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contrib/codegen-tools/codegen/README.md

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+# ND4J Op Definitions and Code Generation
+This project contains the ND4J Op definitions, the DSL (Domain Specific Language) that is used for those definitions and
+code generators that use those definitions to create the actual Java code that is used to use the defined operations.
+
+
+## Why define ops externally?
+As we started to support SameDiff, we also started to introduce inconsistencies between SameDiff and ND4J. Even though 
+both of those libraries use the same underlying implementations for operations, there are both small and large
+differences in the API that we provide for them. Sometimes, we have provided an official API only for one usage, and not
+the other. And very often the documentation for a single op is in many different places.
+
+In the future we want to support other programming languages with libnd4j, and provide more ways to use our C++ backend.
+This would only increase the aforementioned problems.
+ 
+The root of all of those problems, is that Ops are used across different environments, and there is no single way of 
+defining them with an enforced interface.
+
+
+## How does this project help with enforcing a single consistent interface for ops?
+The solution we propose, is to define the operations separately, and then generate the necessary API code for them. All
+of the generated code is to be considered untouchable, editing it will result in the changes being overwritten sooner
+rather than later.
+
+The combination of external op definition and code generation, opens up many opportunities for us. The first one being
+that we can easily create consistent APIs for both ND4J and SameDiff in Java. But, looking into the future, we can also 
+create those APIs for other programming languages like Python, Swift, or even C#. We can even go beyond programming
+languages, and use the op definitions to create better documentation than what JavaDoc or similar might support out of
+the box.
+
+## Maintenance
+This project is currently maintained by Paul Dubs, with feedback often collected from raver119 and Alex Black.
+ 
+## Current Status
+At the moment we still focus on nailing down an easily readable and contribution friendly DSL for op definition and code
+generation that can replace namespace definitions. This means that at the moment we still rely on the pre-existing Op
+definition classes that already exist in ND4J.
+
+## Roadmap
+* Replace Bitwise and Random namespaces with autogenerated code – In progress.
+* Implement a convenient CLI tool.
+* Define all Ops using the DSL. 
+* Automatically generate derivative op declarations from existing ops
+* Replace all namespace definitions in ND4J / SameDiff with automatically generated ones
+* Replace all Op classes with automatically generated ones.
+
+# Usage
+Pre-requisites:
+* JDK 8 or higher
+* Maven 3.3 or higher
+
+TODO: Show usage output of the project itself
+
+TODO: Show how to use from mvn
+
+
+## Generating Code - ND4J Namespaces
+
+A script - `generate.sh` - is provided in the project root. This can be used (at present) to generate ND4J namespace classes.
+It is assumed that the deeplearning4j mono repo and the dl4j-dev-tools repo both exist and have a common parent directory
+i.e., `somedir/deeplearning4j` and `somedir/dl4j-dev-tools` both exist.
+
+The script takes as argument the name (or names) of the ND4J namespaces to generate (not case sensitive) and projects (supported
+projects are nd4j, sd and both by default).
+
+As of 26/11, namespaces names (and hence valid args) include: `bitwise`, `neuralnetwork`, `random`, and `math`
+Note also that `all` may be passed to the script to generate all namespaces.
+
+For example, to generate both bitwise and random namespaces for both nd4j and SameDiff:
+```
+./generate.sh bitwise,random
+```
+Or to generate all namespaces for both nd4j and SameDiff, use:
+```
+./generate.sh all
+``` 
+To generate namespaces for one project only, use:
+```
+./generate.sh linalg -projects sd
+```
+or:
+```
+./generate.sh linalg -projects nd4j
+```
+The script will first compile the project, before running.
+Internally, the `org.nd4j.codegen.cli.CLI` class is used.
+Classes are written to `deeplearning4j/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/factory/ops/`
+
+## Generating documentation.
+It is possible to use generate.sh for generation of code only, docs in markdown format only, or both docs and code.
+To generate docs only and store them to new folder "docs" for all namespaces:
+```
+./generate.sh all -docsdir ../../docs
+```
+Generation for selected namespaces works in the same way as for code:
+```
+./generate.sh -docsdir ../../docs bitwise,linalg
+```
+
+# Code structure
+The project is implemented using a mix of Java and Kotlin. The DSL definition and the accompanying data structures are
+implemented in Kotlin. At the moment the code generators are implemented in Java, in order to allow people who are not
+fluent in Kotlin, but know Java to be able to contribute to the code generators.
+
+The source code for this project is structured a bit different that what you would typically see in a Java or Kotlin
+project. When you take a look inside the `src/main` directory, you will find 4 sub-directories. 
+
+The `java` and `kotlin` directories contain Java and Kotlin code respectively. 
+
+In order to not confuse op definitions with the machinery that allows them to be defined in that way, ops are kept in a
+separate folder called `ops`.
+ 
+Because we use JavaPoet for Java code generator implementation, we also have yet another folder called `stubs`. That
+folder contains stub classes, that are used to reference other classes available in ND4J. These stub classes are
+intentionally left empty, as JavaPoet only requires them for naming and automatically creating proper imports. We use
+stub classes instead of depending on the actual nd4j API in order to break a cyclic dependency that would otherwise be
+created (i.e. in order to be able to generate code for ND4J, we would need an already compiled nd4j to be available).
+**Note:** If something is stubbed here and is moved in ND4J, then it also has to be moved to the appropriate place here,
+otherwise the generated code will be wrong.
+
+The `adr` folder contains "Architecture Decision Records". These files give you more insight into the "why" of some of
+the bigger decisions within this project.
+
+# DSL for Op Definition
+Ops are defined using a DSL that is implemented in Kotlin. This means that other than the DSL, as defined in the
+following, you can also use all of Kotlin when defining Ops. However, doing things the obvious and clearly
+understandable way is better than coming up with a clever way, so prefer to use the DSL as described if unsure.
+
+```kotlin
+val mathNs = Namespace("math") {
+    Op("add") {
+        javaPackage = "org.nd4j.linalg.api.ops.impl.transforms.pairwise.arithmetic"
+
+        Input(NUMERIC, "x") { description = "First input to add" }
+        Input(NUMERIC,"y") { count = AtLeast(1); description = "Second input to add" }
+        Arg(INT,"shape") { count = AtLeast(1); description = "shape" }
+
+
+        Output(NUMERIC, "z") { description = "Output (x+y)" }
+
+        Doc(Language.ANY, DocScope.ALL) {
+            """
+            (From AddOp) Add op doc text that will appear everywhere - classes, constructors, op creators
+            """.trimIndent()
+        }
+        Doc(Language.ANY, DocScope.CLASS_DOC_ONLY) {
+            "Add op doc text that will appear in all class docs (javadoc etc)"
+        }
+        Doc(Language.ANY, DocScope.CONSTRUCTORS_ONLY) {
+            "Add op doc text for constructors only"
+        }
+
+    }
+}
+```
+
+This example shows how a namespace is defined. Namespaces are at the top layer, and ops can only be defined within the
+context of a namespace. This example namespace contains only a single op, called "add". If we wanted to add another op,
+we would simply add it below the first.
+
+As you can see, every op has to have a name, if you try to create one without a name, you will get a compile error. 
+Within the context of the op, we first set in which java package the op class can be found in, then define its inputs, 
+arguments and outputs and finally add some free form documentation about what that op is doing.
+
+Like with the op itself, the inputs, arguments and outputs all have to have a name, but unlike the op, they also require
+a type. Within their context, you can set a description and a count of how many parameters they can take respectively.
+ 
+If an input, argument or output take anything else than exactly 1, they will be treated as arrays. Typically you would
+use this to define ops like `concat` which can take multiple input tensors or ops that might take shape arguments.
+
+## Examples
+The following shows how a typical op definition looks like and how the generated Java code may look.
+
+An op might be defined like this:
+
+```kotlin
+Op("binomial") {
+    javaPackage = "org.nd4j.linalg.api.ops.random.custom"
+    Arg(INT, "nTrials") { description = "Number of trials parameter for the binomial distribution" }
+    Arg(FLOATING_POINT, "p") { description = "Probability of success for each trial" }
+    Arg(INT, "shape") { count = AtLeast(1); description = "Shape of the new random SDVariable, as a 1D array" }
+
+    Output(NUMERIC, "output") { description = "new random SDVariable, where values are randomly sampled according to a Binomial distribution" }
+
+    Doc(Language.ANY, DocScope.ALL) {
+        """
+        Generate a new random SDVariable, where values are randomly sampled according to a Binomial distribution,
+        with the specified number of trials and probability.
+        """.trimIndent()
+    }
+}
+```
+
+The java code generator will create a method like the following for it:
+```java
+  /**
+   * Generate a new random SDVariable, where values are randomly sampled according to a Binomial distribution,
+   * with the specified number of trials and probability.
+   *
+   * @param nTrials Number of trials parameter for the binomial distribution
+   * @param p Probability of success for each trial
+   * @param shape Shape of the new random SDVariable, as a 1D array (Size: AtLeast(min=1))
+   * @return output new random SDVariable, where values are randomly sampled according to a Binomial distribution (NUMERIC type)
+   */
+  public static INDArray binomial(long nTrials, double p, long... shape) {
+    Preconditions.checkArgument(shape.length >= 1, "shape has incorrect count. Expected: AtLeast(min=1)");
+    return Nd4j.exec(new org.nd4j.linalg.api.ops.random.custom.BinomialOp(nTrials, p, shape))[0];
+  }
+```
+
+Or an op with some more constraints:
+
+```kotlin
+Op("and") {
+    javaPackage = "org.nd4j.linalg.api.ops.impl.transforms.custom"
+    val x = Input(INT, "x") { description = "First input array" }
+    val y = Input(INT, "y") { description = "Second input array" }
+    Constraint("Must be same types"){ sameType(x, y) }
+    Constraint("Must have broadcastable shapes"){ broadcastableShapes(x, y) }
+
+    Output(INT, "output"){ description = "Bitwise AND array" }
+
+    Doc(Language.ANY, DocScope.ALL){
+        """
+        Bitwise AND operation. Supports broadcasting.
+        """.trimIndent()
+    }
+}
+```
+
+will be converted to java like this:
+
+```java
+  /**
+   * Bitwise AND operation. Supports broadcasting.
+   *
+   * Inputs must satisfy the following constraints: 
+   * Must be same types: isSameType(x, y)
+   * Must have broadcastable shapes: isBroadcastableShapes(x, y)
+   *
+   * @param x First input array (INT type)
+   * @param y Second input array (INT type)
+   * @return output Bitwise AND array (INT type)
+   */
+  public static INDArray and(INDArray x, INDArray y) {
+    NDValidation.validateInteger("and", x);
+    NDValidation.validateInteger("and", y);
+    Preconditions.checkArgument(isSameType(x, y), "Must be same types");
+    Preconditions.checkArgument(isBroadcastableShapes(x, y), "Must have broadcastable shapes");
+    return Nd4j.exec(new org.nd4j.linalg.api.ops.impl.transforms.custom.AndOp(x, y))[0];
+  }
+```
+
+# Full DSL Description
+## Namespace
+
+    fun NamespaceName() = Namespace("name"){ /* Op definitions in namespace context */}
+    
+Defines a namespace.
+
+## Op
+Only available within a namespace context
+
+    Op("opName") { /* op properties in op context */ }
+    Op("anotherOp", mixin) { /* op properties in op context */ }
+    Op("anotherOp2", mixin, keepInputs=false) { /* op properties in op context */ }
+    
+Every op requires a namespace unique op name.
+ 
+When defining an op, you can also pass a mixin that it should inherit initial properties from. This has the same effect
+as using `useMixin(mixin)` as the very first thing in the op definition. If you don't want to inherit all of the
+parameters of the mixin, you can pass the same additional configuration as you would pass to
+`useMixin(mixin, ...options..)`. See [Mixin](#mixin) for more information.
+ 
+### Op properties
+* `javaPackage` (String): Package where the op is to be found in the java implementation.
+* `javaOpClass` (String): Name of java op class if inconsistent with opName. Default: same as opName
+* `libnd4jName` (String): The name the op has in libnd4j. Default: same as opName
+
+
+## Mixin
+Available in global context.
+
+    val mixin = Mixin("name"){ /* op properties in op context */ }
+    // within an op context:
+    useMixin(mixin)
+    useMixin(mixin, ...options...)
+    
+    // When needing to access something from within the mixin
+    mixin.input("name")
+    mixin.arg("name")
+    mixin.config("name")
+    mixin.output("name")
+    
+Mixins provide the facility to share commonalities between Ops. You can think of it like inheritance, especially when
+you declare the use of a mixin on Op definition. In contrast to normal (single) inheritance where only a single super
+class is possible, the mixin mechanism allows to "inherit" from multiple sources. 
+ 
+You can define almost all the same things within a mixin that you can within an Op. The only things that *can not* be
+configured within a mixin are Op `name`, `libnd4jName` and `javaOpClass`.
+
+As mixins can be configured within the global context, you can share them across namespaces by defining them in their
+own file. If a mixin is namespace specific, you can also define it within the namespace context. 
+
+Mixins are used either on definition as a parameter `Op("opname", mixin){...}`, or with `useMixin(mixin)` within the op
+definition. While the former version only supports a single mixin, the latter version allows you to use as many mixins
+as are required. 
+
+You can also build up mixins by using `useMixin(mixin)` inside a Mixin itself.
+
+`useMixin(mixin, ...options...)` supports a few additional options: `keepInputs`, `keepArgs`, `keepConfigs`, 
+`keepOutputs`, `keepSignatures`,  `keepDoc`, `keepConstraints`. They default to `true`. If you want to skip including
+some of them, you simply set the parameter for it to `false`, e.g. `useMixin(mixin, keepDoc=false)`. 
+
+When using `useMixin(mixin)`, all definitions within the mixin are applied as if this invocation was replaced with the
+content of the mixin itself. This means, that if you have already defined anything prior to using a mixin, the mixin's
+definitions will be **after** the previously defined things. This can be very useful if the commonality  between ops is 
+that they have a few trailing options.
+                                                              
+If a named property or section is defined in both a mixin (or multiple mixins) and the op, then the **last** to define it will
+win. Named properties are `legacy`, `javaPackage`, named sections are `Input`, `Arg`, `Output`, `Config`.
+
+For example, assume you have `javaPackage` defined in both an op and a mixin. Then you can have the following two
+cases:
+
+First case:
+```kotlin
+    Op("foo"){
+        useMixin(exampleMixin)
+        javaPackage = "some.example.package"
+    }
+``` 
+
+Second case:
+```kotlin
+    Op("foo"){
+        javaPackage = "some.example.package"
+        useMixin(exampleMixin)
+    }
+``` 
+
+In the first case, the op will have the `javaPackage` value that is defined within the op. In the second case it will 
+have the `javaPackage` value defined in the mixin.
+
+For inputs, args, outputs, it works similarly. Assume you have `Input(dataType, "a")` defined in both the mixin and the
+op. Again you can have two cases:
+
+First case:
+```kotlin
+    Op("foo"){
+        useMixin(exampleMixin)
+        Input(NUMERIC, "a")
+    }
+``` 
+
+Second case:
+```kotlin
+    Op("foo"){
+        Input(NUMERIC, "a")
+        useMixin(exampleMixin)
+    }
+``` 
+
+In the first case, it will overwrite the input from the mixin. In the second case, the mixin will overwrite that the
+input from the op.
+
+## Config
+Only available within a namespace context
+
+    val nameConfig = Config("Name"){
+        /* input, arg, constraint, doc properties */
+    }
+
+Every config requires a namespace unique name.
+
+A config allows to define a configuration class, that can be used as a holder for complex properties of specific ops 
+which will be passed to an op as a parameter.
+
+Similar to an op itself, it supports `Input`, `Arg`, `Constraint` and `Doc` definitions. 
+
+in order to use the config within an op you either use `useConfig(cfg)` or `val configRef = useConfig(cfg)`. The second
+form allows you to reference the config.
+
+Referencing the config allows to you reference its inputs and args by name: `configRef.input("name")` and 
+`configRef.arg("name")`. Also it allows you to use a config in a signature `Signature(a, b, c, configRef)`.
+
+When default and shorthand signatures are used, configs will be always placed at the end.
+
+If a config is defined but not used, an `IllegalStateException` will be thrown.
+
+See also [ADR 0007 "Configuration Objects"](adr/0007-configuration_objects.md).
+
+
+## Input
+Available within an op, mixin and a config context
+
+    Input(FLOATING_POINT, "b"){ /* input properties in input context */ }
+    val a = Input(INT, "a"){ /* input properties in input context */ }
+    
+Inputs represent tensors. They are what the op will work on.
+
+Every input requires a data type (either `INT`, `FLOATING_POINT`, `NUMERIC` or `BOOLEAN`) and an op unique name.
+
+When defining an input, you can assign it to a variable in order to be able to reference it later on. You might want to
+do this when defining constraints.
+
+If you want an input to represent an array, you will have to set a count accordingly. If no count is set, it is assumed
+that the count is meant to be `Exactly(1)`.
+ 
+### Input properties
+* `description` (String): A short description what this input represents. Setting this is recommended.
+* `count` (Count): Can take one of `Exactly(n)`; `AtLeast(n)`; `AtMost(n)`; `Range(from, to)`
+* `defaultValue` (Input): use another input as the default if this isn't set explicitly. The data type of the other 
+  input has to match the data type of this input. The other input may also have a default value.
+
+## Argument
+Available within an op, mixin and config context
+
+    Arg(FLOATING_POINT, "b"){ /* Arg properties in arg context */ }
+    val a = Arg(INT, "a"){ /* Arg properties in arg context */ }
+    
+Args represent arguments. They modify how the op works on its inputs.
+
+Every arg requires a data type (either `INT`, `FLOATING_POINT`, `NUMERIC` or `BOOLEAN`) and an op unique name.
+
+When defining an arg, you can assign it to a variable in order to be able to reference it later on. You might want to do
+this when defining constraints.
+
+If you want an arg to represent an array, you will have to set a count accordingly. If no count is set, it is assumed
+that the count is meant to be `Exactly(1)`.
+
+Note (Java specific): If the last arg is defined to represent an array, it will be translated to a vararg parameter, e.g.
+`Arg(INT, "a"){ count = AtLeast(1); description = "..." }` will be turned into `long... a`.
+ 
+### Argument properties
+* `description` (String): A short description what this argument represents. Setting this is recommended.
+* `count` (Count): Can take one of `Exactly(n)`; `AtLeast(n)`; `AtMost(n)`; `Range(from, to)`
+* `defaultValue` (null|Number|Boolean|int[]|double[]|boolean[]|Arg|TensorShapeValue|TensorDataTypeValue|String): 
+  Use given value as default value, if this isn't explicitly set. Can refer to *inputs* and *outputs* using `x.shape()`
+  and `x.dataType()`. The given default values has to match the data type for this argument. May also refer to another
+  Arg, and that Arg may also have a default value. Default values based on outputs are treated like without a default 
+  in SameDiff mode.
+* `possibleValues` (String[]): only available when ENUM data type is used for the argument. Takes a list of possible
+  values for the Enum. If used in in abstract base op, the enum will only be created once. See also 
+  [ADR 0006 "Op specific enums"](adr/0006-op_specific_enums.md).
+ 
+
+## Output
+Only available within an op and mixin context
+
+    Output(FLOATING_POINT, "b"){ /* Arg properties in arg context */ }
+
+Every output requires a data type (either `INT`, `FLOATING_POINT`, `NUMERIC` or `BOOLEAN`) and an op unique name.
+
+While outputs can be assigned to a variable, there is no intended use-case for it. In contrast to inputs and args, 
+outputs can not be used in constraints.
+
+### Output properties
+* `description` (String): A short description what this argument represents. Setting this is recommended.
+
+
+## Signature
+Only available within an op and mixin context
+
+    Signature(a,b,c)
+    Signature(a,b,c) { "Some Documentation" }
+    AllParamSignature()
+    AllDefaultParamSignature()
+
+For some ops only specific signatures make sense, as for example some optional parameters may become required in the
+presence of other optional parameters. This feature is mainly meant to help with the fact that not all programming
+languages (e.g. Java) support default parameters. Each signature is meant to describe one overload in those languages.   
+
+See also [ADR 0005 "Optional parameters and signatures"](adr/0005-optional_parameters_and_signatures.md).
+
+Signatures can also reference the output(s) of an op. Those signatures are only relevant in NDArray programming mode.
+They are not to be generated in SameDiff mode.
+
+`AllParamSignature()` and `AllDefaultParamSignature()` are short hands for `Signature(...all parameters...)` and 
+`Signature(...only parameters with no default values...)`. Their parameters include references to outputs unless
+disabled using `withOutput=false` (e.g. `AllParamSignature(withOutput=false)`).
+
+If no signature is specified for an op, it is treated as if `AllParamSignature()` and `AllDefaultParamSignature()` are
+both specified. 
+
+Each signature must satisfy the condition, that all required parameters are listed there. If this condition is not
+satisfied, an `IllegalStateException` will be thrown on construction. 
+  
+
+## Documentation
+Only available within an op and mixin context
+
+    Doc(Language.ANY, DocScope.ALL){
+        """ Some documentation
+        It can be multiline. And indented.
+        """.trimIndent()
+    }
+    
+Documentation can be language specific, and can be set to be only given at specific places. The documentation itself is
+given as a string. Because Kotlin supports multiline strings along with proper indentation, we are using them directly
+here.
+
+Note: At the moment we are only creating java code, so the documentation can use JavaDoc syntax.
+
+You can have multiple Doc definitions; they are treated as additive.
+
+Any instances of the following values will be replaced when generating code:
+
+* `%OPNAME%` -> operation name ("Add", "Sub", etc)
+* `%LIBND4J_OPNAME%` -> libnd4j op name ("add", "sub", etc)
+* `%INPUT_TYPE%` -> input / output type depending on the generated api, i.e. `SDVariable` for SameDiff and `INDArray`
+  for ND4J
+
+See `DocTokens` class for more details. 
+
+## Constraints
+Available within an op, mixin and a config context.
+
+    Constraint("Error Message if constraint isn't satisfied"){ /* constraint definition */ }
+    BackendConstraint("Error Message if constraint isn't satisfied"){ /* constraint definition */ }
+
+Many ops expect their inputs and arguments to satisfy some specific rules. Those rules can be expressed with the
+constraint system.
+
+Constraints are to be enforced within the frontend language, while BackendConstraints are currently only to be used as
+a part of the documentation. They will be enforced within the C++ backend, so there is no point in double checking them.
+
+There is a system in place to define even complex constraints for inputs and arguments.
+
+In a constraint definition, you can reference inputs and arguments directly, if they are previously assigned to
+a variable using `val name = Input(...)`. Inside the Constraint block, you can use the following operations:
+
+* `eq`: Compare equality (applicable to numbers and booleans), e.g. `x eq 7`, `x eq true`
+* `neq`: Compare inequality (applicable to numbers and booleans), e.g. `x neq 3`, `x neq true`
+* `lt`, `lte`: less than, less than equal (applicable to numbers), e.g. `x lt 3`, `x lte 4`
+* `gt`, `gte`: greater than, grater than equal (applicable to numbers), e.g. `x gt 5`, `x gte 6`
+* `and`: combine two comparisons where both have to be true, e.g. `(x eq 8) and (y lt 3)`
+* `or`: combine two comparisons where one has to be true, e.g. `(x eq 8) or (y eq true)`
+* `all`: combine N comparisons where all have to be true, e.g. `all(x eq 8, y lt 3, z eq true)`
+* `some`: combine N comparisons where at least one has to be true, e.g. `some(x eq 8, y lt 3, z eq true)`
+* `not`: negates a comparison, e.g. `not(x eq 3)`
+
+In addition to those operations, you also get access to some more complex constraints:
+* `sameType(...)`: true if all given inputs are the same type, e.g. `sameType(x,y,z)`
+* `sameShape(...)`: true if all given inputs have the same shape, e.g. `sameShape(x,y,z)`
+* `broadcastableShapes(...)`: true if all given inputs have broadcast compatible shapes, e.g. `broadcastableShapes(x,y,z)`
+
+Inputs also get some additional methods on them to define useful constraints:
+* `input.rank()`: Rank of the given input
+* `input.sizeAt(i)`: size of the given input at the i-th dimension
+* `input.isScalar()`: Short hand for `x.rank() == 1`
+
+### Examples
+Some examples of constraints, and what they evaluate to. The example code contains a little bit of context.
+
+```kotlin
+val x = Input(INT, "x") { description = "First input array" }
+val y = Input(INT, "y") { description = "Second input array" }
+Constraint("foo bar"){
+   x.sizeAt(7) eq 7 and y.isScalar()
+}
+```
+
+will evaluate to:
+```java
+    Preconditions.checkArgument((x.sizeAt(7) == 7) && (y.rank() == 1), "foo bar");
+```
+
+More examples (only the constraint itself, without context code):
+
+#### Some
+```kotlin
+some(input.rank() eq 3, input.sizeAt(2) gte 7, input.sizeAt(4) lt 5)
+```
+turns to:
+```java
+((x.rank() == 3) || (x.sizeAt(2) >= 7)) || (x.sizeAt(4) < 5)
+```
+
+# Contributing to this project
+If you want to contribute to this project other than by adding or improving op definitions, the following sections might
+be of special interest to you.
+
+## Extending the DSL
+The DSL is implemented using Kotlin’s type-safe builders feature 
+(see https://kotlinlang.org/docs/reference/type-safe-builders.html). The basic principle is that functions calls can
+receive blocks that can be executed in a specified context. When combined with the fact that we are just looking to
+create an object graph that is then going to be used as input to the code generators, this allows us to create a very 
+feature rich DSL without actually having to write a lot of code to support it.
+
+Most of the DSL specific code can be found in `src/kotlin/org/nd4j/codegen/dsl/OpBuilder.kt`. The actual class
+definitions for the object graph we are building, can be found in `src/kotlin/org/nd4j/codegen/api`. 
+
+If you want to add just a simple field to one of the objects, it is usually enough to directly add it to the particular
+class.
+
+If you want to add a specific section to the op definition, i.e. a section like Input or Doc, you will have to add both
+the class for the object that it is going to be creating, as well as a function within OpBuilder.kt to create and
+register that section within the op.
+ 
+**Note:** When you extend the DSL you will most likely also have to update all code generators to support the feature 
+you have added.
+ 
+## Adding / extending code generators
+Code generators can be written in either Java or Kotlin. Java has the advantage that more people will have experience in
+using it. Kotlin has the advantage of more convenient syntax, especially for plain string manipulation and when dealing
+with Enums and fixed sets of subclasses (called sealed classes in Kotlin).
+
+All generators have to implement the `org.nd4j.codegen.api.generator.Generator` interface. For automatic detection by
+the CLI tool, they should also be within the `org.nd4j.codegen.impl.LANGUAGE` package, where `LANGUAGE` is the actual
+language that they generate.
+
+Code generators can also use an auxiliary generator for constraint generation. Those auxiliary generators, have to
+implement ` org.nd4j.codegen.api.generator.ConstraintCodeGenerator` interface.
+

