denominator the denominator
+     * @return Remainder
      */
     INDArray remainderi(INDArray denominator);
 
+    // TODO: Unused untested method.
     /**
      * In place remainder
-     * @param denominator
-     * @return
+     * @param denominator the denominator
+     * @return Remainder
      */
     INDArray remainderi(Number denominator);
 
     /**
      * remainder of division
-     * @param denominator the array of denominators for each element
-     *                    in this array
-     * @return
+     * @param denominator the array of denominators for each element in this array
+     * @return array of remainders
      */
     INDArray fmod(INDArray denominator);
 
     /**
      *  remainder of division
-     * @param denominator the
+     * @param denominator the array of denominators for each element in this array
      * @param result the result array
-     * @return
+     * @return array of remainders
      */
     INDArray fmod(INDArray denominator, INDArray result);
 
     /**
+     * remainder of division by scalar.
      *
-     * @param denominator
-     * @return
+     * @param denominator the denominator
+     * @return array of remainders
      */
     INDArray fmod(Number denominator);
 
+    /**
+     * remainder of division by scalar.
+     *
+     * @param denominator the denominator
+     * @param result the result array
+     * @return array of remainders
+     */
     INDArray fmod(Number denominator, INDArray result);
 
+    // TODO: Unused untested method.
     /**
      * In place fmod
-     * @param denominator
-     * @return
+     * @param denominator the array of denominators for each element in this array
+     * @return array of remainders
      */
     INDArray fmodi(INDArray denominator);
 
+    // TODO: Unused untested method.
     /**
      * In place fmod
-     * @param denominator
-     * @return
+     * @param denominator the denominator as a scalar
+     * @return array of remainders
      */
     INDArray fmodi(Number denominator);
 
@@ -2546,7 +2580,7 @@ public interface INDArray extends Serializable, AutoCloseable {
      * This method checks, if given attached INDArray is still in scope of its parent Workspace
      *
      * PLEASE NOTE: if this INDArray isn't attached to any Workspace, this method will return true
-     * @return
+     * @return true if attached to workspace.
      */
     boolean isInScope();
 
@@ -2563,12 +2597,11 @@ public interface INDArray extends Serializable, AutoCloseable {
     /**
      * This method detaches INDArray from current Workspace, and attaches it to Workspace above, if any.
      *
-     * PLEASE NOTE: If this INDArray instance is
-     * NOT attached - it will be returned unmodified.
+     * PLEASE NOTE: If this INDArray instance is NOT attached - it will be returned unmodified.
      * PLEASE NOTE: If current Workspace is the top-tier one,
      * effect will be equal to detach() call - detached copy will be returned
      *
-     * @return
+     * @return this ndarray or a detached copy.
      */
     INDArray leverage();
 
@@ -2576,8 +2609,6 @@ public interface INDArray extends Serializable, AutoCloseable {
      * This method detaches INDArray from current Workspace, and attaches it to Workspace with a given Id - if a workspace
      * with that ID exists. If no workspace with the specified ID exists, the current INDArray is returned unmodified.
      *
-     * @param id ID of the workspace to leverage to
-     * @return
      * @see #leverageTo(String, boolean)
      */
     INDArray leverageTo(String id);
@@ -2635,7 +2666,7 @@ public interface INDArray extends Serializable, AutoCloseable {
      * This method returns percentile value for this INDArray
      *
      * @param percentile target percentile in range of 0..100
-     * @return
+     * @return percentile value
      */
     Number percentileNumber(Number percentile);
 
@@ -2648,29 +2679,28 @@ public interface INDArray extends Serializable, AutoCloseable {
 
     /**
      * This method returns median along given dimension(s)
-     * @param dimension
+     * @param dimension Dimension to calculate median
      * @return Median along specified dimensions
      */
     INDArray median(int... dimension);
 
     /**
-     * This method returns median along given dimension(s)
+     * This method returns percentile along given dimension(s)
      * @param percentile target percentile in range of 0..100
      * @param dimension  Dimension to calculate percentile for
-     * @return
+     * @return array with percentiles
      */
     INDArray percentile(Number percentile, int... dimension);
 
-    /**
+    /*
      * ------------ Sparse methods ------------
      */
 
-
     /**
      * Return a array of non-major pointers
      * i.e. return the column indexes in case of row-major ndarray
      * @return a DataBuffer of indexes
-     * */
+     */
     DataBuffer getVectorCoordinates();
 
     /**
@@ -2681,7 +2711,7 @@ public interface INDArray extends Serializable, AutoCloseable {
     /**
      * Return the number of non-null element
      * @return nnz
-     * */
+     */
     int nnz();
 
     /**
@@ -2691,15 +2721,18 @@ public interface INDArray extends Serializable, AutoCloseable {
      * */
     SparseFormat getFormat();
 
+    //TODO: Undocumented but often used method.
     int[] flags();
 
+    //TODO: Undocumented but often used method.
     int[] hiddenDimensions();
 
+    //TODO: Undocumented but often used method.
     int[] sparseOffsets();
 
+    //TODO: Undocumented but often used method.
     int underlyingRank();
 
-
     /**
      * Add an {@link INDArray}
      * to flatbuffers builder
@@ -2710,19 +2743,19 @@ public interface INDArray extends Serializable, AutoCloseable {
 
     /**
      * This method returns true if this INDArray is special case: no-value INDArray
-     * @return
+     * @return True if empty.
      */
     boolean isEmpty();
 
     /**
      * This method returns shapeInformation as jvm long array
-     * @return
+     * @return shapeInformation
      */
     long[] shapeInfoJava();
 
     /**
      * This method returns dtype for this INDArray
-     * @return
+     * @return Datattype
      */
     DataType dataType();
 
@@ -2734,45 +2767,45 @@ public interface INDArray extends Serializable, AutoCloseable {
 
     /**
      * This method checks if this INDArray instance is one of integer types
-     * @return
+     * @return true if integer type
      */
     boolean isZ();
 
     /**
      * This method checks if this INDArray instance has boolean type
-     * @return
+     * @return true if boolean type.
      */
     boolean isB();
 
     /**
      * This method checks if this INDArray instance has String type
-     * @return
+     * @return true if string type.
      */
     boolean isS();
 