contrib/codegen-tools/codegen/generate.sh

@@ -0,0 +1,11 @@
+#!/bin/bash
+
+if test "$#" -eq 0; then
+    echo "No namespaces were specified. One or more namespaces must be provided as an argument"
+    echo "Usage example 1 (single namespace):      ./generate.sh math"
+    echo "Usage example 2 (multiple namespaces):   ./generate.sh math,random"
+    echo "Usage example 2 (all namespaces):        ./generate.sh all"
+else
+    mvn clean package -DskipTests
+    java -cp target/codegen-1.0.0-SNAPSHOT-shaded.jar org.nd4j.codegen.cli.CLI -dir ../../ -namespaces "$@"
+fi

contrib/codegen-tools/codegen/pom.xml

@@ -0,0 +1,258 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project xmlns="http://maven.apache.org/POM/4.0.0"
+         xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
+         xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
+    <modelVersion>4.0.0</modelVersion>
+
+    <groupId>org.nd4j</groupId>
+    <artifactId>codegen</artifactId>
+    <version>1.0.0-SNAPSHOT</version>
+
+    <properties>
+        <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
+        <commonsio.version>2.5</commonsio.version>
+        <commons.lang.version>3.12.0</commons.lang.version>
+        <commons.dbutils.version>1.7</commons.dbutils.version>
+        <lombok.version>1.18.8</lombok.version>
+        <logback.version>1.1.7</logback.version>
+        <junit.version>4.12</junit.version>
+        <junit-jupiter.version>5.4.2</junit-jupiter.version>
+        <java.version>1.8</java.version>
+        <maven-shade-plugin.version>3.1.1</maven-shade-plugin.version>
+        <kotlin.version>1.3.50</kotlin.version>
+        <kotlin.compiler.jvmTarget>1.8</kotlin.compiler.jvmTarget>
+        <kotlin.compiler.incremental>true</kotlin.compiler.incremental>
+    </properties>
+
+    <dependencies>
+        <dependency>
+            <groupId>org.slf4j</groupId>
+            <artifactId>slf4j-api</artifactId>
+            <version>1.7.28</version>
+        </dependency>
+
+        <dependency>
+            <groupId>ch.qos.logback</groupId>
+            <artifactId>logback-classic</artifactId>
+            <version>${logback.version}</version>
+        </dependency>
+
+        <dependency>
+            <groupId>commons-io</groupId>
+            <artifactId>commons-io</artifactId>
+            <version>${commonsio.version}</version>
+        </dependency>
+
+        <dependency>
+            <groupId>org.projectlombok</groupId>
+            <artifactId>lombok</artifactId>
+            <version>${lombok.version}</version>
+        </dependency>
+
+        <dependency>
+            <groupId>org.apache.commons</groupId>
+            <artifactId>commons-lang3</artifactId>
+            <version>${commons.lang.version}</version>
+        </dependency>
+
+        <dependency>
+            <groupId>com.squareup</groupId>
+            <artifactId>javapoet</artifactId>
+            <version>1.11.1</version>
+        </dependency>
+
+
+        <!-- Test Dependencies -->
+        <dependency>
+            <groupId>org.junit.jupiter</groupId>
+            <artifactId>junit-jupiter-api</artifactId>
+            <version>${junit-jupiter.version}</version>
+            <scope>test</scope>
+        </dependency>
+        <dependency>
+            <groupId>org.junit.jupiter</groupId>
+            <artifactId>junit-jupiter-engine</artifactId>
+            <version>${junit-jupiter.version}</version>
+            <scope>test</scope>
+        </dependency>
+
+        <dependency>
+            <groupId>org.jetbrains.kotlin</groupId>
+            <artifactId>kotlin-stdlib-jdk8</artifactId>
+            <version>${kotlin.version}</version>
+        </dependency>
+        <dependency>
+            <groupId>org.jetbrains.kotlin</groupId>
+            <artifactId>kotlin-test</artifactId>
+            <version>${kotlin.version}</version>
+            <scope>test</scope>
+        </dependency>
+
+        <dependency>
+            <groupId>com.fasterxml.jackson.module</groupId>
+            <artifactId>jackson-module-kotlin</artifactId>
+            <version>2.9.9</version>
+        </dependency>
+
+        <dependency>
+            <groupId>com.beust</groupId>
+            <artifactId>jcommander</artifactId>
+            <version>1.78</version>
+        </dependency>
+        <dependency>
+            <groupId>org.nd4j</groupId>
+            <artifactId>nd4j-api</artifactId>
+            <version>1.0.0-SNAPSHOT</version>
+        </dependency>
+    </dependencies>
+
+
+    <build>
+        <plugins>
+            <plugin>
+                <groupId>org.codehaus.mojo</groupId>
+                <artifactId>build-helper-maven-plugin</artifactId>
+                <version>3.0.0</version>
+                <executions>
+                    <execution>
+                        <id>add-source</id>
+                        <phase>generate-sources</phase>
+                        <goals><goal>add-source</goal></goals>
+                        <configuration>
+                            <sources>
+                                <source>src/main/stubs</source>
+                            </sources>
+                        </configuration>
+                    </execution>
+                </executions>
+            </plugin>
+
+            <plugin>
+                <groupId>org.apache.maven.plugins</groupId>
+                <artifactId>maven-shade-plugin</artifactId>
+                <version>${maven-shade-plugin.version}</version>
+                <configuration>
+                    <shadedArtifactAttached>true</shadedArtifactAttached>
+                    <createDependencyReducedPom>false</createDependencyReducedPom>
+                    <filters>
+                        <filter>
+                            <artifact>*:*</artifact>
+                            <excludes>
+                                <exclude>org/datanucleus/**</exclude>
+                                <exclude>META-INF/*.SF</exclude>
+                                <exclude>META-INF/*.DSA</exclude>
+                                <exclude>META-INF/*.RSA</exclude>
+                            </excludes>
+                        </filter>
+                    </filters>
+
+                </configuration>
+
+                <executions>
+                    <execution>
+                        <phase>package</phase>
+                        <goals>
+                            <goal>shade</goal>
+                        </goals>
+                        <configuration>
+                            <transformers>
+                                <transformer implementation="org.apache.maven.plugins.shade.resource.AppendingTransformer">
+                                    <resource>reference.conf</resource>
+                                </transformer>
+                                <transformer implementation="org.apache.maven.plugins.shade.resource.ServicesResourceTransformer"/>
+                                <transformer implementation="org.apache.maven.plugins.shade.resource.ManifestResourceTransformer" />
+                            </transformers>
+                        </configuration>
+                    </execution>
+                </executions>
+            </plugin>
+
+            <plugin>
+                <groupId>org.jetbrains.kotlin</groupId>
+                <artifactId>kotlin-maven-plugin</artifactId>
+                <version>${kotlin.version}</version>
+                <configuration>
+                    <args>
+                        <arg>-Xjsr305=strict</arg>
+                    </args>
+                    <compilerPlugins>
+                        <plugin>spring</plugin>
+                        <plugin>jpa</plugin>
+                    </compilerPlugins>
+                </configuration>
+                <dependencies>
+                    <dependency>
+                        <groupId>org.jetbrains.kotlin</groupId>
+                        <artifactId>kotlin-maven-allopen</artifactId>
+                        <version>${kotlin.version}</version>
+                    </dependency>
+                    <dependency>
+                        <groupId>org.jetbrains.kotlin</groupId>
+                        <artifactId>kotlin-maven-noarg</artifactId>
+                        <version>${kotlin.version}</version>
+                    </dependency>
+                </dependencies>
+                <executions>
+                    <execution>
+                        <id>compile</id>
+                        <goals> <goal>compile</goal> </goals>
+                        <configuration>
+                            <sourceDirs>
+                                <sourceDir>${project.basedir}/src/main/stubs</sourceDir>
+                                <sourceDir>${project.basedir}/src/main/kotlin</sourceDir>
+                                <sourceDir>${project.basedir}/src/main/java</sourceDir>
+                                <sourceDir>${project.basedir}/src/main/ops</sourceDir>
+                            </sourceDirs>
+                        </configuration>
+                    </execution>
+                    <execution>
+                        <id>test-compile</id>
+                        <goals> <goal>test-compile</goal> </goals>
+                        <configuration>
+                            <sourceDirs>
+                                <sourceDir>${project.basedir}/src/test/stubs</sourceDir>
+                                <sourceDir>${project.basedir}/src/test/kotlin</sourceDir>
+                                <sourceDir>${project.basedir}/src/test/java</sourceDir>
+                                <sourceDir>${project.basedir}/src/test/ops</sourceDir>
+                            </sourceDirs>
+                        </configuration>
+                    </execution>
+                </executions>
+            </plugin>
+
+            <!-- https://kotlinlang.org/docs/reference/using-maven.html -->
+            <plugin>
+                <groupId>org.apache.maven.plugins</groupId>
+                <artifactId>maven-compiler-plugin</artifactId>
+                <version>3.5.1</version>
+                <executions>
+                    <!-- Replacing default-compile as it is treated specially by maven -->
+                    <execution>
+                        <id>default-compile</id>
+                        <phase>none</phase>
+                    </execution>
+                    <!-- Replacing default-testCompile as it is treated specially by maven -->
+                    <execution>
+                        <id>default-testCompile</id>
+                        <phase>none</phase>
+                    </execution>
+                    <execution>
+                        <id>java-compile</id>
+                        <phase>compile</phase>
+                        <goals> <goal>compile</goal> </goals>
+                    </execution>
+                    <execution>
+                        <id>java-test-compile</id>
+                        <phase>test-compile</phase>
+                        <goals> <goal>testCompile</goal> </goals>
+                    </execution>
+                </executions>
+                <configuration>
+                    <source>${java.version}</source>
+                    <target>${java.version}</target>
+                </configuration>
+            </plugin>
+        </plugins>
+    </build>
+
+</project>

contrib/codegen-tools/codegen/src/main/java/org/nd4j/codegen/impl/java/JavaPoetGenerator.java

@@ -19,17 +19,13 @@
  */
 
 package org.nd4j.codegen.impl.java;
-
 import org.apache.commons.lang3.StringUtils;
 import org.nd4j.codegen.api.Language;
-import org.nd4j.codegen.api.Namespace;
 import org.nd4j.codegen.api.NamespaceOps;
-import org.nd4j.codegen.api.Op;
 import org.nd4j.codegen.api.generator.Generator;
 import org.nd4j.codegen.api.generator.GeneratorConfig;
 
 import java.io.File;
-import java.io.IOException;
 
 public class JavaPoetGenerator implements Generator {
 
@@ -40,12 +36,12 @@ public class JavaPoetGenerator implements Generator {
     }
 
     @Override
-    public void generateNamespaceNd4j(NamespaceOps namespace, GeneratorConfig config, File directory, String className) throws IOException {
+    public void generateNamespaceNd4j(NamespaceOps namespace, GeneratorConfig config, File directory, String className) throws java.io.IOException {
         Nd4jNamespaceGenerator.generate(namespace, config, directory, className, "org.nd4j.linalg.factory", StringUtils.EMPTY);
     }
 
     @Override
-    public void generateNamespaceSameDiff(NamespaceOps namespace, GeneratorConfig config, File directory, String className) throws IOException {
+    public void generateNamespaceSameDiff(NamespaceOps namespace, GeneratorConfig config, File directory, String className) throws java.io.IOException {
         //throw new UnsupportedOperationException("Not yet implemented");
         Nd4jNamespaceGenerator.generate(namespace, config, directory, className, "org.nd4j.autodiff.samediff", StringUtils.EMPTY);
     }

contrib/codegen-tools/codegen/src/main/ops/org/nd4j/codegen/ops/SDLoss.kt

@@ -47,6 +47,23 @@ fun SDLoss() =  Namespace("Loss"){
         }
     }
 
+
+    Op("ctcLoss") {
+        javaPackage = "org.nd4j.linalg.api.ops.impl.loss"
+        javaOpClass = "CtcLoss"
+        Input(NUMERIC, "targetLabels") { description = "Label array" }
+        Input(NUMERIC, "logitInput") { description = "Inputs" }
+        Input(NUMERIC, "targetLabelLengths") { description = "Length of the target label" }
+        Input(NUMERIC, "logitInputLengths") { description = "Length of the input"}
+        Output(NUMERIC, "output"){ description = "Ctc loss " }
+        Doc(Language.ANY, DocScope.ALL){
+            """
+                CTC Loss: Connectionist Temporal Classification Loss. See:
+                https://dl.acm.org/citation.cfm?id=1143891
+            """.trimIndent()
+        }
+    }
+
     Op("cosineDistance") {
         javaPackage = "org.nd4j.linalg.api.ops.impl.loss"
         javaOpClass = "CosineDistanceLoss"

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/samediff/ops/SDBaseOps.java

@@ -1,32 +1,26 @@
-/*
+/*******************************************************************************
- *  ******************************************************************************
+ * Copyright (c) 2019-2020 Konduit K.K.
- *  *
+ *
- *  *
+ * This program and the accompanying materials are made available under the
- *  * This program and the accompanying materials are made available under the
+ * terms of the Apache License, Version 2.0 which is available at
- *  * terms of the Apache License, Version 2.0 which is available at
+ * https://www.apache.org/licenses/LICENSE-2.0.
- *  * https://www.apache.org/licenses/LICENSE-2.0.
+ *
- *  *
+ * Unless required by applicable law or agreed to in writing, software
- *  *  See the NOTICE file distributed with this work for additional
+ * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
- *  *  information regarding copyright ownership.
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
- *  * Unless required by applicable law or agreed to in writing, software
+ * License for the specific language governing permissions and limitations
- *  * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+ * under the License.
- *  * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+ *
- *  * License for the specific language governing permissions and limitations
+ * SPDX-License-Identifier: Apache-2.0
- *  * under the License.
+ ******************************************************************************/
- *  *
+
- *  * SPDX-License-Identifier: Apache-2.0
+//================== GENERATED CODE - DO NOT MODIFY THIS FILE ==================
- *  *****************************************************************************
- */
 
 package org.nd4j.autodiff.samediff.ops;
 
 import static org.nd4j.autodiff.samediff.ops.SDValidation.isSameType;
 
 import java.lang.String;
-import java.util.Arrays;
-import java.util.concurrent.atomic.AtomicInteger;
-import java.util.stream.Collectors;
-
 import org.nd4j.autodiff.samediff.SDVariable;
 import org.nd4j.autodiff.samediff.SameDiff;
 import org.nd4j.common.base.Preconditions;
@@ -2799,47 +2793,6 @@ public class SDBaseOps {
     return sd.updateVariableNameAndReference(out, name);
   }
 