     /**
      * This method cast elements of this INDArray to new data type
      *
-     * @param dataType
-     * @return
+     * @param dataType new datatype.
+     * @return this if datatype matches, otherwise a new array of specified datatype.
      */
     INDArray castTo(DataType dataType);
 
     /**
      * This method checks if all elements within this array are non-zero (or true, in case of boolean)
-     * @return
+     * @return true if all non-zero.
      */
     boolean all();
 
     /**
      * This method checks if any of the elements within this array are non-zero (or true, in case of boolean)
-     * @return
+     * @return true if any non-zero.
      */
     boolean any();
 
     /**
      * This method checks if any of the elements within this array are non-zero (or true, in case of boolean)
-     * @return
+     * @return true if any non-zero
      */
     boolean none();
 
@@ -2798,35 +2831,29 @@ public interface INDArray extends Serializable, AutoCloseable {
 
     /**
      * This method returns empty array with the same dtype/order/shape as this one
-     * @return
+     * @return empty array with the same dtype/order/shape
      */
     INDArray like();
 
     /**
      * This method returns uninitialized array with the same dtype/order/shape as this one
-     * @return
+     * @return uninitialized array with the same dtype/order/shape
      */
     INDArray ulike();
 
-    /**
-     * This method returns array with gains for Barnes-Hut-Tsne algorithm
-     * @return
-     */
-    //INDArray[] gains(INDArray input, INDArray gradx, INDArray epsilon);
-
     /**
      * Get a string representation of the array with configurable formatting
      * @param options format options
      */
     String toString(@NonNull NDArrayStrings options);
 
-
     /**
      * Get a string representation of the array
      *
      * @param maxElements Summarize if more than maxElements in the array
      * @param forceSummarize Force a summary instead of a full print
      * @param precision The number of decimals to print.  Doesn't print trailing 0s if negative
+     * @return string representation of the array
      */
     String toString(long maxElements, boolean forceSummarize, int precision);
 
diff --git a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ndarray/ISparseNDArray.java b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ndarray/ISparseNDArray.java
index e7dd5d1db..7eac57c8a 100644
--- a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ndarray/ISparseNDArray.java
+++ b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ndarray/ISparseNDArray.java
@@ -28,28 +28,11 @@ public interface ISparseNDArray extends INDArray {
     *
     * */
 
-    /**
-     * Return a array of non-major pointers
-     * i.e. return the column indexes in case of row-major ndarray
-     * @return a DataBuffer of indexes
-     * */
     DataBuffer getVectorCoordinates();
 
-    /**
-     * Return a dense representation of the sparse ndarray
-     * */
     INDArray toDense();
 
-    /**
-     * Return the number of non-null element
-     * @return nnz
-     * */
     int nnz();
 
-    /**
-     * Return the sparse format (i.e COO, CSR, ...)
-     * @return format
-     * @see SparseFormat
-     * */
     SparseFormat getFormat();
 }
diff --git a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/shape/tensorops/TensorArrayConcat.java b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/shape/tensorops/TensorArrayConcat.java
index 7759f96dd..5b3584d11 100644
--- a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/shape/tensorops/TensorArrayConcat.java
+++ b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/shape/tensorops/TensorArrayConcat.java
@@ -37,14 +37,14 @@ public class TensorArrayConcat extends BaseTensorOp {
     }
 
     public TensorArrayConcat(){}
-   @Override
+    @Override
     public String onnxName() {
         throw new NoOpNameFoundException("No onnx op name found for " + opName());
     }
 
     @Override
-    public String tensorflowName() {
-        return "TensorArrayConcatV3";
+    public String[] tensorflowNames() {
+        return new String[]{"TensorArrayConcat", "TensorArrayConcatV2", "TensorArrayConcatV3"};
     }
 
 
diff --git a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/shape/tensorops/TensorArrayGather.java b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/shape/tensorops/TensorArrayGather.java
index 9e7669725..185ef429a 100644
--- a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/shape/tensorops/TensorArrayGather.java
+++ b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/shape/tensorops/TensorArrayGather.java
@@ -37,14 +37,14 @@ public class TensorArrayGather extends BaseTensorOp {
     }
 
     public TensorArrayGather(){}
-   @Override
+    @Override
     public String onnxName() {
         throw new NoOpNameFoundException("No onnx op name found for " + opName());
     }
 
     @Override
-    public String tensorflowName() {
-        return "TensorArrayGatherV3";
+    public String[] tensorflowNames() {
+        return new String[]{"TensorArrayGather", "TensorArrayGatherV2", "TensorArrayGatherV3"};
     }
 
 
diff --git a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/shape/tensorops/TensorArrayRead.java b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/shape/tensorops/TensorArrayRead.java
index 6d8cff91c..2b8114543 100644
--- a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/shape/tensorops/TensorArrayRead.java
+++ b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/shape/tensorops/TensorArrayRead.java
@@ -42,9 +42,10 @@ public class TensorArrayRead extends BaseTensorOp {
     }
 
     public TensorArrayRead(){}
+
     @Override
-    public String tensorflowName() {
-        return "TensorArrayReadV3";
+    public String[] tensorflowNames() {
+        return new String[]{"TensorArrayRead", "TensorArrayReadV2", "TensorArrayReadV3"};
     }
 
 
diff --git a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/shape/tensorops/TensorArrayScatter.java b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/shape/tensorops/TensorArrayScatter.java
index 9e1d93e2f..1f9a986fd 100644
--- a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/shape/tensorops/TensorArrayScatter.java
+++ b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/shape/tensorops/TensorArrayScatter.java
@@ -36,8 +36,8 @@ public class TensorArrayScatter extends BaseTensorOp {
     public TensorArrayScatter(){}
 
     @Override
-    public String tensorflowName() {
-        return "TensorArrayScatterV3";
+    public String[] tensorflowNames() {
+        return new String[]{"TensorArrayScatter", "TensorArrayScatterV2", "TensorArrayScatterV3"};
     }
 