-
-  /**
-   * Split the input in to a list of sub tensors
-   * @param input the input to split
-   * @param numSizeSplits the number of splits
-   * @param splitDim the dimension to split along
-   * @return the set of output variables
-   */
-  public SDVariable[] split(SDVariable input,int numSizeSplits,int splitDim) {
-    SDValidation.validateNumerical("split",input);
-    SDVariable[] out =  new org.nd4j.linalg.api.ops.impl.shape.Split(sd,input,numSizeSplits,splitDim).outputVariables();
-    return out;
-  }
-
-  /**
-   * Split the input in to a list of sub tensors
-   * @param name the potential name of the input
-   * @param input the input to split
-   * @param numSizeSplits the number of splits
-   * @param splitDim the dimension to split along
-   * @return the set of output variables
-   */
-  public SDVariable[] split(String name,SDVariable input,int numSizeSplits,int splitDim) {
-    SDValidation.validateNumerical("split",input);
-    SDVariable[] out =  new org.nd4j.linalg.api.ops.impl.shape.Split(sd,input,numSizeSplits,splitDim).outputVariables();
-    SDVariable[] ret = new SDVariable[out.length];
-    AtomicInteger index = new AtomicInteger(0);
-    Arrays.stream(out).forEach(output -> {
-      if(index.get() < 1) {
-        ret[index.get()] = sd.updateVariableNameAndReference(output,name);
-        index.incrementAndGet();
-      }
-      else {
-        ret[index.get()] = sd.updateVariableNameAndReference(output,name + ":" + index.get());
-        index.incrementAndGet();
-      }
-    });
-
-    return ret;
-  }
-
   /**
    * Reshape the input variable to the specified (fixed) shape. The output variable will have the same values as the<br>
    * input, but with the specified shape.<br>

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/samediff/ops/SDBitwise.java

@@ -1,22 +1,20 @@
-/*
+/*******************************************************************************
- *  ******************************************************************************
+ * Copyright (c) 2019-2020 Konduit K.K.
- *  *
+ *
- *  *
+ * This program and the accompanying materials are made available under the
- *  * This program and the accompanying materials are made available under the
+ * terms of the Apache License, Version 2.0 which is available at
- *  * terms of the Apache License, Version 2.0 which is available at
+ * https://www.apache.org/licenses/LICENSE-2.0.
- *  * https://www.apache.org/licenses/LICENSE-2.0.
+ *
- *  *
+ * Unless required by applicable law or agreed to in writing, software
- *  *  See the NOTICE file distributed with this work for additional
+ * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
- *  *  information regarding copyright ownership.
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
- *  * Unless required by applicable law or agreed to in writing, software
+ * License for the specific language governing permissions and limitations
- *  * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+ * under the License.
- *  * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+ *
- *  * License for the specific language governing permissions and limitations
+ * SPDX-License-Identifier: Apache-2.0
- *  * under the License.
+ ******************************************************************************/
- *  *
+
- *  * SPDX-License-Identifier: Apache-2.0
+//================== GENERATED CODE - DO NOT MODIFY THIS FILE ==================
- *  *****************************************************************************
- */
 
 package org.nd4j.autodiff.samediff.ops;
 
@@ -250,7 +248,7 @@ public class SDBitwise extends SDOps {
 
   /**
    * Bitwise left cyclical shift operation. Supports broadcasting.<br>
-   * Unlike {@link #leftShift(INDArray, INDArray)} the bits will "wrap around":<br>
+   * Unlike #leftShift(INDArray, INDArray) the bits will "wrap around":<br>
    * {@code leftShiftCyclic(01110000, 2) -> 11000001}<br>
    *
    * @param x Input to be bit shifted (INT type)
@@ -265,7 +263,7 @@ public class SDBitwise extends SDOps {
 
   /**
    * Bitwise left cyclical shift operation. Supports broadcasting.<br>
-   * Unlike {@link #leftShift(INDArray, INDArray)} the bits will "wrap around":<br>
+   * Unlike #leftShift(INDArray, INDArray) the bits will "wrap around":<br>
    * {@code leftShiftCyclic(01110000, 2) -> 11000001}<br>
    *
    * @param name name May be null. Name for the output variable
@@ -348,7 +346,7 @@ public class SDBitwise extends SDOps {
 
   /**
    * Bitwise right cyclical shift operation. Supports broadcasting.<br>
-   * Unlike {@link #rightShift(INDArray, INDArray)} the bits will "wrap around":<br>
+   * Unlike rightShift(INDArray, INDArray) the bits will "wrap around":<br>
    * {@code rightShiftCyclic(00001110, 2) -> 10000011}<br>
    *
    * @param x Input to be bit shifted (INT type)
@@ -363,7 +361,7 @@ public class SDBitwise extends SDOps {
 
   /**
    * Bitwise right cyclical shift operation. Supports broadcasting.<br>
-   * Unlike {@link #rightShift(INDArray, INDArray)} the bits will "wrap around":<br>
+   * Unlike rightShift(INDArray, INDArray) the bits will "wrap around":<br>
    * {@code rightShiftCyclic(00001110, 2) -> 10000011}<br>
    *
    * @param name name May be null. Name for the output variable

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/samediff/ops/SDCNN.java

@@ -1,25 +1,25 @@
-/*
+/*******************************************************************************
- *  ******************************************************************************
+ * Copyright (c) 2019-2020 Konduit K.K.
- *  *
+ *
- *  *
+ * This program and the accompanying materials are made available under the
- *  * This program and the accompanying materials are made available under the
+ * terms of the Apache License, Version 2.0 which is available at
- *  * terms of the Apache License, Version 2.0 which is available at
+ * https://www.apache.org/licenses/LICENSE-2.0.
- *  * https://www.apache.org/licenses/LICENSE-2.0.
+ *
- *  *
+ * Unless required by applicable law or agreed to in writing, software
- *  *  See the NOTICE file distributed with this work for additional
+ * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
- *  *  information regarding copyright ownership.
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
- *  * Unless required by applicable law or agreed to in writing, software
+ * License for the specific language governing permissions and limitations
- *  * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+ * under the License.
- *  * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+ *
- *  * License for the specific language governing permissions and limitations
+ * SPDX-License-Identifier: Apache-2.0
- *  * under the License.
+ ******************************************************************************/
- *  *
+
- *  * SPDX-License-Identifier: Apache-2.0
+//================== GENERATED CODE - DO NOT MODIFY THIS FILE ==================
- *  *****************************************************************************
- */
 
 package org.nd4j.autodiff.samediff.ops;
 
+import static org.nd4j.autodiff.samediff.ops.SDValidation.isSameType;
+
 import java.lang.String;
 import org.nd4j.autodiff.samediff.SDVariable;
 import org.nd4j.autodiff.samediff.SameDiff;
@@ -42,8 +42,7 @@ public class SDCNN extends SDOps {
   /**
    * 2D Convolution layer operation - average pooling 2d<br>
    *
-   * @param input the input to average pooling 2d operation - 4d CNN (image) activations in NCHW format
+   * @param input the input to average pooling 2d operation - 4d CNN (image) activations in NCHW format (shape [minibatch, channels, height, width]) or NHWC format (shape [minibatch, height, width, channels]) (NUMERIC type)
-   *                         (shape [minibatch, channels, height, width]) or NHWC format (shape [minibatch, height, width, channels]) (NUMERIC type)
    * @param Pooling2DConfig Configuration Object
    * @return output Result after applying average pooling on the input (NUMERIC type)
    */
@@ -56,8 +55,7 @@ public class SDCNN extends SDOps {
    * 2D Convolution layer operation - average pooling 2d<br>
    *
    * @param name name May be null. Name for the output variable
-   * @param input the input to average pooling 2d operation - 4d CNN (image) activations in NCHW format
+   * @param input the input to average pooling 2d operation - 4d CNN (image) activations in NCHW format (shape [minibatch, channels, height, width]) or NHWC format (shape [minibatch, height, width, channels]) (NUMERIC type)
-   *                         (shape [minibatch, channels, height, width]) or NHWC format (shape [minibatch, height, width, channels]) (NUMERIC type)
    * @param Pooling2DConfig Configuration Object
    * @return output Result after applying average pooling on the input (NUMERIC type)
    */
@@ -70,9 +68,7 @@ public class SDCNN extends SDOps {
   /**
    * 3D convolution layer operation - average pooling 3d <br>
    *
-   * @param input the input to average pooling 3d operation - 5d activations in NCDHW format
+   * @param input the input to average pooling 3d operation - 5d activations in NCDHW format (shape [minibatch, channels, depth, height, width]) or NDHWC format (shape [minibatch, depth, height, width, channels]) (NUMERIC type)
-   *                         (shape [minibatch, channels, depth, height, width]) or NDHWC format
-   *                         (shape [minibatch, depth, height, width, channels]) (NUMERIC type)
    * @param Pooling3DConfig Configuration Object
    * @return output after applying average pooling on the input (NUMERIC type)
    */
@@ -85,9 +81,7 @@ public class SDCNN extends SDOps {
    * 3D convolution layer operation - average pooling 3d <br>
    *
    * @param name name May be null. Name for the output variable
-   * @param input the input to average pooling 3d operation - 5d activations in NCDHW format
+   * @param input the input to average pooling 3d operation - 5d activations in NCDHW format (shape [minibatch, channels, depth, height, width]) or NDHWC format (shape [minibatch, depth, height, width, channels]) (NUMERIC type)
-   *                         (shape [minibatch, channels, depth, height, width]) or NDHWC format
-   *                         (shape [minibatch, depth, height, width, channels]) (NUMERIC type)
    * @param Pooling3DConfig Configuration Object
    * @return output after applying average pooling on the input (NUMERIC type)
    */
@@ -302,9 +296,7 @@ public class SDCNN extends SDOps {
   /**
    * Convolution 3D operation with optional bias <br>
    *
-   * @param input the input to average pooling 3d operation - 5d activations in NCDHW format
+   * @param input the input to average pooling 3d operation - 5d activations in NCDHW format (shape [minibatch, channels, depth, height, width]) or NDHWC format (shape [minibatch, depth, height, width, channels]) (NUMERIC type)
-   * (shape [minibatch, channels, depth, height, width]) or NDHWC format
-   * (shape [minibatch, depth, height, width, channels]) (NUMERIC type)
    * @param weights  Weights for conv3d. Rank 5 with shape [kernelDepth, kernelHeight, kernelWidth, inputChannels, outputChannels]. (NUMERIC type)
    * @param bias  Optional 1D bias array with shape [outputChannels]. May be null. (NUMERIC type)
    * @param Conv3DConfig Configuration Object
@@ -322,9 +314,7 @@ public class SDCNN extends SDOps {
    * Convolution 3D operation with optional bias <br>
    *
    * @param name name May be null. Name for the output variable
-   * @param input the input to average pooling 3d operation - 5d activations in NCDHW format
+   * @param input the input to average pooling 3d operation - 5d activations in NCDHW format (shape [minibatch, channels, depth, height, width]) or NDHWC format (shape [minibatch, depth, height, width, channels]) (NUMERIC type)
-   * (shape [minibatch, channels, depth, height, width]) or NDHWC format
-   * (shape [minibatch, depth, height, width, channels]) (NUMERIC type)
    * @param weights  Weights for conv3d. Rank 5 with shape [kernelDepth, kernelHeight, kernelWidth, inputChannels, outputChannels]. (NUMERIC type)
    * @param bias  Optional 1D bias array with shape [outputChannels]. May be null. (NUMERIC type)
    * @param Conv3DConfig Configuration Object
@@ -342,9 +332,7 @@ public class SDCNN extends SDOps {
   /**
    * Convolution 3D operation with optional bias <br>
    *
-   * @param input the input to average pooling 3d operation - 5d activations in NCDHW format
+   * @param input the input to average pooling 3d operation - 5d activations in NCDHW format (shape [minibatch, channels, depth, height, width]) or NDHWC format (shape [minibatch, depth, height, width, channels]) (NUMERIC type)
-   * (shape [minibatch, channels, depth, height, width]) or NDHWC format
-   * (shape [minibatch, depth, height, width, channels]) (NUMERIC type)
    * @param weights  Weights for conv3d. Rank 5 with shape [kernelDepth, kernelHeight, kernelWidth, inputChannels, outputChannels]. (NUMERIC type)
    * @param Conv3DConfig Configuration Object
    * @return output Conv3d output variable (NUMERIC type)
@@ -359,9 +347,7 @@ public class SDCNN extends SDOps {
    * Convolution 3D operation with optional bias <br>
    *
    * @param name name May be null. Name for the output variable
-   * @param input the input to average pooling 3d operation - 5d activations in NCDHW format
+   * @param input the input to average pooling 3d operation - 5d activations in NCDHW format (shape [minibatch, channels, depth, height, width]) or NDHWC format (shape [minibatch, depth, height, width, channels]) (NUMERIC type)
-   * (shape [minibatch, channels, depth, height, width]) or NDHWC format
-   * (shape [minibatch, depth, height, width, channels]) (NUMERIC type)
    * @param weights  Weights for conv3d. Rank 5 with shape [kernelDepth, kernelHeight, kernelWidth, inputChannels, outputChannels]. (NUMERIC type)
    * @param Conv3DConfig Configuration Object
    * @return output Conv3d output variable (NUMERIC type)
@@ -377,8 +363,7 @@ public class SDCNN extends SDOps {
   /**
    * 2D deconvolution operation with optional bias<br>
    *
-   * @param layerInput the input to deconvolution 2d operation - 4d CNN (image) activations in NCHW format
+   * @param layerInput the input to deconvolution 2d operation - 4d CNN (image) activations in NCHW format (shape [minibatch, channels, height, width]) or NHWC format (shape [minibatch, height, width, channels]) (NUMERIC type)
-   * (shape [minibatch, channels, height, width]) or NHWC format (shape [minibatch, height, width, channels]) (NUMERIC type)
    * @param weights Weights for the 2d deconvolution operation. 4 dimensions with format [inputChannels, outputChannels, kernelHeight, kernelWidth] (NUMERIC type)
    * @param bias Optional 1D bias array with shape [outputChannels]. May be null. (NUMERIC type)
    * @param DeConv2DConfig Configuration Object
@@ -396,8 +381,7 @@ public class SDCNN extends SDOps {
    * 2D deconvolution operation with optional bias<br>
    *
    * @param name name May be null. Name for the output variable
-   * @param layerInput the input to deconvolution 2d operation - 4d CNN (image) activations in NCHW format
+   * @param layerInput the input to deconvolution 2d operation - 4d CNN (image) activations in NCHW format (shape [minibatch, channels, height, width]) or NHWC format (shape [minibatch, height, width, channels]) (NUMERIC type)
-   * (shape [minibatch, channels, height, width]) or NHWC format (shape [minibatch, height, width, channels]) (NUMERIC type)
    * @param weights Weights for the 2d deconvolution operation. 4 dimensions with format [inputChannels, outputChannels, kernelHeight, kernelWidth] (NUMERIC type)
    * @param bias Optional 1D bias array with shape [outputChannels]. May be null. (NUMERIC type)
    * @param DeConv2DConfig Configuration Object
@@ -415,8 +399,7 @@ public class SDCNN extends SDOps {
   /**
    * 2D deconvolution operation with optional bias<br>
    *
-   * @param layerInput the input to deconvolution 2d operation - 4d CNN (image) activations in NCHW format
+   * @param layerInput the input to deconvolution 2d operation - 4d CNN (image) activations in NCHW format (shape [minibatch, channels, height, width]) or NHWC format (shape [minibatch, height, width, channels]) (NUMERIC type)
-   * (shape [minibatch, channels, height, width]) or NHWC format (shape [minibatch, height, width, channels]) (NUMERIC type)
    * @param weights Weights for the 2d deconvolution operation. 4 dimensions with format [inputChannels, outputChannels, kernelHeight, kernelWidth] (NUMERIC type)
    * @param DeConv2DConfig Configuration Object
    * @return output result of deconv2d op (NUMERIC type)
@@ -432,8 +415,7 @@ public class SDCNN extends SDOps {
    * 2D deconvolution operation with optional bias<br>
    *
    * @param name name May be null. Name for the output variable
-   * @param layerInput the input to deconvolution 2d operation - 4d CNN (image) activations in NCHW format
+   * @param layerInput the input to deconvolution 2d operation - 4d CNN (image) activations in NCHW format (shape [minibatch, channels, height, width]) or NHWC format (shape [minibatch, height, width, channels]) (NUMERIC type)
-   * (shape [minibatch, channels, height, width]) or NHWC format (shape [minibatch, height, width, channels]) (NUMERIC type)
    * @param weights Weights for the 2d deconvolution operation. 4 dimensions with format [inputChannels, outputChannels, kernelHeight, kernelWidth] (NUMERIC type)
    * @param DeConv2DConfig Configuration Object
    * @return output result of deconv2d op (NUMERIC type)
@@ -519,8 +501,7 @@ public class SDCNN extends SDOps {
    * Example: if input has shape [mb, 8, 2, 2] and block size is 2, then output size is [mb, 8/(2*2), 2*2, 2*2]<br>
    * = [mb, 2, 4, 4]<br>
    *
-   * @param x the input to depth to space pooling 2d operation - 4d activations in NCHW format
+   * @param x the input to depth to space pooling 2d operation - 4d activations in NCHW format (shape [minibatch, channels, height, width]) or NHWC format (shape [minibatch, height, width, channels]) (NUMERIC type)
-   *                    (shape [minibatch, channels, height, width]) or NHWC format (shape [minibatch, height, width, channels]) (NUMERIC type)
    * @param blockSize Block size, in the height/width dimension
    * @param dataFormat Data format: "NCHW" or "NHWC"
    * @return output Output variable (NUMERIC type)
@@ -537,8 +518,7 @@ public class SDCNN extends SDOps {
    * = [mb, 2, 4, 4]<br>
    *
    * @param name name May be null. Name for the output variable
-   * @param x the input to depth to space pooling 2d operation - 4d activations in NCHW format
+   * @param x the input to depth to space pooling 2d operation - 4d activations in NCHW format (shape [minibatch, channels, height, width]) or NHWC format (shape [minibatch, height, width, channels]) (NUMERIC type)
-   *                    (shape [minibatch, channels, height, width]) or NHWC format (shape [minibatch, height, width, channels]) (NUMERIC type)
    * @param blockSize Block size, in the height/width dimension
    * @param dataFormat Data format: "NCHW" or "NHWC"
    * @return output Output variable (NUMERIC type)
@@ -756,8 +736,7 @@ public class SDCNN extends SDOps {
   /**
    * 2D Convolution layer operation - Max pooling on the input and outputs both max values and indices <br>
    *
-   * @param input the input to max pooling 2d operation - 4d CNN (image) activations in NCHW format
+   * @param input the input to max pooling 2d operation - 4d CNN (image) activations in NCHW format (shape [minibatch, channels, height, width]) or NHWC format (shape [minibatch, height, width, channels]) (NUMERIC type)
-   *                         (shape [minibatch, channels, height, width]) or NHWC format (shape [minibatch, height, width, channels]) (NUMERIC type)
    * @param Pooling2DConfig Configuration Object
    */
   public SDVariable[] maxPoolWithArgmax(SDVariable input, Pooling2DConfig Pooling2DConfig) {
@@ -769,8 +748,7 @@ public class SDCNN extends SDOps {
    * 2D Convolution layer operation - Max pooling on the input and outputs both max values and indices <br>
    *
    * @param names names May be null. Arrays of names for the output variables.
-   * @param input the input to max pooling 2d operation - 4d CNN (image) activations in NCHW format
+   * @param input the input to max pooling 2d operation - 4d CNN (image) activations in NCHW format (shape [minibatch, channels, height, width]) or NHWC format (shape [minibatch, height, width, channels]) (NUMERIC type)
-   *                         (shape [minibatch, channels, height, width]) or NHWC format (shape [minibatch, height, width, channels]) (NUMERIC type)
    * @param Pooling2DConfig Configuration Object
    */
   public SDVariable[] maxPoolWithArgmax(String[] names, SDVariable input,
@@ -783,8 +761,7 @@ public class SDCNN extends SDOps {
   /**
    * 2D Convolution layer operation - max pooling 2d <br>
    *
-   * @param input the input to max pooling 2d operation - 4d CNN (image) activations in NCHW format
+   * @param input the input to max pooling 2d operation - 4d CNN (image) activations in NCHW format (shape [minibatch, channels, height, width]) or NHWC format (shape [minibatch, height, width, channels]) (NUMERIC type)
-   *                         (shape [minibatch, channels, height, width]) or NHWC format (shape [minibatch, height, width, channels]) (NUMERIC type)
    * @param Pooling2DConfig Configuration Object
    * @return output Result after applying max pooling on the input (NUMERIC type)
    */
@@ -797,8 +774,7 @@ public class SDCNN extends SDOps {
    * 2D Convolution layer operation - max pooling 2d <br>
    *
    * @param name name May be null. Name for the output variable
-   * @param input the input to max pooling 2d operation - 4d CNN (image) activations in NCHW format
+   * @param input the input to max pooling 2d operation - 4d CNN (image) activations in NCHW format (shape [minibatch, channels, height, width]) or NHWC format (shape [minibatch, height, width, channels]) (NUMERIC type)
-   *                         (shape [minibatch, channels, height, width]) or NHWC format (shape [minibatch, height, width, channels]) (NUMERIC type)
    * @param Pooling2DConfig Configuration Object
    * @return output Result after applying max pooling on the input (NUMERIC type)
    */
@@ -811,9 +787,7 @@ public class SDCNN extends SDOps {
   /**
    * 3D convolution layer operation - max pooling 3d operation.<br>
    *
-   * @param input the input to average pooling 3d operation - 5d activations in NCDHW format
+   * @param input the input to average pooling 3d operation - 5d activations in NCDHW format (shape [minibatch, channels, depth, height, width]) or NDHWC format (shape [minibatch, depth, height, width, channels]) (NUMERIC type)
-   *                         (shape [minibatch, channels, depth, height, width]) or NDHWC format
-   *                         (shape [minibatch, depth, height, width, channels]) (NUMERIC type)
    * @param Pooling3DConfig Configuration Object
    * @return output Result after applying max pooling on the input (NUMERIC type)
    */
@@ -826,9 +800,7 @@ public class SDCNN extends SDOps {
    * 3D convolution layer operation - max pooling 3d operation.<br>
    *
    * @param name name May be null. Name for the output variable
-   * @param input the input to average pooling 3d operation - 5d activations in NCDHW format
+   * @param input the input to average pooling 3d operation - 5d activations in NCDHW format (shape [minibatch, channels, depth, height, width]) or NDHWC format (shape [minibatch, depth, height, width, channels]) (NUMERIC type)
-   *                         (shape [minibatch, channels, depth, height, width]) or NDHWC format
-   *                         (shape [minibatch, depth, height, width, channels]) (NUMERIC type)
    * @param Pooling3DConfig Configuration Object
    * @return output Result after applying max pooling on the input (NUMERIC type)
    */
@@ -841,8 +813,7 @@ public class SDCNN extends SDOps {
   /**
    * Separable 2D convolution operation with optional bias <br>
    *
-   * @param layerInput the input to max pooling 2d operation - 4d CNN (image) activations in NCHW format
+   * @param layerInput the input to max pooling 2d operation - 4d CNN (image) activations in NCHW format (shape [minibatch, channels, height, width]) or NHWC format (shape [minibatch, height, width, channels]) (NUMERIC type)
-   *                      (shape [minibatch, channels, height, width]) or NHWC format (shape [minibatch, height, width, channels]) (NUMERIC type)
    * @param depthWeights Separable conv2d depth weights. 4 dimensions with format [kernelHeight, kernelWidth, inputChannels, depthMultiplier] (NUMERIC type)
    * @param pointWeights Point weights, rank 4 with format [1, 1, inputChannels*depthMultiplier, outputChannels]. May be null (NUMERIC type)
    * @param bias Optional bias, rank 1 with shape [outputChannels]. May be null. (NUMERIC type)
@@ -862,8 +833,7 @@ public class SDCNN extends SDOps {
    * Separable 2D convolution operation with optional bias <br>
    *
    * @param name name May be null. Name for the output variable
-   * @param layerInput the input to max pooling 2d operation - 4d CNN (image) activations in NCHW format
+   * @param layerInput the input to max pooling 2d operation - 4d CNN (image) activations in NCHW format (shape [minibatch, channels, height, width]) or NHWC format (shape [minibatch, height, width, channels]) (NUMERIC type)
-   *                      (shape [minibatch, channels, height, width]) or NHWC format (shape [minibatch, height, width, channels]) (NUMERIC type)
    * @param depthWeights Separable conv2d depth weights. 4 dimensions with format [kernelHeight, kernelWidth, inputChannels, depthMultiplier] (NUMERIC type)
    * @param pointWeights Point weights, rank 4 with format [1, 1, inputChannels*depthMultiplier, outputChannels]. May be null (NUMERIC type)
    * @param bias Optional bias, rank 1 with shape [outputChannels]. May be null. (NUMERIC type)
@@ -883,8 +853,7 @@ public class SDCNN extends SDOps {
   /**
    * Separable 2D convolution operation with optional bias <br>
    *
-   * @param layerInput the input to max pooling 2d operation - 4d CNN (image) activations in NCHW format
+   * @param layerInput the input to max pooling 2d operation - 4d CNN (image) activations in NCHW format (shape [minibatch, channels, height, width]) or NHWC format (shape [minibatch, height, width, channels]) (NUMERIC type)
-   *                      (shape [minibatch, channels, height, width]) or NHWC format (shape [minibatch, height, width, channels]) (NUMERIC type)
    * @param depthWeights Separable conv2d depth weights. 4 dimensions with format [kernelHeight, kernelWidth, inputChannels, depthMultiplier] (NUMERIC type)
    * @param pointWeights Point weights, rank 4 with format [1, 1, inputChannels*depthMultiplier, outputChannels]. May be null (NUMERIC type)
    * @param Conv2DConfig Configuration Object
@@ -902,8 +871,7 @@ public class SDCNN extends SDOps {
    * Separable 2D convolution operation with optional bias <br>
    *
    * @param name name May be null. Name for the output variable
-   * @param layerInput the input to max pooling 2d operation - 4d CNN (image) activations in NCHW format
+   * @param layerInput the input to max pooling 2d operation - 4d CNN (image) activations in NCHW format (shape [minibatch, channels, height, width]) or NHWC format (shape [minibatch, height, width, channels]) (NUMERIC type)
-   *                      (shape [minibatch, channels, height, width]) or NHWC format (shape [minibatch, height, width, channels]) (NUMERIC type)
    * @param depthWeights Separable conv2d depth weights. 4 dimensions with format [kernelHeight, kernelWidth, inputChannels, depthMultiplier] (NUMERIC type)
    * @param pointWeights Point weights, rank 4 with format [1, 1, inputChannels*depthMultiplier, outputChannels]. May be null (NUMERIC type)
    * @param Conv2DConfig Configuration Object
@@ -964,8 +932,7 @@ public class SDCNN extends SDOps {
    * Example: if input has shape [mb, 2, 4, 4] and block size is 2, then output size is [mb, 8/(2*2), 2*2, 2*2]<br>
    * = [mb, 2, 4, 4] <br>
    *
-   * @param x the input to depth to space pooling 2d operation - 4d activations in NCHW format
+   * @param x the input to depth to space pooling 2d operation - 4d activations in NCHW format (shape [minibatch, channels, height, width]) or NHWC format (shape [minibatch, height, width, channels]) (NUMERIC type)
-   *                    (shape [minibatch, channels, height, width]) or NHWC format (shape [minibatch, height, width, channels]) (NUMERIC type)
    * @param blockSize  Block size, in the height/width dimension
    * @param dataFormat Data format: "NCHW" or "NHWC"
    * @return output Output variable (NUMERIC type)
@@ -982,8 +949,7 @@ public class SDCNN extends SDOps {
    * = [mb, 2, 4, 4] <br>
    *
    * @param name name May be null. Name for the output variable
-   * @param x the input to depth to space pooling 2d operation - 4d activations in NCHW format
+   * @param x the input to depth to space pooling 2d operation - 4d activations in NCHW format (shape [minibatch, channels, height, width]) or NHWC format (shape [minibatch, height, width, channels]) (NUMERIC type)
-   *                    (shape [minibatch, channels, height, width]) or NHWC format (shape [minibatch, height, width, channels]) (NUMERIC type)
    * @param blockSize  Block size, in the height/width dimension
    * @param dataFormat Data format: "NCHW" or "NHWC"
    * @return output Output variable (NUMERIC type)