     @Override
diff --git a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/shape/tensorops/TensorArraySize.java b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/shape/tensorops/TensorArraySize.java
index 0503d377b..f7fd18193 100644
--- a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/shape/tensorops/TensorArraySize.java
+++ b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/shape/tensorops/TensorArraySize.java
@@ -31,8 +31,8 @@ import java.util.Map;
 
 public class TensorArraySize extends BaseTensorOp {
    @Override
-   public String tensorflowName() {
-      return "TensorArraySizeV3";
+   public String[] tensorflowNames() {
+      return new String[]{"TensorArraySize", "TensorArraySizeV2", "TensorArraySizeV3"};
    }
 
 
diff --git a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/shape/tensorops/TensorArraySplit.java b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/shape/tensorops/TensorArraySplit.java
index 589805641..b3630c549 100644
--- a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/shape/tensorops/TensorArraySplit.java
+++ b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/shape/tensorops/TensorArraySplit.java
@@ -36,8 +36,8 @@ public class TensorArraySplit extends BaseTensorOp {
     public TensorArraySplit(){}
 
     @Override
-    public String tensorflowName() {
-        return "TensorArraySplitV3";
+    public String[] tensorflowNames() {
+        return new String[]{"TensorArraySplit", "TensorArraySplitV2", "TensorArraySplitV3"};
     }
 
     @Override
diff --git a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/shape/tensorops/TensorArrayWrite.java b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/shape/tensorops/TensorArrayWrite.java
index c82c732d4..2f1211e1d 100644
--- a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/shape/tensorops/TensorArrayWrite.java
+++ b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/shape/tensorops/TensorArrayWrite.java
@@ -35,8 +35,8 @@ public class TensorArrayWrite extends BaseTensorOp {
 
    public TensorArrayWrite(){}
    @Override
-   public String tensorflowName() {
-      return "TensorArrayWriteV3";
+   public String[] tensorflowNames() {
+      return new String[]{"TensorArrayWrite", "TensorArrayWriteV2", "TensorArrayWriteV3"};
    }
 
    @Override
@@ -49,9 +49,9 @@ public class TensorArrayWrite extends BaseTensorOp {
       return Op.Type.CUSTOM;
    }
 
-    @Override
-    public List<DataType> calculateOutputDataTypes(List<DataType> inputDataType){
-        //Dummy float variable
-        return Collections.singletonList(DataType.FLOAT);
-    }
+   @Override
+   public List<DataType> calculateOutputDataTypes(List<DataType> inputDataType){
+      //Dummy float variable
+      return Collections.singletonList(DataType.FLOAT);
+   }
 }
diff --git a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/transforms/clip/ClipByNorm.java b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/transforms/clip/ClipByNorm.java
index 59b7ec2f5..4d6ba3e66 100644
--- a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/transforms/clip/ClipByNorm.java
+++ b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/transforms/clip/ClipByNorm.java
@@ -71,28 +71,6 @@ public class ClipByNorm extends DynamicCustomOp {
 
     @Override
     public List<SDVariable> doDiff(List<SDVariable> grad) {
-        //dOut/dIn is ??? if clipped, 1 otherwise
-        /*
-        int origRank = Shape.rankFromShape(arg().getShape());
-        SDVariable l2norm = f().norm2(arg(), true, dimensions);
-        SDVariable isClippedBC = f().gte(l2norm, clipValue);
-        SDVariable notClippedBC = isClippedBC.rsub(1.0);
-
-//        SDVariable dnormdx = arg().div(broadcastableNorm);
-//        SDVariable sqNorm = f().square(broadcastableNorm);
-//        SDVariable dOutdInClipped = sqNorm.rdiv(-1).mul(dnormdx).mul(arg()) //-1/(norm2(x))^2 * x/norm2(x)
-//                .add(broadcastableNorm.rdiv(1.0))
-//                .mul(clipValue);
-
-        SDVariable dOutdInClipped = f().neg(f().square(arg()).div(f().cube(l2norm))) //-x^2/(norm2(x))^3
-                .add(l2norm.rdiv(1.0))   //+ 1/norm(x)
-                .mul(clipValue).mul(isClippedBC);
-
-
-        SDVariable ret = notClippedBC.add(dOutdInClipped).mul(grad.get(0));
-        return Arrays.asList(ret);
-        */
-
         return Collections.singletonList(new ClipByNormBp(f().sameDiff(), arg(), grad.get(0), clipValue, dimensions).outputVariable());
     }
 
diff --git a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/transforms/custom/BitwiseAnd.java b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/transforms/custom/BitwiseAnd.java
index d81a72c1f..5483ce17d 100644
--- a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/transforms/custom/BitwiseAnd.java
+++ b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/transforms/custom/BitwiseAnd.java
@@ -50,7 +50,7 @@ public class BitwiseAnd extends BaseDynamicTransformOp {
 
     @Override
     public String opName() {
-        return "bitwise_and";
+        return "BitwiseAnd";
     }
 
 
diff --git a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/transforms/custom/BitwiseOr.java b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/transforms/custom/BitwiseOr.java
index 85dd5c31b..e1e436297 100644
--- a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/transforms/custom/BitwiseOr.java
+++ b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/transforms/custom/BitwiseOr.java
@@ -61,7 +61,7 @@ public class BitwiseOr extends BaseDynamicTransformOp {
 
     @Override
     public String tensorflowName() {
-        return "bitwise_or";
+        return "BitwiseOr";
     }
 
 
diff --git a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/transforms/custom/BitwiseXor.java b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/transforms/custom/BitwiseXor.java
index 136ca9b62..601cc5d84 100644
--- a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/transforms/custom/BitwiseXor.java
+++ b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/transforms/custom/BitwiseXor.java
@@ -61,7 +61,7 @@ public class BitwiseXor extends BaseDynamicTransformOp {
 
     @Override
     public String tensorflowName() {
-        return "bitwise_xor";
+        return "BitwiseXor";
     }
 
 
diff --git a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/factory/BaseNDArrayFactory.java b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/factory/BaseNDArrayFactory.java
index 1edf0d651..a664d9ee5 100644
--- a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/factory/BaseNDArrayFactory.java
+++ b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/factory/BaseNDArrayFactory.java
@@ -17,6 +17,7 @@
 package org.nd4j.linalg.factory;
 
 
+import lombok.NonNull;
 import lombok.val;
 import org.nd4j.base.Preconditions;
 import org.nd4j.linalg.api.blas.*;
@@ -959,8 +960,18 @@ public abstract class BaseNDArrayFactory implements NDArrayFactory {
      *
      * @param arrs
      */
-    public INDArray hstack(INDArray... arrs) {
-        return Nd4j.concat(1, arrs);
+    public INDArray hstack(@NonNull INDArray... arrs) {
+        int firstRank = arrs[0].rank();
+        Preconditions.checkState(firstRank > 0 && firstRank <= 2, "Only rank 1 and 2 arrays may be horizontally stacked; first input has rank %ndRank shape %nhShape", arrs[0], arrs[0]);
+        for( int i=1; i<arrs.length; i++ ){
+            Preconditions.checkState(firstRank == arrs[i].rank(), "Array ranks must be equal for horizontal stacking, arrs[0].rank=%s, arrs[%s].rank=%s",
+                    arrs[0].rank(), i, arrs[i].rank());
+        }
+        if(firstRank == 1){
+            return Nd4j.concat(0, arrs);
+        } else {
+            return Nd4j.concat(1, arrs);
+        }
     }
 
     /**
@@ -972,7 +983,6 @@ public abstract class BaseNDArrayFactory implements NDArrayFactory {
     @Override
     public INDArray vstack(final INDArray... arrs) {
         return Nd4j.concat(0, arrs);
-
     }
 
 
diff --git a/nd4j/nd4j-backends/nd4j-backend-impls/nd4j-cuda/src/main/java/org/nd4j/linalg/jcublas/JCublasNDArray.java b/nd4j/nd4j-backends/nd4j-backend-impls/nd4j-cuda/src/main/java/org/nd4j/linalg/jcublas/JCublasNDArray.java
index eb0db01a3..79d87a01e 100644
--- a/nd4j/nd4j-backends/nd4j-backend-impls/nd4j-cuda/src/main/java/org/nd4j/linalg/jcublas/JCublasNDArray.java
+++ b/nd4j/nd4j-backends/nd4j-backend-impls/nd4j-cuda/src/main/java/org/nd4j/linalg/jcublas/JCublasNDArray.java
@@ -544,13 +544,6 @@ public class JCublasNDArray extends BaseNDArray {
         return LongShapeDescriptor.fromShape(shape(), stride(), elementWiseStride(), ordering(), dataType(), isEmpty());
     }
 
-    /**
-     * This method does direct array copy. Impossible to use on views or mixed orders.
-     *
-     * PLEASE NOTE: YOU SHOULD NEVER USE THIS METHOD, UNLESS YOU 100% CLEAR ABOUT IT
-     *
-     * @return
-     */
     @Override
     public INDArray unsafeDuplication() {
         return unsafeDuplication(true);
@@ -717,14 +710,6 @@ public class JCublasNDArray extends BaseNDArray {
         return copy;
     }
 
-
-    /**
-     * This method pulls this INDArray into current Workspace.
-     *
-     * PLEASE NOTE: If there's no current Workspace - INDArray returned as is
-     *
-     * @return
-     */
     @Override
     public INDArray migrate() {
         WorkspaceUtils.assertValidArray(this, "Cannot leverage INDArray to new workspace");
diff --git a/nd4j/nd4j-backends/nd4j-backend-impls/nd4j-cuda/src/main/java/org/nd4j/linalg/jcublas/JcusparseNDArrayCSR.java b/nd4j/nd4j-backends/nd4j-backend-impls/nd4j-cuda/src/main/java/org/nd4j/linalg/jcublas/JcusparseNDArrayCSR.java
index 2698c299f..ae738f5c3 100644
--- a/nd4j/nd4j-backends/nd4j-backend-impls/nd4j-cuda/src/main/java/org/nd4j/linalg/jcublas/JcusparseNDArrayCSR.java
+++ b/nd4j/nd4j-backends/nd4j-backend-impls/nd4j-cuda/src/main/java/org/nd4j/linalg/jcublas/JcusparseNDArrayCSR.java
@@ -114,11 +114,6 @@ public class JcusparseNDArrayCSR extends BaseSparseNDArrayCSR {
         throw new UnsupportedOperationException();
     }
 
-    /**
-     * This method returns true if this INDArray is special case: no-value INDArray
-     *
-     * @return
-     */
     @Override
     public boolean isEmpty() {
         throw new UnsupportedOperationException();
diff --git a/nd4j/nd4j-backends/nd4j-backend-impls/nd4j-native/src/main/java/org/nd4j/linalg/cpu/nativecpu/NDArray.java b/nd4j/nd4j-backends/nd4j-backend-impls/nd4j-native/src/main/java/org/nd4j/linalg/cpu/nativecpu/NDArray.java
index dc9ab6417..a6cd47fb0 100644
--- a/nd4j/nd4j-backends/nd4j-backend-impls/nd4j-native/src/main/java/org/nd4j/linalg/cpu/nativecpu/NDArray.java
+++ b/nd4j/nd4j-backends/nd4j-backend-impls/nd4j-native/src/main/java/org/nd4j/linalg/cpu/nativecpu/NDArray.java
@@ -459,13 +459,6 @@ public class NDArray extends BaseNDArray {
         return new BaseNDArrayProxy(this);
     }
 
-    /**
-     * This method does direct array copy. Impossible to use on views or mixed orders.
-     *
-     * PLEASE NOTE: YOU SHOULD NEVER USE THIS METHOD, UNLESS YOU 100% CLEAR ABOUT IT
-     *
-     * @return
-     */
     @Override
     public INDArray unsafeDuplication() {
         WorkspaceUtils.assertValidArray(this, "Cannot duplicate array");
diff --git a/nd4j/nd4j-backends/nd4j-backend-impls/nd4j-native/src/main/java/org/nd4j/linalg/cpu/nativecpu/SparseNDArrayCSR.java b/nd4j/nd4j-backends/nd4j-backend-impls/nd4j-native/src/main/java/org/nd4j/linalg/cpu/nativecpu/SparseNDArrayCSR.java
index b35662b98..f73f9dece 100644
--- a/nd4j/nd4j-backends/nd4j-backend-impls/nd4j-native/src/main/java/org/nd4j/linalg/cpu/nativecpu/SparseNDArrayCSR.java
+++ b/nd4j/nd4j-backends/nd4j-backend-impls/nd4j-native/src/main/java/org/nd4j/linalg/cpu/nativecpu/SparseNDArrayCSR.java
@@ -124,11 +124,6 @@ public class SparseNDArrayCSR extends BaseSparseNDArrayCSR {
         throw new UnsupportedOperationException();
     }
 