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/samediff/ops/SDImage.java

@@ -1,25 +1,25 @@
-/*
+/*******************************************************************************
- *  ******************************************************************************
+ * Copyright (c) 2019-2020 Konduit K.K.
- *  *
+ *
- *  *
+ * This program and the accompanying materials are made available under the
- *  * This program and the accompanying materials are made available under the
+ * terms of the Apache License, Version 2.0 which is available at
- *  * terms of the Apache License, Version 2.0 which is available at
+ * https://www.apache.org/licenses/LICENSE-2.0.
- *  * https://www.apache.org/licenses/LICENSE-2.0.
+ *
- *  *
+ * Unless required by applicable law or agreed to in writing, software
- *  *  See the NOTICE file distributed with this work for additional
+ * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
- *  *  information regarding copyright ownership.
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
- *  * Unless required by applicable law or agreed to in writing, software
+ * License for the specific language governing permissions and limitations
- *  * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+ * under the License.
- *  * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+ *
- *  * License for the specific language governing permissions and limitations
+ * SPDX-License-Identifier: Apache-2.0
- *  * under the License.
+ ******************************************************************************/
- *  *
+
- *  * SPDX-License-Identifier: Apache-2.0
+//================== GENERATED CODE - DO NOT MODIFY THIS FILE ==================
- *  *****************************************************************************
- */
 
 package org.nd4j.autodiff.samediff.ops;
 
+import static org.nd4j.autodiff.samediff.ops.SDValidation.isSameType;
+
 import java.lang.String;
 import org.nd4j.autodiff.samediff.SDVariable;
 import org.nd4j.autodiff.samediff.SameDiff;

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/samediff/ops/SDLinalg.java

@@ -1,25 +1,25 @@
-/*
+/*******************************************************************************
- *  ******************************************************************************
+ * Copyright (c) 2019-2020 Konduit K.K.
- *  *
+ *
- *  *
+ * This program and the accompanying materials are made available under the
- *  * This program and the accompanying materials are made available under the
+ * terms of the Apache License, Version 2.0 which is available at
- *  * terms of the Apache License, Version 2.0 which is available at
+ * https://www.apache.org/licenses/LICENSE-2.0.
- *  * https://www.apache.org/licenses/LICENSE-2.0.
+ *
- *  *
+ * Unless required by applicable law or agreed to in writing, software
- *  *  See the NOTICE file distributed with this work for additional
+ * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
- *  *  information regarding copyright ownership.
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
- *  * Unless required by applicable law or agreed to in writing, software
+ * License for the specific language governing permissions and limitations
- *  * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+ * under the License.
- *  * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+ *
- *  * License for the specific language governing permissions and limitations
+ * SPDX-License-Identifier: Apache-2.0
- *  * under the License.
+ ******************************************************************************/
- *  *
+
- *  * SPDX-License-Identifier: Apache-2.0
+//================== GENERATED CODE - DO NOT MODIFY THIS FILE ==================
- *  *****************************************************************************
- */
 
 package org.nd4j.autodiff.samediff.ops;
 
+import static org.nd4j.autodiff.samediff.ops.SDValidation.isSameType;
+
 import java.lang.String;
 import org.nd4j.autodiff.samediff.SDVariable;
 import org.nd4j.autodiff.samediff.SameDiff;

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/samediff/ops/SDLoss.java

@@ -1,25 +1,25 @@
-/*
+/*******************************************************************************
- *  ******************************************************************************
+ * Copyright (c) 2019-2020 Konduit K.K.
- *  *
+ *
- *  *
+ * This program and the accompanying materials are made available under the
- *  * This program and the accompanying materials are made available under the
+ * terms of the Apache License, Version 2.0 which is available at
- *  * terms of the Apache License, Version 2.0 which is available at
+ * https://www.apache.org/licenses/LICENSE-2.0.
- *  * https://www.apache.org/licenses/LICENSE-2.0.
+ *
- *  *
+ * Unless required by applicable law or agreed to in writing, software
- *  *  See the NOTICE file distributed with this work for additional
+ * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
- *  *  information regarding copyright ownership.
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
- *  * Unless required by applicable law or agreed to in writing, software
+ * License for the specific language governing permissions and limitations
- *  * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+ * under the License.
- *  * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+ *
- *  * License for the specific language governing permissions and limitations
+ * SPDX-License-Identifier: Apache-2.0
- *  * under the License.
+ ******************************************************************************/
- *  *
+
- *  * SPDX-License-Identifier: Apache-2.0
+//================== GENERATED CODE - DO NOT MODIFY THIS FILE ==================
- *  *****************************************************************************
- */
 
 package org.nd4j.autodiff.samediff.ops;
 
+import static org.nd4j.autodiff.samediff.ops.SDValidation.isSameType;
+
 import java.lang.String;
 import org.nd4j.autodiff.loss.LossReduce;
 import org.nd4j.autodiff.samediff.SDVariable;
@@ -36,7 +36,7 @@ public class SDLoss extends SDOps {
    * @param label Label array (NUMERIC type)
    * @param predictions Predictions array (NUMERIC type)
    * @param weights Weights array. May be null. If null, a weight of 1.0 is used (NUMERIC type)
-   * @param lossReduce Reduction type for the loss. See {@link LossReduce} for more details. Default: {@link LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT}
+   * @param lossReduce Reduction type for the loss. See LossReduce for more details. Default: LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT
    * @return output loss variable (NUMERIC type)
    */
   public SDVariable absoluteDifference(SDVariable label, SDVariable predictions, SDVariable weights,
@@ -56,7 +56,7 @@ public class SDLoss extends SDOps {
    * @param label Label array (NUMERIC type)
    * @param predictions Predictions array (NUMERIC type)
    * @param weights Weights array. May be null. If null, a weight of 1.0 is used (NUMERIC type)
-   * @param lossReduce Reduction type for the loss. See {@link LossReduce} for more details. Default: {@link LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT}
+   * @param lossReduce Reduction type for the loss. See LossReduce for more details. Default: LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT
    * @return output loss variable (NUMERIC type)
    */
   public SDVariable absoluteDifference(String name, SDVariable label, SDVariable predictions,
@@ -116,7 +116,7 @@ public class SDLoss extends SDOps {
    * @param label Label array (NUMERIC type)
    * @param predictions Predictions array (NUMERIC type)
    * @param weights Weights array. May be null. If null, a weight of 1.0 is use (NUMERIC type)
-   * @param lossReduce Reduction type for the loss. See {@link LossReduce} for more details. Default: {@link LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT}
+   * @param lossReduce Reduction type for the loss. See LossReduce for more details. Default: LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT
    * @param dimension Dimension to perform the cosine distance over
    * @return output Cosine distance loss  (NUMERIC type)
    */
@@ -141,7 +141,7 @@ public class SDLoss extends SDOps {
    * @param label Label array (NUMERIC type)
    * @param predictions Predictions array (NUMERIC type)
    * @param weights Weights array. May be null. If null, a weight of 1.0 is use (NUMERIC type)
-   * @param lossReduce Reduction type for the loss. See {@link LossReduce} for more details. Default: {@link LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT}
+   * @param lossReduce Reduction type for the loss. See LossReduce for more details. Default: LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT
    * @param dimension Dimension to perform the cosine distance over
    * @return output Cosine distance loss  (NUMERIC type)
    */
@@ -202,6 +202,49 @@ public class SDLoss extends SDOps {
     return sd.updateVariableNameAndReference(out, name);
   }
 
+  /**
+   * CTC Loss: Connectionist Temporal Classification Loss. See:<br>
+   * https://dl.acm.org/citation.cfm?id=1143891<br>
+   *
+   * @param targetLabels Label array (NUMERIC type)
+   * @param logitInput Inputs (NUMERIC type)
+   * @param targetLabelLengths Length of the target label (NUMERIC type)
+   * @param logitInputLengths Length of the input (NUMERIC type)
+   * @return output Ctc loss  (NUMERIC type)
+   */
+  public SDVariable ctcLoss(SDVariable targetLabels, SDVariable logitInput,
+      SDVariable targetLabelLengths, SDVariable logitInputLengths) {
+    SDValidation.validateNumerical("ctcLoss", "targetLabels", targetLabels);
+    SDValidation.validateNumerical("ctcLoss", "logitInput", logitInput);
+    SDValidation.validateNumerical("ctcLoss", "targetLabelLengths", targetLabelLengths);
+    SDValidation.validateNumerical("ctcLoss", "logitInputLengths", logitInputLengths);
+    SDVariable out = new org.nd4j.linalg.api.ops.impl.loss.CtcLoss(sd,targetLabels, logitInput, targetLabelLengths, logitInputLengths).outputVariable();
+    out.markAsLoss();
+    return out;
+  }
+
+  /**
+   * CTC Loss: Connectionist Temporal Classification Loss. See:<br>
+   * https://dl.acm.org/citation.cfm?id=1143891<br>
+   *
+   * @param name name May be null. Name for the output variable
+   * @param targetLabels Label array (NUMERIC type)
+   * @param logitInput Inputs (NUMERIC type)
+   * @param targetLabelLengths Length of the target label (NUMERIC type)
+   * @param logitInputLengths Length of the input (NUMERIC type)
+   * @return output Ctc loss  (NUMERIC type)
+   */
+  public SDVariable ctcLoss(String name, SDVariable targetLabels, SDVariable logitInput,
+      SDVariable targetLabelLengths, SDVariable logitInputLengths) {
+    SDValidation.validateNumerical("ctcLoss", "targetLabels", targetLabels);
+    SDValidation.validateNumerical("ctcLoss", "logitInput", logitInput);
+    SDValidation.validateNumerical("ctcLoss", "targetLabelLengths", targetLabelLengths);
+    SDValidation.validateNumerical("ctcLoss", "logitInputLengths", logitInputLengths);
+    SDVariable out =  new org.nd4j.linalg.api.ops.impl.loss.CtcLoss(sd,targetLabels, logitInput, targetLabelLengths, logitInputLengths).outputVariable();
+    out.markAsLoss();
+    return sd.updateVariableNameAndReference(out, name);
+  }
+
   /**
    * Hinge loss: a loss function used for training classifiers.<br>
    * Implements {@code L = max(0, 1 - t * predictions)} where t is the label values after internally converting to {-1,1}<br>
@@ -210,7 +253,7 @@ public class SDLoss extends SDOps {
    * @param label Label array. Each value should be 0.0 or 1.0 (internally -1 to 1 is used) (NUMERIC type)
    * @param predictions Predictions array (NUMERIC type)
    * @param weights Weights array. May be null. If null, a weight of 1.0 is used (NUMERIC type)
-   * @param lossReduce Reduction type for the loss. See {@link LossReduce} for more details. Default: {@link LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT}
+   * @param lossReduce Reduction type for the loss. See LossReduce for more details. Default: LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT
    * @return output Loss variable (NUMERIC type)
    */
   public SDVariable hingeLoss(SDVariable label, SDVariable predictions, SDVariable weights,
@@ -232,7 +275,7 @@ public class SDLoss extends SDOps {
    * @param label Label array. Each value should be 0.0 or 1.0 (internally -1 to 1 is used) (NUMERIC type)
    * @param predictions Predictions array (NUMERIC type)
    * @param weights Weights array. May be null. If null, a weight of 1.0 is used (NUMERIC type)
-   * @param lossReduce Reduction type for the loss. See {@link LossReduce} for more details. Default: {@link LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT}
+   * @param lossReduce Reduction type for the loss. See LossReduce for more details. Default: LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT
    * @return output Loss variable (NUMERIC type)
    */
   public SDVariable hingeLoss(String name, SDVariable label, SDVariable predictions,
@@ -297,7 +340,7 @@ public class SDLoss extends SDOps {
    * @param label Label array (NUMERIC type)
    * @param predictions Predictions array (NUMERIC type)
    * @param weights Weights array. May be null. If null, a weight of 1.0 is used (NUMERIC type)
-   * @param lossReduce Reduction type for the loss. See {@link LossReduce} for more details. Default: {@link LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT}
+   * @param lossReduce Reduction type for the loss. See LossReduce for more details. Default: LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT
    * @param delta Loss function delta value
    * @return output Huber loss (NUMERIC type)
    */
@@ -324,7 +367,7 @@ public class SDLoss extends SDOps {
    * @param label Label array (NUMERIC type)
    * @param predictions Predictions array (NUMERIC type)
    * @param weights Weights array. May be null. If null, a weight of 1.0 is used (NUMERIC type)
-   * @param lossReduce Reduction type for the loss. See {@link LossReduce} for more details. Default: {@link LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT}
+   * @param lossReduce Reduction type for the loss. See LossReduce for more details. Default: LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT
    * @param delta Loss function delta value
    * @return output Huber loss (NUMERIC type)
    */
@@ -423,7 +466,7 @@ public class SDLoss extends SDOps {
    * @param label Label array (NUMERIC type)
    * @param predictions Predictions array (NUMERIC type)
    * @param weights Weights array. May be null. If null, a weight of 1.0 is used (NUMERIC type)
-   * @param lossReduce Reduction type for the loss. See {@link LossReduce} for more details. Default: {@link LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT}
+   * @param lossReduce Reduction type for the loss. See LossReduce for more details. Default: LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT
    * @param epsilon epsilon
    * @return output Log loss  (NUMERIC type)
    */
@@ -445,7 +488,7 @@ public class SDLoss extends SDOps {
    * @param label Label array (NUMERIC type)
    * @param predictions Predictions array (NUMERIC type)
    * @param weights Weights array. May be null. If null, a weight of 1.0 is used (NUMERIC type)
-   * @param lossReduce Reduction type for the loss. See {@link LossReduce} for more details. Default: {@link LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT}
+   * @param lossReduce Reduction type for the loss. See LossReduce for more details. Default: LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT
    * @param epsilon epsilon
    * @return output Log loss  (NUMERIC type)
    */
@@ -499,7 +542,7 @@ public class SDLoss extends SDOps {
    * @param label Label array. Each value should be 0.0 or 1.0 (NUMERIC type)
    * @param predictions Predictions array (has to be log(x) of actual predictions) (NUMERIC type)
    * @param weights Weights array. May be null. If null, a weight of 1.0 is used (NUMERIC type)
-   * @param lossReduce Reduction type for the loss. See {@link LossReduce} for more details. Default: {@link LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT}
+   * @param lossReduce Reduction type for the loss. See LossReduce for more details. Default: LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT
    * @param full Boolean flag. true for logPoissonFull, false for logPoisson
    * @return output Loss variable (NUMERIC type)
    */
@@ -521,7 +564,7 @@ public class SDLoss extends SDOps {
    * @param label Label array. Each value should be 0.0 or 1.0 (NUMERIC type)
    * @param predictions Predictions array (has to be log(x) of actual predictions) (NUMERIC type)
    * @param weights Weights array. May be null. If null, a weight of 1.0 is used (NUMERIC type)
-   * @param lossReduce Reduction type for the loss. See {@link LossReduce} for more details. Default: {@link LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT}
+   * @param lossReduce Reduction type for the loss. See LossReduce for more details. Default: LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT
    * @param full Boolean flag. true for logPoissonFull, false for logPoisson
    * @return output Loss variable (NUMERIC type)
    */
@@ -585,7 +628,7 @@ public class SDLoss extends SDOps {
    * @param label Label array (NUMERIC type)
    * @param predictions Predictions array (NUMERIC type)
    * @param weights Weights array. May be null. If null, a weight of 1.0 is used. Must be either null, scalar, or have shape [batchSize] (NUMERIC type)
-   * @param lossReduce Reduction type for the loss. See {@link LossReduce} for more details. Default: {@link LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT}
+   * @param lossReduce Reduction type for the loss. See LossReduce for more details. Default: LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT
    * @return output Loss variable, scalar output (NUMERIC type)
    */
   public SDVariable meanPairwiseSquaredError(SDVariable label, SDVariable predictions,
@@ -608,7 +651,7 @@ public class SDLoss extends SDOps {
    * @param label Label array (NUMERIC type)
    * @param predictions Predictions array (NUMERIC type)
    * @param weights Weights array. May be null. If null, a weight of 1.0 is used. Must be either null, scalar, or have shape [batchSize] (NUMERIC type)
-   * @param lossReduce Reduction type for the loss. See {@link LossReduce} for more details. Default: {@link LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT}
+   * @param lossReduce Reduction type for the loss. See LossReduce for more details. Default: LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT
    * @return output Loss variable, scalar output (NUMERIC type)
    */
   public SDVariable meanPairwiseSquaredError(String name, SDVariable label, SDVariable predictions,
@@ -666,13 +709,13 @@ public class SDLoss extends SDOps {
 