-    /**
-     * This method returns true if this INDArray is special case: no-value INDArray
-     *
-     * @return
-     */
     @Override
     public boolean isEmpty() {
         return false;
diff --git a/nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/autodiff/opvalidation/ShapeOpValidation.java b/nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/autodiff/opvalidation/ShapeOpValidation.java
index bf4b331a3..2cbdfd3fd 100644
--- a/nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/autodiff/opvalidation/ShapeOpValidation.java
+++ b/nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/autodiff/opvalidation/ShapeOpValidation.java
@@ -2472,4 +2472,28 @@ public class ShapeOpValidation extends BaseOpValidation {
                 .build();
     }
 
+    @Test
+    public void testBroadcastInt1() {
+
+        INDArray out = Nd4j.create(DataType.INT, 1);
+        DynamicCustomOp op = DynamicCustomOp.builder("broadcast_dynamic_shape")
+                .addInputs(Nd4j.createFromArray(1), Nd4j.createFromArray(4))
+                .addOutputs(out)
+                .build();
+        Nd4j.getExecutioner().exec(op);
+        assertEquals(Nd4j.createFromArray(4), out);
+
+    }
+
+    @Test
+    public void testBroadcastInt2(){
+        INDArray out = Nd4j.create(DataType.INT, 2);
+        DynamicCustomOp op = DynamicCustomOp.builder("broadcast_dynamic_shape")
+                .addInputs(Nd4j.createFromArray(2, 2), Nd4j.createFromArray(1))
+                .addOutputs(out)
+                .build();
+        Nd4j.getExecutioner().exec(op);
+
+        assertEquals(Nd4j.createFromArray(2, 2), out);
+    }
 }
diff --git a/nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/autodiff/samediff/SameDiffTests.java b/nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/autodiff/samediff/SameDiffTests.java
index bb15b3392..8656cb46f 100644
--- a/nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/autodiff/samediff/SameDiffTests.java
+++ b/nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/autodiff/samediff/SameDiffTests.java
@@ -3563,4 +3563,26 @@ public class SameDiffTests extends BaseNd4jTest {
         assertEquals(casted.dataType(), DataType.FLOAT);
         assertTrue(casted.getShapeDescriptor().isEmpty());
     }
+
+
+    @Test
+    public void testEmptyShapeVar(){
+        SameDiff sd = SameDiff.create();
+
+        try {
+            sd.var(DataType.FLOAT, 1, 0, 2);
+            fail("Expected exception");
+        } catch (IllegalArgumentException e){
+            String m = e.getMessage();
+            assertTrue(m, m.contains("variable") && m.contains("empty") && m.contains("0"));
+        }
+
+        try {
+            sd.var(Nd4j.create(1, 0, 2));
+            fail("Expected exception");
+        } catch (IllegalArgumentException e){
+            String m = e.getMessage().toLowerCase();
+            assertTrue(m, m.contains("variable") && m.contains("empty") && m.contains("0"));
+        }
+    }
 }
diff --git a/nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/autodiff/samediff/SameDiffTrainingTest.java b/nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/autodiff/samediff/SameDiffTrainingTest.java
index 04d6e9551..9361c47f1 100644
--- a/nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/autodiff/samediff/SameDiffTrainingTest.java
+++ b/nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/autodiff/samediff/SameDiffTrainingTest.java
@@ -16,9 +16,16 @@
 
 package org.nd4j.autodiff.samediff;
 
+import static org.junit.Assert.assertTrue;
+
+import java.util.Collections;
+import java.util.HashMap;
+import java.util.List;
+import java.util.Map;
 import lombok.extern.slf4j.Slf4j;
 import org.junit.Test;
 import org.nd4j.autodiff.listeners.impl.ScoreListener;
+import org.nd4j.autodiff.listeners.records.History;
 import org.nd4j.evaluation.IEvaluation;
 import org.nd4j.evaluation.classification.Evaluation;
 import org.nd4j.linalg.BaseNd4jTest;
@@ -29,16 +36,14 @@ import org.nd4j.linalg.dataset.api.iterator.DataSetIterator;
 import org.nd4j.linalg.dataset.api.preprocessor.NormalizerStandardize;
 import org.nd4j.linalg.factory.Nd4j;
 import org.nd4j.linalg.factory.Nd4jBackend;
-import org.nd4j.linalg.learning.config.*;
+import org.nd4j.linalg.learning.config.AMSGrad;
+import org.nd4j.linalg.learning.config.AdaMax;
+import org.nd4j.linalg.learning.config.Adam;
+import org.nd4j.linalg.learning.config.IUpdater;
+import org.nd4j.linalg.learning.config.Nesterovs;
+import org.nd4j.linalg.learning.config.Sgd;
 import org.nd4j.weightinit.impl.XavierInitScheme;
 
-import java.util.Collections;
-import java.util.HashMap;
-import java.util.List;
-import java.util.Map;
-
-import static org.junit.Assert.assertTrue;
-
 @Slf4j
 public class SameDiffTrainingTest extends BaseNd4jTest {
 
@@ -118,6 +123,110 @@ public class SameDiffTrainingTest extends BaseNd4jTest {
     }
 
 
+    @Test
+    public void irisTrainingEvalTest() {
+
+        DataSetIterator iter = new IrisDataSetIterator(150, 150);
+        NormalizerStandardize std = new NormalizerStandardize();
+        std.fit(iter);
+        iter.setPreProcessor(std);
+
+        Nd4j.getRandom().setSeed(12345);
+        SameDiff sd = SameDiff.create();
+
+        SDVariable in = sd.placeHolder("input", DataType.FLOAT, -1, 4);
+        SDVariable label = sd.placeHolder("label", DataType.FLOAT, -1, 3);
+
+        SDVariable w0 = sd.var("w0", new XavierInitScheme('c', 4, 10), DataType.FLOAT, 4, 10);
+        SDVariable b0 = sd.zero("b0", DataType.FLOAT, 1, 10);
+
+        SDVariable w1 = sd.var("w1", new XavierInitScheme('c', 10, 3), DataType.FLOAT, 10, 3);
+        SDVariable b1 = sd.zero("b1", DataType.FLOAT, 1, 3);
+
+        SDVariable z0 = in.mmul(w0).add(b0);
+        SDVariable a0 = sd.math().tanh(z0);
+        SDVariable z1 = a0.mmul(w1).add("prediction", b1);
+        SDVariable a1 = sd.nn().softmax(z1);
+
+        SDVariable diff = sd.f().squaredDifference(a1, label);
+        SDVariable lossMse = diff.mul(diff).mean();
+
+        TrainingConfig conf = new TrainingConfig.Builder()
+                .l2(1e-4)
+                .updater(new Adam(1e-2))
+                .dataSetFeatureMapping("input")
+                .dataSetLabelMapping("label")
+                .trainEvaluation("prediction", 0, new Evaluation())
+                .build();
+
+        sd.setTrainingConfig(conf);
+
+        History hist = sd.fit().train(iter, 50).exec();
+
+        Evaluation e = hist.finalTrainingEvaluations().evaluation("prediction");
+
+        System.out.println(e.stats());
+
+        double acc = e.accuracy();
+
+        assertTrue("Accuracy bad: " + acc, acc >= 0.75);
+    }
+
+
+    @Test
+    public void irisTrainingValidationTest() {
+
+        DataSetIterator iter = new IrisDataSetIterator(150, 150);
+        NormalizerStandardize std = new NormalizerStandardize();
+        std.fit(iter);
+        iter.setPreProcessor(std);
+
+        DataSetIterator valIter = new IrisDataSetIterator(30, 60);
+        NormalizerStandardize valStd = new NormalizerStandardize();
+        valStd.fit(valIter);
+        valIter.setPreProcessor(std);
+
+        Nd4j.getRandom().setSeed(12345);
+        SameDiff sd = SameDiff.create();
+
+        SDVariable in = sd.placeHolder("input", DataType.FLOAT, -1, 4);
+        SDVariable label = sd.placeHolder("label", DataType.FLOAT, -1, 3);
+
+        SDVariable w0 = sd.var("w0", new XavierInitScheme('c', 4, 10), DataType.FLOAT, 4, 10);
+        SDVariable b0 = sd.zero("b0", DataType.FLOAT, 1, 10);
+
+        SDVariable w1 = sd.var("w1", new XavierInitScheme('c', 10, 3), DataType.FLOAT, 10, 3);
+        SDVariable b1 = sd.zero("b1", DataType.FLOAT, 1, 3);
+
+        SDVariable z0 = in.mmul(w0).add(b0);
+        SDVariable a0 = sd.math().tanh(z0);
+        SDVariable z1 = a0.mmul(w1).add("prediction", b1);
+        SDVariable a1 = sd.nn().softmax(z1);
+
+        SDVariable diff = sd.f().squaredDifference(a1, label);
+        SDVariable lossMse = diff.mul(diff).mean();
+
+        TrainingConfig conf = new TrainingConfig.Builder()
+                .l2(1e-4)
+                .updater(new Adam(1e-2))
+                .dataSetFeatureMapping("input")
+                .dataSetLabelMapping("label")
+                .validationEvaluation("prediction", 0, new Evaluation())
+                .build();
+
+        sd.setTrainingConfig(conf);
+
+        History hist = sd.fit().train(iter, 50).validate(valIter, 5).exec();
+
+        Evaluation e = hist.finalValidationEvaluations().evaluation("prediction");
+
+        System.out.println(e.stats());
+
+        double acc = e.accuracy();
+
+        assertTrue("Accuracy bad: " + acc, acc >= 0.75);
+    }
+
 
     @Test
     public void testTrainingMixedDtypes(){
diff --git a/nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/imports/TFGraphs/TFGraphTestAllSameDiff.java b/nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/imports/TFGraphs/TFGraphTestAllSameDiff.java
index 1fef4a07b..fb420ac4b 100644
--- a/nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/imports/TFGraphs/TFGraphTestAllSameDiff.java
+++ b/nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/imports/TFGraphs/TFGraphTestAllSameDiff.java
@@ -66,30 +66,23 @@ public class TFGraphTestAllSameDiff {   //Note: Can't extend BaseNd4jTest here a
     private static final String MODEL_FILENAME = "frozen_model.pb";
 
     public static final String[] IGNORE_REGEXES = new String[]{
-
-            //Still failing: 2019/07/01 - https://github.com/deeplearning4j/deeplearning4j/issues/6322 and https://github.com/eclipse/deeplearning4j/issues/7955
-            "broadcast_dynamic_shape/1_4",
-            "broadcast_dynamic_shape/2,2_1",
-
-            //Failing 2019/07/01 - Libnd4j Concat sizing issue - https://github.com/eclipse/deeplearning4j/issues/7963
-            "boolean_mask/.*",
-
             //Failing 2019/07/01 - Issue 10, https://github.com/deeplearning4j/deeplearning4j/issues/6958
+            //Still failing 2019/09/11
             "slogdet/.*",
 
-            //Failing 2019/07/01 - https://github.com/eclipse/deeplearning4j/issues/7965
+            //Failing 2019/09/11 - https://github.com/eclipse/deeplearning4j/issues/7965
             "bincount/.*",
 
             //TODO floormod and truncatemod behave differently - i.e., "c" vs. "python" semantics. Need to check implementations too
             "truncatemod/.*",
 
-            //Still failing as of 2019/07/02 - https://github.com/deeplearning4j/deeplearning4j/issues/6464 - not sure if related to: https://github.com/deeplearning4j/deeplearning4j/issues/6447
+            //Still failing as of 2019/09/11 - https://github.com/deeplearning4j/deeplearning4j/issues/6464 - not sure if related to: https://github.com/deeplearning4j/deeplearning4j/issues/6447
             "cnn2d_nn/nhwc_b1_k12_s12_d12_SAME",
 
-            //2019/07/02 - No tensorflow op found for SparseTensorDenseAdd
+            //2019/09/11 - No tensorflow op found for SparseTensorDenseAdd
             "confusion/.*",
 
-            //2019/07/02 - Couple of tests failing (InferenceSession issues)
+            //2019/09/11 - Couple of tests failing (InferenceSession issues)
             "rnn/bstack/d_.*",
 
             //2019/05/21 - Failing on AVX2/512 intermittently (Linux, OSX), passing elsewhere
@@ -103,13 +96,6 @@ public class TFGraphTestAllSameDiff {   //Note: Can't extend BaseNd4jTest here a
             //2019/05/28 - JVM crash on ppc64le only - See issue 7657
             "g_11",
 