   /**
    * Mean squared error loss function. Implements {@code (label[i] - prediction[i])^2} - i.e., squared error on a per-element basis.<br>
-   * When averaged (using {@link LossReduce#MEAN_BY_WEIGHT} or {@link LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT} (the default))<br>
+   * When averaged (using LossReduce#MEAN_BY_WEIGHT or LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT (the default))<br>
    * this is the mean squared error loss function.<br>
    *
    * @param label Label array (NUMERIC type)
    * @param predictions Predictions array (NUMERIC type)
    * @param weights Weights array. May be null. If null, a weight of 1.0 is used (NUMERIC type)
-   * @param lossReduce Reduction type for the loss. See {@link LossReduce} for more details. Default: {@link LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT}
+   * @param lossReduce Reduction type for the loss. See LossReduce for more details. Default: LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT
    * @return output Loss variable (NUMERIC type)
    */
   public SDVariable meanSquaredError(SDVariable label, SDVariable predictions, SDVariable weights,
@@ -687,14 +730,14 @@ public class SDLoss extends SDOps {
 
   /**
    * Mean squared error loss function. Implements {@code (label[i] - prediction[i])^2} - i.e., squared error on a per-element basis.<br>
-   * When averaged (using {@link LossReduce#MEAN_BY_WEIGHT} or {@link LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT} (the default))<br>
+   * When averaged (using LossReduce#MEAN_BY_WEIGHT or LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT (the default))<br>
    * this is the mean squared error loss function.<br>
    *
    * @param name name May be null. Name for the output variable
    * @param label Label array (NUMERIC type)
    * @param predictions Predictions array (NUMERIC type)
    * @param weights Weights array. May be null. If null, a weight of 1.0 is used (NUMERIC type)
-   * @param lossReduce Reduction type for the loss. See {@link LossReduce} for more details. Default: {@link LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT}
+   * @param lossReduce Reduction type for the loss. See LossReduce for more details. Default: LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT
    * @return output Loss variable (NUMERIC type)
    */
   public SDVariable meanSquaredError(String name, SDVariable label, SDVariable predictions,
@@ -709,7 +752,7 @@ public class SDLoss extends SDOps {
 
   /**
    * Mean squared error loss function. Implements {@code (label[i] - prediction[i])^2} - i.e., squared error on a per-element basis.<br>
-   * When averaged (using {@link LossReduce#MEAN_BY_WEIGHT} or {@link LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT} (the default))<br>
+   * When averaged (using LossReduce#MEAN_BY_WEIGHT or LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT (the default))<br>
    * this is the mean squared error loss function.<br>
    *
    * @param label Label array (NUMERIC type)
@@ -728,7 +771,7 @@ public class SDLoss extends SDOps {
 
   /**
    * Mean squared error loss function. Implements {@code (label[i] - prediction[i])^2} - i.e., squared error on a per-element basis.<br>
-   * When averaged (using {@link LossReduce#MEAN_BY_WEIGHT} or {@link LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT} (the default))<br>
+   * When averaged (using LossReduce#MEAN_BY_WEIGHT or LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT (the default))<br>
    * this is the mean squared error loss function.<br>
    *
    * @param name name May be null. Name for the output variable
@@ -764,7 +807,7 @@ public class SDLoss extends SDOps {
    * @param label Label array (NUMERIC type)
    * @param predictionLogits Predictions array (NUMERIC type)
    * @param weights Weights array. May be null. If null, a weight of 1.0 is used (NUMERIC type)
-   * @param lossReduce Reduction type for the loss. See {@link LossReduce} for more details. Default: {@link LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT}
+   * @param lossReduce Reduction type for the loss. See LossReduce for more details. Default: LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT
    * @param labelSmoothing Label smoothing value. Default value: 0
    * @return output Loss variable (NUMERIC type)
    */
@@ -796,7 +839,7 @@ public class SDLoss extends SDOps {
    * @param label Label array (NUMERIC type)
    * @param predictionLogits Predictions array (NUMERIC type)
    * @param weights Weights array. May be null. If null, a weight of 1.0 is used (NUMERIC type)
-   * @param lossReduce Reduction type for the loss. See {@link LossReduce} for more details. Default: {@link LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT}
+   * @param lossReduce Reduction type for the loss. See LossReduce for more details. Default: LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT
    * @param labelSmoothing Label smoothing value. Default value: 0
    * @return output Loss variable (NUMERIC type)
    */
@@ -872,7 +915,7 @@ public class SDLoss extends SDOps {
   /**
    * Applies the softmax activation function to the input, then implement multi-class cross entropy:<br>
    * {@code -sum_classes label[i] * log(p[c])} where {@code p = softmax(logits)}<br>
-   * If {@link LossReduce#NONE} is used, returned shape is [numExamples] out for [numExamples, numClasses] predicitons/labels;<br>
+   * If LossReduce#NONE is used, returned shape is [numExamples] out for [numExamples, numClasses] predicitons/labels;<br>
    * otherwise, the output is a scalar.<br>
    * <p><br>
    * When label smoothing is > 0, the following label smoothing is used:<br>
@@ -884,7 +927,7 @@ public class SDLoss extends SDOps {
    * @param oneHotLabels Label array. Should be one-hot per example and same shape as predictions (for example, [mb, nOut]) (NUMERIC type)
    * @param logitPredictions Predictions array (pre-softmax) (NUMERIC type)
    * @param weights Weights array. May be null. If null, a weight of 1.0 is used (NUMERIC type)
-   * @param lossReduce Reduction type for the loss. See {@link LossReduce} for more details. Default: {@link LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT}
+   * @param lossReduce Reduction type for the loss. See LossReduce for more details. Default: LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT
    * @param labelSmoothing Label smoothing value. Default value: 0
    * @return output Loss variable (NUMERIC type)
    */
@@ -901,7 +944,7 @@ public class SDLoss extends SDOps {
   /**
    * Applies the softmax activation function to the input, then implement multi-class cross entropy:<br>
    * {@code -sum_classes label[i] * log(p[c])} where {@code p = softmax(logits)}<br>
-   * If {@link LossReduce#NONE} is used, returned shape is [numExamples] out for [numExamples, numClasses] predicitons/labels;<br>
+   * If LossReduce#NONE is used, returned shape is [numExamples] out for [numExamples, numClasses] predicitons/labels;<br>
    * otherwise, the output is a scalar.<br>
    * <p><br>
    * When label smoothing is > 0, the following label smoothing is used:<br>
@@ -914,7 +957,7 @@ public class SDLoss extends SDOps {
    * @param oneHotLabels Label array. Should be one-hot per example and same shape as predictions (for example, [mb, nOut]) (NUMERIC type)
    * @param logitPredictions Predictions array (pre-softmax) (NUMERIC type)
    * @param weights Weights array. May be null. If null, a weight of 1.0 is used (NUMERIC type)
-   * @param lossReduce Reduction type for the loss. See {@link LossReduce} for more details. Default: {@link LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT}
+   * @param lossReduce Reduction type for the loss. See LossReduce for more details. Default: LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT
    * @param labelSmoothing Label smoothing value. Default value: 0
    * @return output Loss variable (NUMERIC type)
    */
@@ -932,7 +975,7 @@ public class SDLoss extends SDOps {
   /**
    * Applies the softmax activation function to the input, then implement multi-class cross entropy:<br>
    * {@code -sum_classes label[i] * log(p[c])} where {@code p = softmax(logits)}<br>
-   * If {@link LossReduce#NONE} is used, returned shape is [numExamples] out for [numExamples, numClasses] predicitons/labels;<br>
+   * If LossReduce#NONE is used, returned shape is [numExamples] out for [numExamples, numClasses] predicitons/labels;<br>
    * otherwise, the output is a scalar.<br>
    * <p><br>
    * When label smoothing is > 0, the following label smoothing is used:<br>
@@ -959,7 +1002,7 @@ public class SDLoss extends SDOps {
   /**
    * Applies the softmax activation function to the input, then implement multi-class cross entropy:<br>
    * {@code -sum_classes label[i] * log(p[c])} where {@code p = softmax(logits)}<br>
-   * If {@link LossReduce#NONE} is used, returned shape is [numExamples] out for [numExamples, numClasses] predicitons/labels;<br>
+   * If LossReduce#NONE is used, returned shape is [numExamples] out for [numExamples, numClasses] predicitons/labels;<br>
    * otherwise, the output is a scalar.<br>
    * <p><br>
    * When label smoothing is > 0, the following label smoothing is used:<br>

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/samediff/ops/SDMath.java

@@ -1,25 +1,25 @@
-/*
+/*******************************************************************************
- *  ******************************************************************************
+ * Copyright (c) 2019-2020 Konduit K.K.
- *  *
+ *
- *  *
+ * This program and the accompanying materials are made available under the
- *  * This program and the accompanying materials are made available under the
+ * terms of the Apache License, Version 2.0 which is available at
- *  * terms of the Apache License, Version 2.0 which is available at
+ * https://www.apache.org/licenses/LICENSE-2.0.
- *  * https://www.apache.org/licenses/LICENSE-2.0.
+ *
- *  *
+ * Unless required by applicable law or agreed to in writing, software
- *  *  See the NOTICE file distributed with this work for additional
+ * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
- *  *  information regarding copyright ownership.
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
- *  * Unless required by applicable law or agreed to in writing, software
+ * License for the specific language governing permissions and limitations
- *  * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+ * under the License.
- *  * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+ *
- *  * License for the specific language governing permissions and limitations
+ * SPDX-License-Identifier: Apache-2.0
- *  * under the License.
+ ******************************************************************************/
- *  *
+
- *  * SPDX-License-Identifier: Apache-2.0
+//================== GENERATED CODE - DO NOT MODIFY THIS FILE ==================
- *  *****************************************************************************
- */
 
 package org.nd4j.autodiff.samediff.ops;
 
+import static org.nd4j.autodiff.samediff.ops.SDValidation.isSameType;
+
 import java.lang.String;
 import org.nd4j.autodiff.samediff.SDVariable;
 import org.nd4j.autodiff.samediff.SameDiff;

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/samediff/ops/SDNN.java

@@ -1,25 +1,25 @@
-/*
+/*******************************************************************************
- *  ******************************************************************************
+ * Copyright (c) 2019-2020 Konduit K.K.
- *  *
+ *
- *  *
+ * This program and the accompanying materials are made available under the
- *  * This program and the accompanying materials are made available under the
+ * terms of the Apache License, Version 2.0 which is available at
- *  * terms of the Apache License, Version 2.0 which is available at
+ * https://www.apache.org/licenses/LICENSE-2.0.
- *  * https://www.apache.org/licenses/LICENSE-2.0.
+ *
- *  *
+ * Unless required by applicable law or agreed to in writing, software
- *  *  See the NOTICE file distributed with this work for additional
+ * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
- *  *  information regarding copyright ownership.
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
- *  * Unless required by applicable law or agreed to in writing, software
+ * License for the specific language governing permissions and limitations
- *  * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+ * under the License.
- *  * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+ *
- *  * License for the specific language governing permissions and limitations
+ * SPDX-License-Identifier: Apache-2.0
- *  * under the License.
+ ******************************************************************************/
- *  *
+
- *  * SPDX-License-Identifier: Apache-2.0
+//================== GENERATED CODE - DO NOT MODIFY THIS FILE ==================
- *  *****************************************************************************
- */
 
 package org.nd4j.autodiff.samediff.ops;
 
+import static org.nd4j.autodiff.samediff.ops.SDValidation.isSameType;
+
 import java.lang.String;
 import org.nd4j.autodiff.samediff.SDVariable;
 import org.nd4j.autodiff.samediff.SameDiff;

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/samediff/ops/SDRNN.java

@@ -1,25 +1,25 @@
-/*
+/*******************************************************************************
- *  ******************************************************************************
+ * Copyright (c) 2019-2020 Konduit K.K.
- *  *
+ *
- *  *
+ * This program and the accompanying materials are made available under the
- *  * This program and the accompanying materials are made available under the
+ * terms of the Apache License, Version 2.0 which is available at
- *  * terms of the Apache License, Version 2.0 which is available at
+ * https://www.apache.org/licenses/LICENSE-2.0.
- *  * https://www.apache.org/licenses/LICENSE-2.0.
+ *
- *  *
+ * Unless required by applicable law or agreed to in writing, software
- *  *  See the NOTICE file distributed with this work for additional
+ * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
- *  *  information regarding copyright ownership.
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
- *  * Unless required by applicable law or agreed to in writing, software
+ * License for the specific language governing permissions and limitations
- *  * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+ * under the License.
- *  * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+ *
- *  * License for the specific language governing permissions and limitations
+ * SPDX-License-Identifier: Apache-2.0
- *  * under the License.
+ ******************************************************************************/
- *  *
+
- *  * SPDX-License-Identifier: Apache-2.0
+//================== GENERATED CODE - DO NOT MODIFY THIS FILE ==================
- *  *****************************************************************************
- */
 
 package org.nd4j.autodiff.samediff.ops;
 
+import static org.nd4j.autodiff.samediff.ops.SDValidation.isSameType;
+
 import java.lang.String;
 import org.nd4j.autodiff.samediff.SDVariable;
 import org.nd4j.autodiff.samediff.SameDiff;
@@ -144,22 +144,22 @@ public class SDRNN extends SDOps {
 
   /**
    * Long Short-Term Memory layer - Hochreiter 1997.<br>
-   * SUPPORTS following data formats:\n<br>
+   * SUPPORTS following data formats:<br>
-   * for unidirectional: \n" +<br>
+   * for unidirectional:<br>
-   * TNS: shapes [timeLength, numExamples, inOutSize]\n<br>
+   * TNS: shapes [timeLength, numExamples, inOutSize]<br>
-   * NST: shapes [numExamples, inOutSize, timeLength]\n<br>
+   * NST: shapes [numExamples, inOutSize, timeLength]<br>
    * NTS: shapes [numExamples, timeLength, inOutSize]<br>
-   * for bidirectional:\n<br>
+   * for bidirectional:<br>
-   * T2NS: shapes [timeLength, 2, numExamples, inOutSize] (for ONNX)\n<br>
+   * T2NS: shapes [timeLength, 2, numExamples, inOutSize] (for ONNX)<br>
-   * SUPPORTS following direction modes:\n<br>
+   * SUPPORTS following direction modes:<br>
    * FWD: forward<br>
    * BWD: backward<br>
-   * BIDIR_SUM: bidirectional sum\n<br>
+   * BIDIR_SUM: bidirectional sum<br>
-   * BIDIR_CONCAT: bidirectional concat\n" +<br>
+   * BIDIR_CONCAT: bidirectional concat<br>
-   * BIDIR_EXTRA_DIM: bidirectional extra output dim (in conjunction with format dataFormat - T2NS)"<br>
+   * BIDIR_EXTRA_DIM: bidirectional extra output dim (in conjunction with format dataFormat - T2NS)<br>
    * You may use different gate configurations:<br>
-   * specify gate/cell/out aplha/beta and numbers of activations for gate/cell/out described in activations enum\n<br>
+   * specify gate/cell/out aplha/beta and numbers of activations for gate/cell/out described in activations enum<br>
-   * ("RELU","SIGMOID","AFFINE","LEAKY_RELU","THRESHHOLD_RELU","SCALED_TAHN","HARD_SIGMOID","ELU","SOFTSIGN","SOFTPLUS")\n<br>
+   * ("RELU","SIGMOID","AFFINE","LEAKY_RELU","THRESHHOLD_RELU","SCALED_TAHN","HARD_SIGMOID","ELU","SOFTSIGN","SOFTPLUS")<br>
    * Also this layer supports MKLDNN (DNNL) and cuDNN acceleration<br>
    *
    * @param x  Input, with shape dependent on the data format (in config). (NUMERIC type)
@@ -180,22 +180,22 @@ public class SDRNN extends SDOps {
 
   /**
    * Long Short-Term Memory layer - Hochreiter 1997.<br>
-   * SUPPORTS following data formats:\n<br>
+   * SUPPORTS following data formats:<br>
-   * for unidirectional: \n" +<br>
+   * for unidirectional:<br>
-   * TNS: shapes [timeLength, numExamples, inOutSize]\n<br>
+   * TNS: shapes [timeLength, numExamples, inOutSize]<br>
-   * NST: shapes [numExamples, inOutSize, timeLength]\n<br>
+   * NST: shapes [numExamples, inOutSize, timeLength]<br>
    * NTS: shapes [numExamples, timeLength, inOutSize]<br>
-   * for bidirectional:\n<br>
+   * for bidirectional:<br>
-   * T2NS: shapes [timeLength, 2, numExamples, inOutSize] (for ONNX)\n<br>
+   * T2NS: shapes [timeLength, 2, numExamples, inOutSize] (for ONNX)<br>
-   * SUPPORTS following direction modes:\n<br>
+   * SUPPORTS following direction modes:<br>
    * FWD: forward<br>
    * BWD: backward<br>
-   * BIDIR_SUM: bidirectional sum\n<br>
+   * BIDIR_SUM: bidirectional sum<br>
-   * BIDIR_CONCAT: bidirectional concat\n" +<br>
+   * BIDIR_CONCAT: bidirectional concat<br>
-   * BIDIR_EXTRA_DIM: bidirectional extra output dim (in conjunction with format dataFormat - T2NS)"<br>
+   * BIDIR_EXTRA_DIM: bidirectional extra output dim (in conjunction with format dataFormat - T2NS)<br>
    * You may use different gate configurations:<br>
-   * specify gate/cell/out aplha/beta and numbers of activations for gate/cell/out described in activations enum\n<br>
+   * specify gate/cell/out aplha/beta and numbers of activations for gate/cell/out described in activations enum<br>
-   * ("RELU","SIGMOID","AFFINE","LEAKY_RELU","THRESHHOLD_RELU","SCALED_TAHN","HARD_SIGMOID","ELU","SOFTSIGN","SOFTPLUS")\n<br>
+   * ("RELU","SIGMOID","AFFINE","LEAKY_RELU","THRESHHOLD_RELU","SCALED_TAHN","HARD_SIGMOID","ELU","SOFTSIGN","SOFTPLUS")<br>
    * Also this layer supports MKLDNN (DNNL) and cuDNN acceleration<br>
    *
    * @param names names May be null. Arrays of names for the output variables.
@@ -218,22 +218,22 @@ public class SDRNN extends SDOps {
 
   /**
    * Long Short-Term Memory layer - Hochreiter 1997.<br>
-   * SUPPORTS following data formats:\n<br>
+   * SUPPORTS following data formats:<br>
-   * for unidirectional: \n" +<br>
+   * for unidirectional:<br>
-   * TNS: shapes [timeLength, numExamples, inOutSize]\n<br>
+   * TNS: shapes [timeLength, numExamples, inOutSize]<br>
-   * NST: shapes [numExamples, inOutSize, timeLength]\n<br>
+   * NST: shapes [numExamples, inOutSize, timeLength]<br>
    * NTS: shapes [numExamples, timeLength, inOutSize]<br>
-   * for bidirectional:\n<br>
+   * for bidirectional:<br>
-   * T2NS: shapes [timeLength, 2, numExamples, inOutSize] (for ONNX)\n<br>
+   * T2NS: shapes [timeLength, 2, numExamples, inOutSize] (for ONNX)<br>
-   * SUPPORTS following direction modes:\n<br>
+   * SUPPORTS following direction modes:<br>
    * FWD: forward<br>
    * BWD: backward<br>
-   * BIDIR_SUM: bidirectional sum\n<br>
+   * BIDIR_SUM: bidirectional sum<br>
-   * BIDIR_CONCAT: bidirectional concat\n" +<br>
+   * BIDIR_CONCAT: bidirectional concat<br>
-   * BIDIR_EXTRA_DIM: bidirectional extra output dim (in conjunction with format dataFormat - T2NS)"<br>
+   * BIDIR_EXTRA_DIM: bidirectional extra output dim (in conjunction with format dataFormat - T2NS)<br>
    * You may use different gate configurations:<br>
-   * specify gate/cell/out aplha/beta and numbers of activations for gate/cell/out described in activations enum\n<br>
+   * specify gate/cell/out aplha/beta and numbers of activations for gate/cell/out described in activations enum<br>
-   * ("RELU","SIGMOID","AFFINE","LEAKY_RELU","THRESHHOLD_RELU","SCALED_TAHN","HARD_SIGMOID","ELU","SOFTSIGN","SOFTPLUS")\n<br>
+   * ("RELU","SIGMOID","AFFINE","LEAKY_RELU","THRESHHOLD_RELU","SCALED_TAHN","HARD_SIGMOID","ELU","SOFTSIGN","SOFTPLUS")<br>
    * Also this layer supports MKLDNN (DNNL) and cuDNN acceleration<br>
    *
    * @param x  Input, with shape dependent on the data format (in config). (NUMERIC type)
@@ -248,22 +248,22 @@ public class SDRNN extends SDOps {
 
   /**
    * Long Short-Term Memory layer - Hochreiter 1997.<br>
-   * SUPPORTS following data formats:\n<br>
+   * SUPPORTS following data formats:<br>
-   * for unidirectional: \n" +<br>
+   * for unidirectional:<br>
-   * TNS: shapes [timeLength, numExamples, inOutSize]\n<br>
+   * TNS: shapes [timeLength, numExamples, inOutSize]<br>
-   * NST: shapes [numExamples, inOutSize, timeLength]\n<br>
+   * NST: shapes [numExamples, inOutSize, timeLength]<br>
    * NTS: shapes [numExamples, timeLength, inOutSize]<br>
-   * for bidirectional:\n<br>
+   * for bidirectional:<br>
-   * T2NS: shapes [timeLength, 2, numExamples, inOutSize] (for ONNX)\n<br>
+   * T2NS: shapes [timeLength, 2, numExamples, inOutSize] (for ONNX)<br>
-   * SUPPORTS following direction modes:\n<br>
+   * SUPPORTS following direction modes:<br>
    * FWD: forward<br>
    * BWD: backward<br>
-   * BIDIR_SUM: bidirectional sum\n<br>
+   * BIDIR_SUM: bidirectional sum<br>
-   * BIDIR_CONCAT: bidirectional concat\n" +<br>
+   * BIDIR_CONCAT: bidirectional concat<br>
-   * BIDIR_EXTRA_DIM: bidirectional extra output dim (in conjunction with format dataFormat - T2NS)"<br>
+   * BIDIR_EXTRA_DIM: bidirectional extra output dim (in conjunction with format dataFormat - T2NS)<br>
    * You may use different gate configurations:<br>
-   * specify gate/cell/out aplha/beta and numbers of activations for gate/cell/out described in activations enum\n<br>
+   * specify gate/cell/out aplha/beta and numbers of activations for gate/cell/out described in activations enum<br>
-   * ("RELU","SIGMOID","AFFINE","LEAKY_RELU","THRESHHOLD_RELU","SCALED_TAHN","HARD_SIGMOID","ELU","SOFTSIGN","SOFTPLUS")\n<br>
+   * ("RELU","SIGMOID","AFFINE","LEAKY_RELU","THRESHHOLD_RELU","SCALED_TAHN","HARD_SIGMOID","ELU","SOFTSIGN","SOFTPLUS")<br>
    * Also this layer supports MKLDNN (DNNL) and cuDNN acceleration<br>
    *
    * @param names names May be null. Arrays of names for the output variables.

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/samediff/ops/SDRandom.java

@@ -1,25 +1,25 @@
-/*
+/*******************************************************************************
- *  ******************************************************************************
+ * Copyright (c) 2019-2020 Konduit K.K.
- *  *
+ *
- *  *
+ * This program and the accompanying materials are made available under the
- *  * This program and the accompanying materials are made available under the
+ * terms of the Apache License, Version 2.0 which is available at
- *  * terms of the Apache License, Version 2.0 which is available at
+ * https://www.apache.org/licenses/LICENSE-2.0.
- *  * https://www.apache.org/licenses/LICENSE-2.0.
+ *
- *  *
+ * Unless required by applicable law or agreed to in writing, software
- *  *  See the NOTICE file distributed with this work for additional
+ * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
- *  *  information regarding copyright ownership.
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
- *  * Unless required by applicable law or agreed to in writing, software
+ * License for the specific language governing permissions and limitations
- *  * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+ * under the License.
- *  * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+ *
- *  * License for the specific language governing permissions and limitations
+ * SPDX-License-Identifier: Apache-2.0
- *  * under the License.
+ ******************************************************************************/
- *  *
+
- *  * SPDX-License-Identifier: Apache-2.0
+//================== GENERATED CODE - DO NOT MODIFY THIS FILE ==================
- *  *****************************************************************************
- */
 
 package org.nd4j.autodiff.samediff.ops;
 
+import static org.nd4j.autodiff.samediff.ops.SDValidation.isSameType;
+
 import java.lang.String;
 import org.nd4j.autodiff.samediff.SDVariable;
 import org.nd4j.autodiff.samediff.SameDiff;

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/enums/CellAct.java

@@ -1,25 +1,25 @@
-/*
+/*******************************************************************************
- *  ******************************************************************************
+ * Copyright (c) 2019-2020 Konduit K.K.
- *  *
+ *
- *  *
+ * This program and the accompanying materials are made available under the
- *  * This program and the accompanying materials are made available under the
+ * terms of the Apache License, Version 2.0 which is available at
- *  * terms of the Apache License, Version 2.0 which is available at
+ * https://www.apache.org/licenses/LICENSE-2.0.
- *  * https://www.apache.org/licenses/LICENSE-2.0.
+ *
- *  *
+ * Unless required by applicable law or agreed to in writing, software
- *  *  See the NOTICE file distributed with this work for additional
+ * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
- *  *  information regarding copyright ownership.
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
- *  * Unless required by applicable law or agreed to in writing, software
+ * License for the specific language governing permissions and limitations
- *  * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+ * under the License.
- *  * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+ *
- *  * License for the specific language governing permissions and limitations
+ * SPDX-License-Identifier: Apache-2.0
- *  * under the License.
+ ******************************************************************************/
- *  *
+
- *  * SPDX-License-Identifier: Apache-2.0
+//================== GENERATED CODE - DO NOT MODIFY THIS FILE ==================
- *  *****************************************************************************
- */
 
 package org.nd4j.enums;
 
+/**
+ * Activations */
 public enum CellAct {
   TANH,
 

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/enums/DataFormat.java

@@ -1,25 +1,25 @@
-/*
+/*******************************************************************************
- *  ******************************************************************************
+ * Copyright (c) 2019-2020 Konduit K.K.
- *  *
+ *
- *  *
+ * This program and the accompanying materials are made available under the
- *  * This program and the accompanying materials are made available under the
+ * terms of the Apache License, Version 2.0 which is available at
- *  * terms of the Apache License, Version 2.0 which is available at
+ * https://www.apache.org/licenses/LICENSE-2.0.
- *  * https://www.apache.org/licenses/LICENSE-2.0.
+ *
- *  *
+ * Unless required by applicable law or agreed to in writing, software
- *  *  See the NOTICE file distributed with this work for additional
+ * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
- *  *  information regarding copyright ownership.
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
- *  * Unless required by applicable law or agreed to in writing, software
+ * License for the specific language governing permissions and limitations
- *  * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+ * under the License.
- *  * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+ *
- *  * License for the specific language governing permissions and limitations
+ * SPDX-License-Identifier: Apache-2.0
- *  * under the License.
+ ******************************************************************************/
- *  *
+
- *  * SPDX-License-Identifier: Apache-2.0
+//================== GENERATED CODE - DO NOT MODIFY THIS FILE ==================
- *  *****************************************************************************
- */
 
 package org.nd4j.enums;
 
+/**
+ * Data format: "NCHW" or "NHWC" */
 public enum DataFormat {
   NCHW,
 

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/enums/GateAct.java

@@ -1,25 +1,25 @@
-/*
+/*******************************************************************************
- *  ******************************************************************************
+ * Copyright (c) 2019-2020 Konduit K.K.
- *  *
+ *
- *  *
+ * This program and the accompanying materials are made available under the
- *  * This program and the accompanying materials are made available under the
+ * terms of the Apache License, Version 2.0 which is available at
- *  * terms of the Apache License, Version 2.0 which is available at
+ * https://www.apache.org/licenses/LICENSE-2.0.
- *  * https://www.apache.org/licenses/LICENSE-2.0.
+ *
- *  *
+ * Unless required by applicable law or agreed to in writing, software
- *  *  See the NOTICE file distributed with this work for additional
+ * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
- *  *  information regarding copyright ownership.
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
- *  * Unless required by applicable law or agreed to in writing, software
+ * License for the specific language governing permissions and limitations
- *  * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+ * under the License.
- *  * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+ *
- *  * License for the specific language governing permissions and limitations
+ * SPDX-License-Identifier: Apache-2.0
- *  * under the License.
+ ******************************************************************************/
- *  *
+
- *  * SPDX-License-Identifier: Apache-2.0
+//================== GENERATED CODE - DO NOT MODIFY THIS FILE ==================
- *  *****************************************************************************
- */
 
 package org.nd4j.enums;
 
+/**
+ * Activations */
 public enum GateAct {
   TANH,
 

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/enums/ImageResizeMethod.java

@@ -1,32 +1,43 @@
-/*
+/*******************************************************************************
- *  ******************************************************************************
+ * Copyright (c) 2019-2020 Konduit K.K.
- *  *
+ *
- *  *
+ * This program and the accompanying materials are made available under the
- *  * This program and the accompanying materials are made available under the
+ * terms of the Apache License, Version 2.0 which is available at
- *  * terms of the Apache License, Version 2.0 which is available at
+ * https://www.apache.org/licenses/LICENSE-2.0.
- *  * https://www.apache.org/licenses/LICENSE-2.0.
+ *
- *  *
+ * Unless required by applicable law or agreed to in writing, software
- *  *  See the NOTICE file distributed with this work for additional
+ * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
- *  *  information regarding copyright ownership.
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
- *  * Unless required by applicable law or agreed to in writing, software
+ * License for the specific language governing permissions and limitations
- *  * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+ * under the License.
- *  * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+ *
- *  * License for the specific language governing permissions and limitations
+ * SPDX-License-Identifier: Apache-2.0
- *  * under the License.
+ ******************************************************************************/
- *  *
+
- *  * SPDX-License-Identifier: Apache-2.0
+//================== GENERATED CODE - DO NOT MODIFY THIS FILE ==================
- *  *****************************************************************************
- */
 
 package org.nd4j.enums;
 
+/**
+ * ResizeBilinear: Bilinear interpolation. If 'antialias' is true, becomes a hat/tent filter function with radius 1 when downsampling.
+ * ResizeLanczos5: Lanczos kernel with radius 5. Very-high-quality filter but may have stronger ringing.
+ * ResizeBicubic: Cubic interpolant of Keys. Equivalent to Catmull-Rom kernel. Reasonably good quality and faster than Lanczos3Kernel, particularly when upsampling.
+ * ResizeGaussian: Gaussian kernel with radius 3, sigma = 1.5 / 3.0.
+ * ResizeNearest: Nearest neighbor interpolation. 'antialias' has no effect when used with nearest neighbor interpolation.
+ * ResizeArea: Anti-aliased resampling with area interpolation. 'antialias' has no effect when used with area interpolation; it always anti-aliases.
+ * ResizeMitchelcubic: Mitchell-Netravali Cubic non-interpolating filter. For synthetic images (especially those lacking proper prefiltering), less ringing than Keys cubic kernel but less sharp. */
 public enum ImageResizeMethod {
-  ResizeBilinear, // as java require
+  ResizeBilinear,
-  ResizeNearest,
+
   ResizeBicubic,
-  ResizeArea,
+
+  ResizeNearest,
+
   ResizeGaussian,
-  ResizeLanczos3,
+
   ResizeLanczos5,
-  ResizeMitchellcubic;
+
+  ResizeMitchelcubic,
+
+  ResizeArea
 }

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/enums/LSTMDataFormat.java

@@ -1,25 +1,28 @@
-/*
+/*******************************************************************************
- *  ******************************************************************************
+ * Copyright (c) 2019-2020 Konduit K.K.
- *  *
+ *
- *  *
+ * This program and the accompanying materials are made available under the
- *  * This program and the accompanying materials are made available under the
+ * terms of the Apache License, Version 2.0 which is available at
- *  * terms of the Apache License, Version 2.0 which is available at
+ * https://www.apache.org/licenses/LICENSE-2.0.
- *  * https://www.apache.org/licenses/LICENSE-2.0.
+ *
- *  *
+ * Unless required by applicable law or agreed to in writing, software
- *  *  See the NOTICE file distributed with this work for additional
+ * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
- *  *  information regarding copyright ownership.
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
- *  * Unless required by applicable law or agreed to in writing, software
+ * License for the specific language governing permissions and limitations
- *  * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+ * under the License.
- *  * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+ *
- *  * License for the specific language governing permissions and limitations
+ * SPDX-License-Identifier: Apache-2.0
- *  * under the License.
+ ******************************************************************************/
- *  *
+
- *  * SPDX-License-Identifier: Apache-2.0
+//================== GENERATED CODE - DO NOT MODIFY THIS FILE ==================
- *  *****************************************************************************
- */
 
 package org.nd4j.enums;
 
+/**
+ * for unidirectional:  TNS: shape [timeLength, numExamples, inOutSize] - sometimes referred to as "time major"<br>
+ *   NST: shape [numExamples, inOutSize, timeLength]<br>
+ *   NTS: shape [numExamples, timeLength, inOutSize] - TF "time_major=false" layout<br> for bidirectional:
+ *    T2NS: 3 = [timeLength, 2, numExamples, inOutSize] (for ONNX) */
 public enum LSTMDataFormat {
   TNS,
 

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/enums/LSTMDirectionMode.java

@@ -1,25 +1,30 @@
-/*
+/*******************************************************************************
- *  ******************************************************************************
+ * Copyright (c) 2019-2020 Konduit K.K.
- *  *
+ *
- *  *
+ * This program and the accompanying materials are made available under the
- *  * This program and the accompanying materials are made available under the
+ * terms of the Apache License, Version 2.0 which is available at
- *  * terms of the Apache License, Version 2.0 which is available at
+ * https://www.apache.org/licenses/LICENSE-2.0.
- *  * https://www.apache.org/licenses/LICENSE-2.0.
+ *
- *  *
+ * Unless required by applicable law or agreed to in writing, software
- *  *  See the NOTICE file distributed with this work for additional
+ * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
- *  *  information regarding copyright ownership.
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
- *  * Unless required by applicable law or agreed to in writing, software
+ * License for the specific language governing permissions and limitations
- *  * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+ * under the License.
- *  * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+ *
- *  * License for the specific language governing permissions and limitations
+ * SPDX-License-Identifier: Apache-2.0
- *  * under the License.
+ ******************************************************************************/
- *  *
+
- *  * SPDX-License-Identifier: Apache-2.0
+//================== GENERATED CODE - DO NOT MODIFY THIS FILE ==================
- *  *****************************************************************************
- */
 
 package org.nd4j.enums;
 
+/**
+ * direction <br>
+ *  FWD: 0 = fwd
+ *  BWD: 1 = bwd
+ *  BIDIR_SUM: 2 = bidirectional sum
+ *  BIDIR_CONCAT: 3 = bidirectional concat
+ *  BIDIR_EXTRA_DIM: 4 = bidirectional extra output dim (in conjunction with format dataFormat = 3) */
 public enum LSTMDirectionMode {
   FWD,
 

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/enums/OutAct.java

@@ -1,25 +1,25 @@
-/*
+/*******************************************************************************
- *  ******************************************************************************
+ * Copyright (c) 2019-2020 Konduit K.K.
- *  *
+ *
- *  *
+ * This program and the accompanying materials are made available under the
- *  * This program and the accompanying materials are made available under the
+ * terms of the Apache License, Version 2.0 which is available at
- *  * terms of the Apache License, Version 2.0 which is available at
+ * https://www.apache.org/licenses/LICENSE-2.0.
- *  * https://www.apache.org/licenses/LICENSE-2.0.
+ *
- *  *
+ * Unless required by applicable law or agreed to in writing, software
- *  *  See the NOTICE file distributed with this work for additional
+ * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
- *  *  information regarding copyright ownership.
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
- *  * Unless required by applicable law or agreed to in writing, software
+ * License for the specific language governing permissions and limitations
- *  * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+ * under the License.
- *  * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+ *
- *  * License for the specific language governing permissions and limitations
+ * SPDX-License-Identifier: Apache-2.0
- *  * under the License.
+ ******************************************************************************/
- *  *
+
- *  * SPDX-License-Identifier: Apache-2.0
+//================== GENERATED CODE - DO NOT MODIFY THIS FILE ==================
- *  *****************************************************************************
- */
 
 package org.nd4j.enums;
 
+/**
+ * Activations */
 public enum OutAct {
   TANH,
 

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/enums/PadMode.java

@@ -1,22 +1,20 @@
-/*
+/*******************************************************************************
- *  ******************************************************************************
+ * Copyright (c) 2019-2020 Konduit K.K.
- *  *
+ *
- *  *
+ * This program and the accompanying materials are made available under the
- *  * This program and the accompanying materials are made available under the
+ * terms of the Apache License, Version 2.0 which is available at
- *  * terms of the Apache License, Version 2.0 which is available at
+ * https://www.apache.org/licenses/LICENSE-2.0.
- *  * https://www.apache.org/licenses/LICENSE-2.0.
+ *
- *  *
+ * Unless required by applicable law or agreed to in writing, software
- *  *  See the NOTICE file distributed with this work for additional
+ * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
- *  *  information regarding copyright ownership.
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
- *  * Unless required by applicable law or agreed to in writing, software
+ * License for the specific language governing permissions and limitations
- *  * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+ * under the License.
- *  * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+ *
- *  * License for the specific language governing permissions and limitations
+ * SPDX-License-Identifier: Apache-2.0
- *  * under the License.
+ ******************************************************************************/
- *  *
+
- *  * SPDX-License-Identifier: Apache-2.0
+//================== GENERATED CODE - DO NOT MODIFY THIS FILE ==================
- *  *****************************************************************************
- */
 
 package org.nd4j.enums;
 

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/enums/PartitionMode.java

@@ -1,22 +1,20 @@
-/*
+/*******************************************************************************
- *  ******************************************************************************
+ * Copyright (c) 2019-2020 Konduit K.K.
- *  *
+ *
- *  *
+ * This program and the accompanying materials are made available under the
- *  * This program and the accompanying materials are made available under the
+ * terms of the Apache License, Version 2.0 which is available at
- *  * terms of the Apache License, Version 2.0 which is available at
+ * https://www.apache.org/licenses/LICENSE-2.0.
- *  * https://www.apache.org/licenses/LICENSE-2.0.
+ *
- *  *
+ * Unless required by applicable law or agreed to in writing, software
- *  *  See the NOTICE file distributed with this work for additional
+ * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
- *  *  information regarding copyright ownership.
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
- *  * Unless required by applicable law or agreed to in writing, software
+ * License for the specific language governing permissions and limitations
- *  * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+ * under the License.
- *  * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+ *
- *  * License for the specific language governing permissions and limitations
+ * SPDX-License-Identifier: Apache-2.0
- *  * under the License.
+ ******************************************************************************/
- *  *
+
- *  * SPDX-License-Identifier: Apache-2.0
+//================== GENERATED CODE - DO NOT MODIFY THIS FILE ==================
- *  *****************************************************************************
- */
 
 package org.nd4j.enums;
 

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/enums/RnnDataFormat.java

@@ -1,25 +1,28 @@
-/*
+/*******************************************************************************
- *  ******************************************************************************
+ * Copyright (c) 2019-2020 Konduit K.K.
- *  *
+ *
- *  *
+ * This program and the accompanying materials are made available under the
- *  * This program and the accompanying materials are made available under the
+ * terms of the Apache License, Version 2.0 which is available at
- *  * terms of the Apache License, Version 2.0 which is available at
+ * https://www.apache.org/licenses/LICENSE-2.0.
- *  * https://www.apache.org/licenses/LICENSE-2.0.
+ *
- *  *
+ * Unless required by applicable law or agreed to in writing, software
- *  *  See the NOTICE file distributed with this work for additional
+ * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
- *  *  information regarding copyright ownership.
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
- *  * Unless required by applicable law or agreed to in writing, software
+ * License for the specific language governing permissions and limitations
- *  * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+ * under the License.
- *  * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+ *
- *  * License for the specific language governing permissions and limitations
+ * SPDX-License-Identifier: Apache-2.0
- *  * under the License.
+ ******************************************************************************/
- *  *
+
- *  * SPDX-License-Identifier: Apache-2.0
+//================== GENERATED CODE - DO NOT MODIFY THIS FILE ==================
- *  *****************************************************************************
- */
 
 package org.nd4j.enums;
 