-            //2019/06/21 - Not yet implemented: https://github.com/eclipse/deeplearning4j/issues/7913
-            "fake_quant/min_max_args_per_channel/.*",
-
-            //2019/06/22 - Known issue: https://github.com/eclipse/deeplearning4j/issues/7935
-            "fake_quant/min_max_vars/.*",
-            "fake_quant/min_max_args/.*",
-      
             //2019/07/09 - Need "Multinomial" op - https://github.com/eclipse/deeplearning4j/issues/7913
             "multinomial/.*"
     };
diff --git a/nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/linalg/Nd4jTestsC.java b/nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/linalg/Nd4jTestsC.java
index 8f0c4de35..31d51d59a 100644
--- a/nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/linalg/Nd4jTestsC.java
+++ b/nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/linalg/Nd4jTestsC.java
@@ -8122,6 +8122,19 @@ public class Nd4jTestsC extends BaseNd4jTest {
         return ret;
     }
 
+
+    @Test
+    public void testVStackHStack1d() {
+        INDArray rowVector1 = Nd4j.create(new double[]{1,2,3});
+        INDArray rowVector2 = Nd4j.create(new double[]{4,5,6});
+
+        INDArray vStack = Nd4j.vstack(rowVector1, rowVector2);      //Vertical stack:   [3]+[3] to [2,3]
+        INDArray hStack = Nd4j.hstack(rowVector1, rowVector2);      //Horizontal stack: [3]+[3] to [6]
+
+        assertEquals(Nd4j.createFromArray(1.0,2,3,4,5,6).reshape('c', 2, 3), vStack);
+        assertEquals(Nd4j.createFromArray(1.0,2,3,4,5,6), hStack);
+    }
+
     @Override
     public char ordering() {
         return 'c';
diff --git a/nd4j/nd4j-common/src/main/java/org/nd4j/linalg/util/ArrayUtil.java b/nd4j/nd4j-common/src/main/java/org/nd4j/linalg/util/ArrayUtil.java
index 2fe33dfba..cf54d4357 100644
--- a/nd4j/nd4j-common/src/main/java/org/nd4j/linalg/util/ArrayUtil.java
+++ b/nd4j/nd4j-common/src/main/java/org/nd4j/linalg/util/ArrayUtil.java
@@ -66,6 +66,26 @@ public class ArrayUtil {
         return false;
     }
 
+    public static boolean contains(int[] arr, int value){
+        if(arr == null)
+            return false;
+        for( int i : arr ) {
+            if (i == value)
+                return true;
+        }
+        return false;
+    }
+
+    public static boolean contains(long[] arr, int value){
+        if(arr == null)
+            return false;
+        for( long i : arr ) {
+            if (i == value)
+                return true;
+        }
+        return false;
+    }
+
     /**
      *
      * @param arrs
diff --git a/nd4s/README.md b/nd4s/README.md
index a5b675cd8..f862b2e69 100644
--- a/nd4s/README.md
+++ b/nd4s/README.md
@@ -2,7 +2,7 @@
 
 [![Join the chat at https://gitter.im/deeplearning4j/deeplearning4j](https://badges.gitter.im/Join%20Chat.svg)](https://gitter.im/deeplearning4j/deeplearning4j?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge)
 
-ND4S is open-source Scala bindings for [ND4J](https://github.com/deeplearning4j/nd4j). Released under an Apache 2.0 license. 
+ND4S is open-source Scala bindings for [ND4J](https://github.com/eclipse/deeplearning4j/tree/master/nd4j). Released under an Apache 2.0 license. 
 
 # Main Features
 * NDArray manipulation syntax sugar with safer type.
@@ -34,7 +34,7 @@ ND4S is actively developed. You can clone the repository, compile it, and refere
 Clone the repository:
 
 ```
-$ git clone https://github.com/deeplearning4j/nd4s.git
+$ git clone https://github.com/eclipse/deeplearning4j.git
 ```
 
 Compile the project:
diff --git a/pydatavec/README.md b/pydatavec/README.md
index be6de5853..15f85029f 100644
--- a/pydatavec/README.md
+++ b/pydatavec/README.md
@@ -8,7 +8,7 @@ pip install pydatavec
 
 ## Examples
 
-Examples are in the [dl4j-examples repo](www.github.com/deeplearning4j/dl4j-examples)
+Examples are in the [dl4j-examples repo](www.github.com/eclipse/deeplearning4j-examples)
 
 Clone dl4j-examples:
 
diff --git a/rl4j/.github/CONTRIBUTING.md b/rl4j/.github/CONTRIBUTING.md
deleted file mode 100644
index 9293c0dc5..000000000
--- a/rl4j/.github/CONTRIBUTING.md
+++ /dev/null
@@ -1,15 +0,0 @@
-## Contribute
-
-1. Check for open issues, or open a new issue to start a discussion around a feature idea or a bug.
-2. If you feel uncomfortable or uncertain about an issue or your changes, feel free to contact us on Gitter using the link above.
-3. Fork [the repository](https://github.com/deeplearning4j/rl4j.git) on GitHub to start making your changes to the **master** branch (or branch off of it).
-4. Write a test, which shows that the bug was fixed or that the feature works as expected.
-5. Note the repository follows
-   the [Google Java style](https://google.github.io/styleguide/javaguide.html)
-   with two modifications: 120-char column wrap and 4-spaces indentation. You
-   can format your code to this format by typing `mvn formatter:format` in the
-   subproject you work on, by using the `contrib/formatter.xml` at the root of
-   the repository to configure the Eclipse formatter, or by [using the INtellij
-   plugin](https://github.com/HPI-Information-Systems/Metanome/wiki/Installing-the-google-styleguide-settings-in-intellij-and-eclipse).
-
-6. Send a pull request, and bug us on Gitter until it gets merged and published.
diff --git a/rl4j/.github/ISSUE_TEMPLATE.md b/rl4j/.github/ISSUE_TEMPLATE.md
deleted file mode 100644
index 9dd585d22..000000000
--- a/rl4j/.github/ISSUE_TEMPLATE.md
+++ /dev/null
@@ -1,19 +0,0 @@
-#### Issue Description
-
-Please describe your issue, along with:
-- expected behavior
-- encountered behavior
-
-#### Version Information
-
-Please indicate relevant versions, including, if relevant:
-
-* Deeplearning4j version
-* platform information (OS, etc)
-* CUDA version, if used
-* NVIDIA driver version, if in use
-
-#### Contributing
-
-If you'd like to help us fix the issue by contributing some code, but would
-like guidance or help in doing so, please mention it!
diff --git a/rl4j/.github/PULL_REQUEST_TEMPLATE.md b/rl4j/.github/PULL_REQUEST_TEMPLATE.md
deleted file mode 100644
index 1f92bacd6..000000000
--- a/rl4j/.github/PULL_REQUEST_TEMPLATE.md
+++ /dev/null
@@ -1,10 +0,0 @@
-## What changes were proposed in this pull request?
-
-(Please fill in changes proposed in this fix)
-
-## How was this patch tested?
-
-(Please explain how this patch was tested. E.g. unit tests, integration tests, manual tests)
-
-Please review
-https://github.com/deeplearning4j/deeplearning4j/blob/master/CONTRIBUTING.md before opening a pull request.
diff --git a/rl4j/README.md b/rl4j/README.md
index cb9829e91..ef5797701 100644
--- a/rl4j/README.md
+++ b/rl4j/README.md
@@ -19,9 +19,9 @@ Here is a useful blog post I wrote to introduce you to reinforcement learning, D
 