+/**
+ *  The data format of the input. Input shape depends on data format (in config):<br>
+ *  TNS -> [timeSteps, batchSize, inSize]<br>
+ *  NST -> [batchSize, inSize, timeSteps]<br>
+ *  NTS -> [batchSize, timeSteps, inSize]<br> */
 public enum RnnDataFormat {
   TNS,
 

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/loss/CtcLoss.java

@@ -20,29 +20,20 @@
 
 package org.nd4j.linalg.api.ops.impl.loss;
 
-import org.nd4j.autodiff.loss.LossReduce;
 import org.nd4j.autodiff.samediff.SDVariable;
 import org.nd4j.autodiff.samediff.SameDiff;
-import org.nd4j.linalg.api.ndarray.INDArray;
+import org.nd4j.linalg.api.ops.DynamicCustomOp;
-import org.nd4j.linalg.api.ops.impl.loss.bp.AbsoluteDifferenceLossBp;
+import org.nd4j.linalg.api.ops.impl.loss.bp.CtcLossBp;
 
 import java.util.List;
 
-public class CtcLoss extends BaseLoss {
+public class CtcLoss extends DynamicCustomOp {
 
 
-    public CtcLoss(SameDiff sameDiff, LossReduce lossReduce, SDVariable predictions, SDVariable weights, SDVariable labels){
+    public CtcLoss(SameDiff sameDiff, SDVariable targetLabels,SDVariable logitInputs,SDVariable targetLabelLengths,SDVariable logitInputLengths){
-        super(sameDiff, lossReduce, predictions, weights, labels);
+        super(sameDiff,new SDVariable[]{targetLabels,logitInputs,targetLabelLengths,logitInputLengths});
     }
 
-    public CtcLoss(SameDiff sameDiff, SDVariable label, SDVariable predictions, SDVariable weights,
-                   LossReduce lossReduce) {
-        this(sameDiff, lossReduce, predictions, weights, label);
-    }
-
-    public CtcLoss(INDArray labels, INDArray predictions, INDArray weights, LossReduce lossReduce){
-        super(lossReduce, predictions, weights, labels);
-    }
 
     public CtcLoss(){ }
 
@@ -55,6 +46,6 @@ public class CtcLoss extends BaseLoss {
     public List<SDVariable> doDiff(List<SDVariable> grad) {
         //No external gradient
         //Args are: predictions, weights, label
-        return new AbsoluteDifferenceLossBp(sameDiff, lossReduce, arg(0), arg(1), arg(2)).outputs();
+        return new CtcLossBp(sameDiff,  arg(0), arg(1), arg(2),arg(3)).outputs();
     }
 }

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/loss/bp/CtcLossBp.java

@@ -20,17 +20,17 @@
 
 package org.nd4j.linalg.api.ops.impl.loss.bp;
 
-import org.nd4j.autodiff.loss.LossReduce;
 import org.nd4j.autodiff.samediff.SDVariable;
 import org.nd4j.autodiff.samediff.SameDiff;
+import org.nd4j.linalg.api.ops.DynamicCustomOp;
 
 import java.util.List;
 
-public class CtcLossBp extends BaseLossBp {
+public class CtcLossBp extends DynamicCustomOp {
 
 
-    public CtcLossBp(SameDiff sameDiff, LossReduce lossReduce, SDVariable predictions, SDVariable weights, SDVariable labels){
+    public CtcLossBp(SameDiff sameDiff, SDVariable targetLabels,SDVariable logitInputs,SDVariable targetLabelLengths,SDVariable logitInputLengths){
-        super(sameDiff, lossReduce, predictions, weights, labels);
+        super(sameDiff,new SDVariable[]{targetLabels,logitInputs,targetLabelLengths,logitInputLengths});
     }
 
     public CtcLossBp(){ }

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/factory/ops/NDBase.java

@@ -1,22 +1,20 @@
-/*
+/*******************************************************************************
- *  ******************************************************************************
+ * Copyright (c) 2019-2020 Konduit K.K.
- *  *
+ *
- *  *
+ * This program and the accompanying materials are made available under the
- *  * This program and the accompanying materials are made available under the
+ * terms of the Apache License, Version 2.0 which is available at
- *  * terms of the Apache License, Version 2.0 which is available at
+ * https://www.apache.org/licenses/LICENSE-2.0.
- *  * https://www.apache.org/licenses/LICENSE-2.0.
+ *
- *  *
+ * Unless required by applicable law or agreed to in writing, software
- *  *  See the NOTICE file distributed with this work for additional
+ * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
- *  *  information regarding copyright ownership.
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
- *  * Unless required by applicable law or agreed to in writing, software
+ * License for the specific language governing permissions and limitations
- *  * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+ * under the License.
- *  * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+ *
- *  * License for the specific language governing permissions and limitations
+ * SPDX-License-Identifier: Apache-2.0
- *  * under the License.
+ ******************************************************************************/
- *  *
+
- *  * SPDX-License-Identifier: Apache-2.0
+//================== GENERATED CODE - DO NOT MODIFY THIS FILE ==================
- *  *****************************************************************************
- */
 
 package org.nd4j.linalg.factory.ops;
 

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/factory/ops/NDBitwise.java

@@ -1,22 +1,20 @@
-/*
+/*******************************************************************************
- *  ******************************************************************************
+ * Copyright (c) 2019-2020 Konduit K.K.
- *  *
+ *
- *  *
+ * This program and the accompanying materials are made available under the
- *  * This program and the accompanying materials are made available under the
+ * terms of the Apache License, Version 2.0 which is available at
- *  * terms of the Apache License, Version 2.0 which is available at
+ * https://www.apache.org/licenses/LICENSE-2.0.
- *  * https://www.apache.org/licenses/LICENSE-2.0.
+ *
- *  *
+ * Unless required by applicable law or agreed to in writing, software
- *  *  See the NOTICE file distributed with this work for additional
+ * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
- *  *  information regarding copyright ownership.
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
- *  * Unless required by applicable law or agreed to in writing, software
+ * License for the specific language governing permissions and limitations
- *  * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+ * under the License.
- *  * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+ *
- *  * License for the specific language governing permissions and limitations
+ * SPDX-License-Identifier: Apache-2.0
- *  * under the License.
+ ******************************************************************************/
- *  *
+
- *  * SPDX-License-Identifier: Apache-2.0
+//================== GENERATED CODE - DO NOT MODIFY THIS FILE ==================
- *  *****************************************************************************
- */
 
 package org.nd4j.linalg.factory.ops;
 
@@ -134,7 +132,7 @@ public class NDBitwise {
 
   /**
    * Bitwise left cyclical shift operation. Supports broadcasting.<br>
-   * Unlike {@link #leftShift(INDArray, INDArray)} the bits will "wrap around":<br>
+   * Unlike #leftShift(INDArray, INDArray) the bits will "wrap around":<br>
    * {@code leftShiftCyclic(01110000, 2) -> 11000001}<br>
    *
    * @param x Input to be bit shifted (INT type)
@@ -180,7 +178,7 @@ public class NDBitwise {
 
   /**
    * Bitwise right cyclical shift operation. Supports broadcasting.<br>
-   * Unlike {@link #rightShift(INDArray, INDArray)} the bits will "wrap around":<br>
+   * Unlike rightShift(INDArray, INDArray) the bits will "wrap around":<br>
    * {@code rightShiftCyclic(00001110, 2) -> 10000011}<br>
    *
    * @param x Input to be bit shifted (INT type)

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/factory/ops/NDCNN.java

@@ -1,25 +1,25 @@
-/*
+/*******************************************************************************
- *  ******************************************************************************
+ * Copyright (c) 2019-2020 Konduit K.K.
- *  *
+ *
- *  *
+ * This program and the accompanying materials are made available under the
- *  * This program and the accompanying materials are made available under the
+ * terms of the Apache License, Version 2.0 which is available at
- *  * terms of the Apache License, Version 2.0 which is available at
+ * https://www.apache.org/licenses/LICENSE-2.0.
- *  * https://www.apache.org/licenses/LICENSE-2.0.
+ *
- *  *
+ * Unless required by applicable law or agreed to in writing, software
- *  *  See the NOTICE file distributed with this work for additional
+ * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
- *  *  information regarding copyright ownership.
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
- *  * Unless required by applicable law or agreed to in writing, software
+ * License for the specific language governing permissions and limitations
- *  * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+ * under the License.
- *  * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+ *
- *  * License for the specific language governing permissions and limitations
+ * SPDX-License-Identifier: Apache-2.0
- *  * under the License.
+ ******************************************************************************/
- *  *
+
- *  * SPDX-License-Identifier: Apache-2.0
+//================== GENERATED CODE - DO NOT MODIFY THIS FILE ==================
- *  *****************************************************************************
- */
 
 package org.nd4j.linalg.factory.ops;
 
+import static org.nd4j.linalg.factory.NDValidation.isSameType;
+
 import org.nd4j.common.base.Preconditions;
 import org.nd4j.enums.DataFormat;
 import org.nd4j.linalg.api.ndarray.INDArray;
@@ -41,8 +41,7 @@ public class NDCNN {
   /**
    * 2D Convolution layer operation - average pooling 2d<br>
    *
-   * @param input the input to average pooling 2d operation - 4d CNN (image) activations in NCHW format
+   * @param input the input to average pooling 2d operation - 4d CNN (image) activations in NCHW format (shape [minibatch, channels, height, width]) or NHWC format (shape [minibatch, height, width, channels]) (NUMERIC type)
-   *                         (shape [minibatch, channels, height, width]) or NHWC format (shape [minibatch, height, width, channels]) (NUMERIC type)
    * @param Pooling2DConfig Configuration Object
    * @return output Result after applying average pooling on the input (NUMERIC type)
    */
@@ -54,9 +53,7 @@ public class NDCNN {
   /**
    * 3D convolution layer operation - average pooling 3d <br>
    *
-   * @param input the input to average pooling 3d operation - 5d activations in NCDHW format
+   * @param input the input to average pooling 3d operation - 5d activations in NCDHW format (shape [minibatch, channels, depth, height, width]) or NDHWC format (shape [minibatch, depth, height, width, channels]) (NUMERIC type)
-   *                         (shape [minibatch, channels, depth, height, width]) or NDHWC format
-   *                         (shape [minibatch, depth, height, width, channels]) (NUMERIC type)
    * @param Pooling3DConfig Configuration Object
    * @return output after applying average pooling on the input (NUMERIC type)
    */
@@ -161,9 +158,7 @@ public class NDCNN {
   /**
    * Convolution 3D operation with optional bias <br>
    *
-   * @param input the input to average pooling 3d operation - 5d activations in NCDHW format
+   * @param input the input to average pooling 3d operation - 5d activations in NCDHW format (shape [minibatch, channels, depth, height, width]) or NDHWC format (shape [minibatch, depth, height, width, channels]) (NUMERIC type)
-   * (shape [minibatch, channels, depth, height, width]) or NDHWC format
-   * (shape [minibatch, depth, height, width, channels]) (NUMERIC type)
    * @param weights  Weights for conv3d. Rank 5 with shape [kernelDepth, kernelHeight, kernelWidth, inputChannels, outputChannels]. (NUMERIC type)
    * @param bias  Optional 1D bias array with shape [outputChannels]. May be null. (NUMERIC type)
    * @param Conv3DConfig Configuration Object
@@ -180,9 +175,7 @@ public class NDCNN {
   /**
    * Convolution 3D operation with optional bias <br>
    *
-   * @param input the input to average pooling 3d operation - 5d activations in NCDHW format
+   * @param input the input to average pooling 3d operation - 5d activations in NCDHW format (shape [minibatch, channels, depth, height, width]) or NDHWC format (shape [minibatch, depth, height, width, channels]) (NUMERIC type)
-   * (shape [minibatch, channels, depth, height, width]) or NDHWC format
-   * (shape [minibatch, depth, height, width, channels]) (NUMERIC type)
    * @param weights  Weights for conv3d. Rank 5 with shape [kernelDepth, kernelHeight, kernelWidth, inputChannels, outputChannels]. (NUMERIC type)
    * @param Conv3DConfig Configuration Object
    * @return output Conv3d output variable (NUMERIC type)
@@ -196,8 +189,7 @@ public class NDCNN {
   /**
    * 2D deconvolution operation with optional bias<br>
    *
-   * @param layerInput the input to deconvolution 2d operation - 4d CNN (image) activations in NCHW format
+   * @param layerInput the input to deconvolution 2d operation - 4d CNN (image) activations in NCHW format (shape [minibatch, channels, height, width]) or NHWC format (shape [minibatch, height, width, channels]) (NUMERIC type)
-   * (shape [minibatch, channels, height, width]) or NHWC format (shape [minibatch, height, width, channels]) (NUMERIC type)
    * @param weights Weights for the 2d deconvolution operation. 4 dimensions with format [inputChannels, outputChannels, kernelHeight, kernelWidth] (NUMERIC type)
    * @param bias Optional 1D bias array with shape [outputChannels]. May be null. (NUMERIC type)
    * @param DeConv2DConfig Configuration Object
@@ -214,8 +206,7 @@ public class NDCNN {
   /**
    * 2D deconvolution operation with optional bias<br>
    *
-   * @param layerInput the input to deconvolution 2d operation - 4d CNN (image) activations in NCHW format
+   * @param layerInput the input to deconvolution 2d operation - 4d CNN (image) activations in NCHW format (shape [minibatch, channels, height, width]) or NHWC format (shape [minibatch, height, width, channels]) (NUMERIC type)
-   * (shape [minibatch, channels, height, width]) or NHWC format (shape [minibatch, height, width, channels]) (NUMERIC type)
    * @param weights Weights for the 2d deconvolution operation. 4 dimensions with format [inputChannels, outputChannels, kernelHeight, kernelWidth] (NUMERIC type)
    * @param DeConv2DConfig Configuration Object
    * @return output result of deconv2d op (NUMERIC type)
@@ -263,8 +254,7 @@ public class NDCNN {
    * Example: if input has shape [mb, 8, 2, 2] and block size is 2, then output size is [mb, 8/(2*2), 2*2, 2*2]<br>
    * = [mb, 2, 4, 4]<br>
    *
-   * @param x the input to depth to space pooling 2d operation - 4d activations in NCHW format
+   * @param x the input to depth to space pooling 2d operation - 4d activations in NCHW format (shape [minibatch, channels, height, width]) or NHWC format (shape [minibatch, height, width, channels]) (NUMERIC type)
-   *                    (shape [minibatch, channels, height, width]) or NHWC format (shape [minibatch, height, width, channels]) (NUMERIC type)
    * @param blockSize Block size, in the height/width dimension
    * @param dataFormat Data format: "NCHW" or "NHWC"
    * @return output Output variable (NUMERIC type)
@@ -373,8 +363,7 @@ public class NDCNN {
   /**
    * 2D Convolution layer operation - Max pooling on the input and outputs both max values and indices <br>
    *
-   * @param input the input to max pooling 2d operation - 4d CNN (image) activations in NCHW format
+   * @param input the input to max pooling 2d operation - 4d CNN (image) activations in NCHW format (shape [minibatch, channels, height, width]) or NHWC format (shape [minibatch, height, width, channels]) (NUMERIC type)
-   *                         (shape [minibatch, channels, height, width]) or NHWC format (shape [minibatch, height, width, channels]) (NUMERIC type)
    * @param Pooling2DConfig Configuration Object
    */
   public INDArray[] maxPoolWithArgmax(INDArray input, Pooling2DConfig Pooling2DConfig) {
@@ -385,8 +374,7 @@ public class NDCNN {
   /**
    * 2D Convolution layer operation - max pooling 2d <br>
    *
-   * @param input the input to max pooling 2d operation - 4d CNN (image) activations in NCHW format
+   * @param input the input to max pooling 2d operation - 4d CNN (image) activations in NCHW format (shape [minibatch, channels, height, width]) or NHWC format (shape [minibatch, height, width, channels]) (NUMERIC type)
-   *                         (shape [minibatch, channels, height, width]) or NHWC format (shape [minibatch, height, width, channels]) (NUMERIC type)
    * @param Pooling2DConfig Configuration Object
    * @return output Result after applying max pooling on the input (NUMERIC type)
    */
@@ -398,9 +386,7 @@ public class NDCNN {
   /**
    * 3D convolution layer operation - max pooling 3d operation.<br>
    *
-   * @param input the input to average pooling 3d operation - 5d activations in NCDHW format
+   * @param input the input to average pooling 3d operation - 5d activations in NCDHW format (shape [minibatch, channels, depth, height, width]) or NDHWC format (shape [minibatch, depth, height, width, channels]) (NUMERIC type)
-   *                         (shape [minibatch, channels, depth, height, width]) or NDHWC format
-   *                         (shape [minibatch, depth, height, width, channels]) (NUMERIC type)
    * @param Pooling3DConfig Configuration Object
    * @return output Result after applying max pooling on the input (NUMERIC type)
    */
@@ -412,8 +398,7 @@ public class NDCNN {
   /**
    * Separable 2D convolution operation with optional bias <br>
    *
-   * @param layerInput the input to max pooling 2d operation - 4d CNN (image) activations in NCHW format
+   * @param layerInput the input to max pooling 2d operation - 4d CNN (image) activations in NCHW format (shape [minibatch, channels, height, width]) or NHWC format (shape [minibatch, height, width, channels]) (NUMERIC type)
-   *                      (shape [minibatch, channels, height, width]) or NHWC format (shape [minibatch, height, width, channels]) (NUMERIC type)
    * @param depthWeights Separable conv2d depth weights. 4 dimensions with format [kernelHeight, kernelWidth, inputChannels, depthMultiplier] (NUMERIC type)
    * @param pointWeights Point weights, rank 4 with format [1, 1, inputChannels*depthMultiplier, outputChannels]. May be null (NUMERIC type)
    * @param bias Optional bias, rank 1 with shape [outputChannels]. May be null. (NUMERIC type)
@@ -432,8 +417,7 @@ public class NDCNN {
   /**
    * Separable 2D convolution operation with optional bias <br>
    *
-   * @param layerInput the input to max pooling 2d operation - 4d CNN (image) activations in NCHW format
+   * @param layerInput the input to max pooling 2d operation - 4d CNN (image) activations in NCHW format (shape [minibatch, channels, height, width]) or NHWC format (shape [minibatch, height, width, channels]) (NUMERIC type)
-   *                      (shape [minibatch, channels, height, width]) or NHWC format (shape [minibatch, height, width, channels]) (NUMERIC type)
    * @param depthWeights Separable conv2d depth weights. 4 dimensions with format [kernelHeight, kernelWidth, inputChannels, depthMultiplier] (NUMERIC type)
    * @param pointWeights Point weights, rank 4 with format [1, 1, inputChannels*depthMultiplier, outputChannels]. May be null (NUMERIC type)
    * @param Conv2DConfig Configuration Object
@@ -471,8 +455,7 @@ public class NDCNN {
    * Example: if input has shape [mb, 2, 4, 4] and block size is 2, then output size is [mb, 8/(2*2), 2*2, 2*2]<br>
    * = [mb, 2, 4, 4] <br>
    *
-   * @param x the input to depth to space pooling 2d operation - 4d activations in NCHW format
+   * @param x the input to depth to space pooling 2d operation - 4d activations in NCHW format (shape [minibatch, channels, height, width]) or NHWC format (shape [minibatch, height, width, channels]) (NUMERIC type)
-   *                    (shape [minibatch, channels, height, width]) or NHWC format (shape [minibatch, height, width, channels]) (NUMERIC type)
    * @param blockSize  Block size, in the height/width dimension
    * @param dataFormat Data format: "NCHW" or "NHWC"
    * @return output Output variable (NUMERIC type)

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/factory/ops/NDImage.java

@@ -1,25 +1,25 @@
-/*
+/*******************************************************************************
- *  ******************************************************************************
+ * Copyright (c) 2019-2020 Konduit K.K.
- *  *
+ *
- *  *
+ * This program and the accompanying materials are made available under the
- *  * This program and the accompanying materials are made available under the
+ * terms of the Apache License, Version 2.0 which is available at
- *  * terms of the Apache License, Version 2.0 which is available at
+ * https://www.apache.org/licenses/LICENSE-2.0.
- *  * https://www.apache.org/licenses/LICENSE-2.0.
+ *
- *  *
+ * Unless required by applicable law or agreed to in writing, software
- *  *  See the NOTICE file distributed with this work for additional
+ * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
- *  *  information regarding copyright ownership.
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
- *  * Unless required by applicable law or agreed to in writing, software
+ * License for the specific language governing permissions and limitations
- *  * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+ * under the License.
- *  * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+ *
- *  * License for the specific language governing permissions and limitations
+ * SPDX-License-Identifier: Apache-2.0
- *  * under the License.
+ ******************************************************************************/
- *  *
+
- *  * SPDX-License-Identifier: Apache-2.0
+//================== GENERATED CODE - DO NOT MODIFY THIS FILE ==================
- *  *****************************************************************************
- */
 
 package org.nd4j.linalg.factory.ops;
 