 [Blog post](https://rubenfiszel.github.io/posts/rl4j/2016-08-24-Reinforcement-Learning-and-DQN.html)
 
-[Examples](https://github.com/deeplearning4j/dl4j-examples/tree/master/rl4j-examples)
+[Examples](https://github.com/eclipse/deeplearning4j-examples/tree/master/rl4j-examples)
 
-[Cartpole example](https://github.com/deeplearning4j/dl4j-examples/blob/master/rl4j-examples/src/main/java/org/deeplearning4j/examples/rl4j/Cartpole.java)
+[Cartpole example](https://github.com/eclipse/deeplearning4j-examples/blob/master/rl4j-examples/src/main/java/org/deeplearning4j/examples/rl4j/Cartpole.java)
 
 # Disclaimer
 
@@ -35,7 +35,7 @@ Comments are welcome on our gitter channel:
 ** INSTALL rl4j-api before installing all (see below)!**
 
 * mvn install -pl rl4j-api
-* [if you want rl4j-gym too] Download and mvn install: [gym-java-client](https://github.com/deeplearning4j/gym-java-client)
+* [if you want rl4j-gym too] Download and mvn install: [gym-java-client](https://github.com/eclipse/deeplearning4j/tree/master/gym-java-client)
 * mvn install
 
 # Visualisation
@@ -64,7 +64,7 @@ Doom is not ready yet but you can make it work if you feel adventurous with some
 * export MALMO_HOME=YOURMALMO_FOLDER
 * export MALMO_XSD_PATH=$MALMO_HOME/Schemas
 * launch malmo per [instructions](https://github.com/Microsoft/malmo#launching-minecraft-with-our-mod)
-* run with this [main](https://github.com/deeplearning4j/dl4j-examples/blob/master/rl4j-examples/src/main/java/org/deeplearning4j/examples/rl4j/MalmoPixels.java)
+* run with this [main](https://github.com/eclipse/deeplearning4j-examples/blob/master/rl4j-examples/src/main/java/org/deeplearning4j/examples/rl4j/MalmoPixels.java)
 
 
 
diff --git a/scalnet/.github/CONTRIBUTING.md b/scalnet/.github/CONTRIBUTING.md
deleted file mode 100644
index 45199d5ba..000000000
--- a/scalnet/.github/CONTRIBUTING.md
+++ /dev/null
@@ -1,7 +0,0 @@
-## Contribute
-
-1. Check for open issues, or open a new issue to start a discussion around a feature idea or a bug.
-2. If you feel uncomfortable or uncertain about an issue or your changes, feel free to contact us on Gitter using the link above.
-3. Fork [the repository](https://github.com/deeplearning4j/ScalNet.git) on GitHub to start making your changes to the **master** branch (or branch off of it).
-4. Write a test, which shows that the bug was fixed or that the feature works as expected.
-5. Send a pull request, and bug us on Gitter until it gets merged and published.
diff --git a/scalnet/.github/ISSUE_TEMPLATE.md b/scalnet/.github/ISSUE_TEMPLATE.md
deleted file mode 100644
index 9dd585d22..000000000
--- a/scalnet/.github/ISSUE_TEMPLATE.md
+++ /dev/null
@@ -1,19 +0,0 @@
-#### Issue Description
-
-Please describe your issue, along with:
-- expected behavior
-- encountered behavior
-
-#### Version Information
-
-Please indicate relevant versions, including, if relevant:
-
-* Deeplearning4j version
-* platform information (OS, etc)
-* CUDA version, if used
-* NVIDIA driver version, if in use
-
-#### Contributing
-
-If you'd like to help us fix the issue by contributing some code, but would
-like guidance or help in doing so, please mention it!
diff --git a/scalnet/.github/PULL_REQUEST_TEMPLATE.md b/scalnet/.github/PULL_REQUEST_TEMPLATE.md
deleted file mode 100644
index 1f92bacd6..000000000
--- a/scalnet/.github/PULL_REQUEST_TEMPLATE.md
+++ /dev/null
@@ -1,10 +0,0 @@
-## What changes were proposed in this pull request?
-
-(Please fill in changes proposed in this fix)
-
-## How was this patch tested?
-
-(Please explain how this patch was tested. E.g. unit tests, integration tests, manual tests)
-
-Please review
-https://github.com/deeplearning4j/deeplearning4j/blob/master/CONTRIBUTING.md before opening a pull request.
diff --git a/scalnet/README.md b/scalnet/README.md
index 34eae924b..9f5c1dc7f 100644
--- a/scalnet/README.md
+++ b/scalnet/README.md
@@ -18,10 +18,9 @@ Come in to [gitter](https://gitter.im/deeplearning4j/deeplearning4j) if you are
 
 # How to build
 
-Currently ScalNet depends on deeplearning4j and nd4j SNAPSHOTS. 
+ScalNet depends on Deeplearning4j and ND4J
 
-- [deeplearning4j/deeplearning4j](https://github.com/deeplearning4j/deeplearning4j)
-- [deeplearning4j/nd4j](https://github.com/deeplearning4j/nd4j)
+- [deeplearning4j and nd4j](https://github.com/eclipse/deeplearning4j)
 
 ### sbt