+import static org.nd4j.linalg.factory.NDValidation.isSameType;
+
 import org.nd4j.common.base.Preconditions;
 import org.nd4j.enums.ImageResizeMethod;
 import org.nd4j.linalg.api.ndarray.INDArray;

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/factory/ops/NDLinalg.java

@@ -1,25 +1,25 @@
-/*
+/*******************************************************************************
- *  ******************************************************************************
+ * Copyright (c) 2019-2020 Konduit K.K.
- *  *
+ *
- *  *
+ * This program and the accompanying materials are made available under the
- *  * This program and the accompanying materials are made available under the
+ * terms of the Apache License, Version 2.0 which is available at
- *  * terms of the Apache License, Version 2.0 which is available at
+ * https://www.apache.org/licenses/LICENSE-2.0.
- *  * https://www.apache.org/licenses/LICENSE-2.0.
+ *
- *  *
+ * Unless required by applicable law or agreed to in writing, software
- *  *  See the NOTICE file distributed with this work for additional
+ * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
- *  *  information regarding copyright ownership.
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
- *  * Unless required by applicable law or agreed to in writing, software
+ * License for the specific language governing permissions and limitations
- *  * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+ * under the License.
- *  * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+ *
- *  * License for the specific language governing permissions and limitations
+ * SPDX-License-Identifier: Apache-2.0
- *  * under the License.
+ ******************************************************************************/
- *  *
+
- *  * SPDX-License-Identifier: Apache-2.0
+//================== GENERATED CODE - DO NOT MODIFY THIS FILE ==================
- *  *****************************************************************************
- */
 
 package org.nd4j.linalg.factory.ops;
 
+import static org.nd4j.linalg.factory.NDValidation.isSameType;
+
 import org.nd4j.linalg.api.buffer.DataType;
 import org.nd4j.linalg.api.ndarray.INDArray;
 import org.nd4j.linalg.factory.NDValidation;

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/factory/ops/NDLoss.java

@@ -1,25 +1,25 @@
-/*
+/*******************************************************************************
- *  ******************************************************************************
+ * Copyright (c) 2019-2020 Konduit K.K.
- *  *
+ *
- *  *
+ * This program and the accompanying materials are made available under the
- *  * This program and the accompanying materials are made available under the
+ * terms of the Apache License, Version 2.0 which is available at
- *  * terms of the Apache License, Version 2.0 which is available at
+ * https://www.apache.org/licenses/LICENSE-2.0.
- *  * https://www.apache.org/licenses/LICENSE-2.0.
+ *
- *  *
+ * Unless required by applicable law or agreed to in writing, software
- *  *  See the NOTICE file distributed with this work for additional
+ * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
- *  *  information regarding copyright ownership.
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
- *  * Unless required by applicable law or agreed to in writing, software
+ * License for the specific language governing permissions and limitations
- *  * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+ * under the License.
- *  * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+ *
- *  * License for the specific language governing permissions and limitations
+ * SPDX-License-Identifier: Apache-2.0
- *  * under the License.
+ ******************************************************************************/
- *  *
+
- *  * SPDX-License-Identifier: Apache-2.0
+//================== GENERATED CODE - DO NOT MODIFY THIS FILE ==================
- *  *****************************************************************************
- */
 
 package org.nd4j.linalg.factory.ops;
 
+import static org.nd4j.linalg.factory.NDValidation.isSameType;
+
 import org.nd4j.autodiff.loss.LossReduce;
 import org.nd4j.linalg.api.ndarray.INDArray;
 import org.nd4j.linalg.factory.NDValidation;
@@ -35,7 +35,7 @@ public class NDLoss {
    * @param label Label array (NUMERIC type)
    * @param predictions Predictions array (NUMERIC type)
    * @param weights Weights array. May be null. If null, a weight of 1.0 is used (NUMERIC type)
-   * @param lossReduce Reduction type for the loss. See {@link LossReduce} for more details. Default: {@link LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT}
+   * @param lossReduce Reduction type for the loss. See LossReduce for more details. Default: LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT
    * @return output loss variable (NUMERIC type)
    */
   public INDArray absoluteDifference(INDArray label, INDArray predictions, INDArray weights,
@@ -71,7 +71,7 @@ public class NDLoss {
    * @param label Label array (NUMERIC type)
    * @param predictions Predictions array (NUMERIC type)
    * @param weights Weights array. May be null. If null, a weight of 1.0 is use (NUMERIC type)
-   * @param lossReduce Reduction type for the loss. See {@link LossReduce} for more details. Default: {@link LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT}
+   * @param lossReduce Reduction type for the loss. See LossReduce for more details. Default: LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT
    * @param dimension Dimension to perform the cosine distance over
    * @return output Cosine distance loss  (NUMERIC type)
    */
@@ -104,6 +104,25 @@ public class NDLoss {
     return Nd4j.exec(new org.nd4j.linalg.api.ops.impl.loss.CosineDistanceLoss(label, predictions, weights, org.nd4j.autodiff.loss.LossReduce.MEAN_BY_NONZERO_WEIGHT_COUNT, dimension))[0];
   }
 
+  /**
+   * CTC Loss: Connectionist Temporal Classification Loss. See:<br>
+   * https://dl.acm.org/citation.cfm?id=1143891<br>
+   *
+   * @param targetLabels Label array (NUMERIC type)
+   * @param logitInput Inputs (NUMERIC type)
+   * @param targetLabelLengths Length of the target label (NUMERIC type)
+   * @param logitInputLengths Length of the input (NUMERIC type)
+   * @return output Ctc loss  (NUMERIC type)
+   */
+  public INDArray ctcLoss(INDArray targetLabels, INDArray logitInput, INDArray targetLabelLengths,
+      INDArray logitInputLengths) {
+    NDValidation.validateNumerical("ctcLoss", "targetLabels", targetLabels);
+    NDValidation.validateNumerical("ctcLoss", "logitInput", logitInput);
+    NDValidation.validateNumerical("ctcLoss", "targetLabelLengths", targetLabelLengths);
+    NDValidation.validateNumerical("ctcLoss", "logitInputLengths", logitInputLengths);
+    return Nd4j.exec(new org.nd4j.linalg.api.ops.impl.loss.CtcLoss(targetLabels, logitInput, targetLabelLengths, logitInputLengths))[0];
+  }
+
   /**
    * Hinge loss: a loss function used for training classifiers.<br>
    * Implements {@code L = max(0, 1 - t * predictions)} where t is the label values after internally converting to {-1,1}<br>
@@ -112,7 +131,7 @@ public class NDLoss {
    * @param label Label array. Each value should be 0.0 or 1.0 (internally -1 to 1 is used) (NUMERIC type)
    * @param predictions Predictions array (NUMERIC type)
    * @param weights Weights array. May be null. If null, a weight of 1.0 is used (NUMERIC type)
-   * @param lossReduce Reduction type for the loss. See {@link LossReduce} for more details. Default: {@link LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT}
+   * @param lossReduce Reduction type for the loss. See LossReduce for more details. Default: LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT
    * @return output Loss variable (NUMERIC type)
    */
   public INDArray hingeLoss(INDArray label, INDArray predictions, INDArray weights,
@@ -152,7 +171,7 @@ public class NDLoss {
    * @param label Label array (NUMERIC type)
    * @param predictions Predictions array (NUMERIC type)
    * @param weights Weights array. May be null. If null, a weight of 1.0 is used (NUMERIC type)
-   * @param lossReduce Reduction type for the loss. See {@link LossReduce} for more details. Default: {@link LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT}
+   * @param lossReduce Reduction type for the loss. See LossReduce for more details. Default: LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT
    * @param delta Loss function delta value
    * @return output Huber loss (NUMERIC type)
    */
@@ -204,7 +223,7 @@ public class NDLoss {
    * @param label Label array (NUMERIC type)
    * @param predictions Predictions array (NUMERIC type)
    * @param weights Weights array. May be null. If null, a weight of 1.0 is used (NUMERIC type)
-   * @param lossReduce Reduction type for the loss. See {@link LossReduce} for more details. Default: {@link LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT}
+   * @param lossReduce Reduction type for the loss. See LossReduce for more details. Default: LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT
    * @param epsilon epsilon
    * @return output Log loss  (NUMERIC type)
    */
@@ -237,7 +256,7 @@ public class NDLoss {
    * @param label Label array. Each value should be 0.0 or 1.0 (NUMERIC type)
    * @param predictions Predictions array (has to be log(x) of actual predictions) (NUMERIC type)
    * @param weights Weights array. May be null. If null, a weight of 1.0 is used (NUMERIC type)
-   * @param lossReduce Reduction type for the loss. See {@link LossReduce} for more details. Default: {@link LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT}
+   * @param lossReduce Reduction type for the loss. See LossReduce for more details. Default: LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT
    * @param full Boolean flag. true for logPoissonFull, false for logPoisson
    * @return output Loss variable (NUMERIC type)
    */
@@ -275,7 +294,7 @@ public class NDLoss {
    * @param label Label array (NUMERIC type)
    * @param predictions Predictions array (NUMERIC type)
    * @param weights Weights array. May be null. If null, a weight of 1.0 is used. Must be either null, scalar, or have shape [batchSize] (NUMERIC type)
-   * @param lossReduce Reduction type for the loss. See {@link LossReduce} for more details. Default: {@link LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT}
+   * @param lossReduce Reduction type for the loss. See LossReduce for more details. Default: LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT
    * @return output Loss variable, scalar output (NUMERIC type)
    */
   public INDArray meanPairwiseSquaredError(INDArray label, INDArray predictions, INDArray weights,
@@ -306,13 +325,13 @@ public class NDLoss {
 
   /**
    * Mean squared error loss function. Implements {@code (label[i] - prediction[i])^2} - i.e., squared error on a per-element basis.<br>
-   * When averaged (using {@link LossReduce#MEAN_BY_WEIGHT} or {@link LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT} (the default))<br>
+   * When averaged (using LossReduce#MEAN_BY_WEIGHT or LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT (the default))<br>
    * this is the mean squared error loss function.<br>
    *
    * @param label Label array (NUMERIC type)
    * @param predictions Predictions array (NUMERIC type)
    * @param weights Weights array. May be null. If null, a weight of 1.0 is used (NUMERIC type)
-   * @param lossReduce Reduction type for the loss. See {@link LossReduce} for more details. Default: {@link LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT}
+   * @param lossReduce Reduction type for the loss. See LossReduce for more details. Default: LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT
    * @return output Loss variable (NUMERIC type)
    */
   public INDArray meanSquaredError(INDArray label, INDArray predictions, INDArray weights,
@@ -325,7 +344,7 @@ public class NDLoss {
 
   /**
    * Mean squared error loss function. Implements {@code (label[i] - prediction[i])^2} - i.e., squared error on a per-element basis.<br>
-   * When averaged (using {@link LossReduce#MEAN_BY_WEIGHT} or {@link LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT} (the default))<br>
+   * When averaged (using LossReduce#MEAN_BY_WEIGHT or LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT (the default))<br>
    * this is the mean squared error loss function.<br>
    *
    * @param label Label array (NUMERIC type)
@@ -357,7 +376,7 @@ public class NDLoss {
    * @param label Label array (NUMERIC type)
    * @param predictionLogits Predictions array (NUMERIC type)
    * @param weights Weights array. May be null. If null, a weight of 1.0 is used (NUMERIC type)
-   * @param lossReduce Reduction type for the loss. See {@link LossReduce} for more details. Default: {@link LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT}
+   * @param lossReduce Reduction type for the loss. See LossReduce for more details. Default: LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT
    * @param labelSmoothing Label smoothing value. Default value: 0
    * @return output Loss variable (NUMERIC type)
    */
@@ -398,7 +417,7 @@ public class NDLoss {
   /**
    * Applies the softmax activation function to the input, then implement multi-class cross entropy:<br>
    * {@code -sum_classes label[i] * log(p[c])} where {@code p = softmax(logits)}<br>
-   * If {@link LossReduce#NONE} is used, returned shape is [numExamples] out for [numExamples, numClasses] predicitons/labels;<br>
+   * If LossReduce#NONE is used, returned shape is [numExamples] out for [numExamples, numClasses] predicitons/labels;<br>
    * otherwise, the output is a scalar.<br>
    * <p><br>
    * When label smoothing is > 0, the following label smoothing is used:<br>
@@ -410,7 +429,7 @@ public class NDLoss {
    * @param oneHotLabels Label array. Should be one-hot per example and same shape as predictions (for example, [mb, nOut]) (NUMERIC type)
    * @param logitPredictions Predictions array (pre-softmax) (NUMERIC type)
    * @param weights Weights array. May be null. If null, a weight of 1.0 is used (NUMERIC type)
-   * @param lossReduce Reduction type for the loss. See {@link LossReduce} for more details. Default: {@link LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT}
+   * @param lossReduce Reduction type for the loss. See LossReduce for more details. Default: LossReduce#MEAN_BY_NONZERO_WEIGHT_COUNT
    * @param labelSmoothing Label smoothing value. Default value: 0
    * @return output Loss variable (NUMERIC type)
    */
@@ -425,7 +444,7 @@ public class NDLoss {
   /**
    * Applies the softmax activation function to the input, then implement multi-class cross entropy:<br>
    * {@code -sum_classes label[i] * log(p[c])} where {@code p = softmax(logits)}<br>
-   * If {@link LossReduce#NONE} is used, returned shape is [numExamples] out for [numExamples, numClasses] predicitons/labels;<br>
+   * If LossReduce#NONE is used, returned shape is [numExamples] out for [numExamples, numClasses] predicitons/labels;<br>
    * otherwise, the output is a scalar.<br>
    * <p><br>
    * When label smoothing is > 0, the following label smoothing is used:<br>

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/factory/ops/NDMath.java

@@ -1,25 +1,25 @@
-/*
+/*******************************************************************************
- *  ******************************************************************************
+ * Copyright (c) 2019-2020 Konduit K.K.
- *  *
+ *
- *  *
+ * This program and the accompanying materials are made available under the
- *  * This program and the accompanying materials are made available under the
+ * terms of the Apache License, Version 2.0 which is available at
- *  * terms of the Apache License, Version 2.0 which is available at
+ * https://www.apache.org/licenses/LICENSE-2.0.
- *  * https://www.apache.org/licenses/LICENSE-2.0.
+ *
- *  *
+ * Unless required by applicable law or agreed to in writing, software
- *  *  See the NOTICE file distributed with this work for additional
+ * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
- *  *  information regarding copyright ownership.
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
- *  * Unless required by applicable law or agreed to in writing, software
+ * License for the specific language governing permissions and limitations
- *  * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+ * under the License.
- *  * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+ *
- *  * License for the specific language governing permissions and limitations
+ * SPDX-License-Identifier: Apache-2.0
- *  * under the License.
+ ******************************************************************************/
- *  *
+
- *  * SPDX-License-Identifier: Apache-2.0
+//================== GENERATED CODE - DO NOT MODIFY THIS FILE ==================
- *  *****************************************************************************
- */
 
 package org.nd4j.linalg.factory.ops;
 
+import static org.nd4j.linalg.factory.NDValidation.isSameType;
+
 import org.nd4j.common.base.Preconditions;
 import org.nd4j.enums.PartitionMode;
 import org.nd4j.linalg.api.buffer.DataType;

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/factory/ops/NDNN.java

@@ -1,25 +1,25 @@
-/*
+/*******************************************************************************
- *  ******************************************************************************
+ * Copyright (c) 2019-2020 Konduit K.K.
- *  *
+ *
- *  *
+ * This program and the accompanying materials are made available under the
- *  * This program and the accompanying materials are made available under the
+ * terms of the Apache License, Version 2.0 which is available at
- *  * terms of the Apache License, Version 2.0 which is available at
+ * https://www.apache.org/licenses/LICENSE-2.0.
- *  * https://www.apache.org/licenses/LICENSE-2.0.
+ *
- *  *
+ * Unless required by applicable law or agreed to in writing, software
- *  *  See the NOTICE file distributed with this work for additional
+ * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
- *  *  information regarding copyright ownership.
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
- *  * Unless required by applicable law or agreed to in writing, software
+ * License for the specific language governing permissions and limitations
- *  * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+ * under the License.
- *  * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+ *
- *  * License for the specific language governing permissions and limitations
+ * SPDX-License-Identifier: Apache-2.0
- *  * under the License.
+ ******************************************************************************/
- *  *
+
- *  * SPDX-License-Identifier: Apache-2.0
+//================== GENERATED CODE - DO NOT MODIFY THIS FILE ==================
- *  *****************************************************************************
- */
 
 package org.nd4j.linalg.factory.ops;
 
+import static org.nd4j.linalg.factory.NDValidation.isSameType;
+
 import org.nd4j.common.base.Preconditions;
 import org.nd4j.enums.PadMode;
 import org.nd4j.linalg.api.ndarray.INDArray;

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/factory/ops/NDRNN.java

@@ -1,25 +1,25 @@
-/*
+/*******************************************************************************
- *  ******************************************************************************
+ * Copyright (c) 2019-2020 Konduit K.K.
- *  *
+ *
- *  *
+ * This program and the accompanying materials are made available under the
- *  * This program and the accompanying materials are made available under the
+ * terms of the Apache License, Version 2.0 which is available at
- *  * terms of the Apache License, Version 2.0 which is available at
+ * https://www.apache.org/licenses/LICENSE-2.0.
- *  * https://www.apache.org/licenses/LICENSE-2.0.
+ *
- *  *
+ * Unless required by applicable law or agreed to in writing, software
- *  *  See the NOTICE file distributed with this work for additional
+ * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
- *  *  information regarding copyright ownership.
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
- *  * Unless required by applicable law or agreed to in writing, software
+ * License for the specific language governing permissions and limitations
- *  * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+ * under the License.
- *  * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+ *
- *  * License for the specific language governing permissions and limitations
+ * SPDX-License-Identifier: Apache-2.0
- *  * under the License.
+ ******************************************************************************/
- *  *
+
- *  * SPDX-License-Identifier: Apache-2.0
+//================== GENERATED CODE - DO NOT MODIFY THIS FILE ==================
- *  *****************************************************************************
- */
 
 package org.nd4j.linalg.factory.ops;
 
+import static org.nd4j.linalg.factory.NDValidation.isSameType;
+
 import org.nd4j.linalg.api.ndarray.INDArray;
 import org.nd4j.linalg.api.ops.impl.layers.recurrent.config.LSTMConfiguration;
 import org.nd4j.linalg.api.ops.impl.layers.recurrent.config.LSTMLayerConfig;
@@ -85,22 +85,22 @@ public class NDRNN {
 
   /**
    * Long Short-Term Memory layer - Hochreiter 1997.<br>
-   * SUPPORTS following data formats:\n<br>
+   * SUPPORTS following data formats:<br>
-   * for unidirectional: \n" +<br>
+   * for unidirectional:<br>
-   * TNS: shapes [timeLength, numExamples, inOutSize]\n<br>
+   * TNS: shapes [timeLength, numExamples, inOutSize]<br>
-   * NST: shapes [numExamples, inOutSize, timeLength]\n<br>
+   * NST: shapes [numExamples, inOutSize, timeLength]<br>
    * NTS: shapes [numExamples, timeLength, inOutSize]<br>
-   * for bidirectional:\n<br>
+   * for bidirectional:<br>
-   * T2NS: shapes [timeLength, 2, numExamples, inOutSize] (for ONNX)\n<br>
+   * T2NS: shapes [timeLength, 2, numExamples, inOutSize] (for ONNX)<br>
-   * SUPPORTS following direction modes:\n<br>
+   * SUPPORTS following direction modes:<br>
    * FWD: forward<br>
    * BWD: backward<br>
-   * BIDIR_SUM: bidirectional sum\n<br>
+   * BIDIR_SUM: bidirectional sum<br>
-   * BIDIR_CONCAT: bidirectional concat\n" +<br>
+   * BIDIR_CONCAT: bidirectional concat<br>
-   * BIDIR_EXTRA_DIM: bidirectional extra output dim (in conjunction with format dataFormat - T2NS)"<br>
+   * BIDIR_EXTRA_DIM: bidirectional extra output dim (in conjunction with format dataFormat - T2NS)<br>
    * You may use different gate configurations:<br>
-   * specify gate/cell/out aplha/beta and numbers of activations for gate/cell/out described in activations enum\n<br>
+   * specify gate/cell/out aplha/beta and numbers of activations for gate/cell/out described in activations enum<br>
-   * ("RELU","SIGMOID","AFFINE","LEAKY_RELU","THRESHHOLD_RELU","SCALED_TAHN","HARD_SIGMOID","ELU","SOFTSIGN","SOFTPLUS")\n<br>
+   * ("RELU","SIGMOID","AFFINE","LEAKY_RELU","THRESHHOLD_RELU","SCALED_TAHN","HARD_SIGMOID","ELU","SOFTSIGN","SOFTPLUS")<br>
    * Also this layer supports MKLDNN (DNNL) and cuDNN acceleration<br>
    *
    * @param x  Input, with shape dependent on the data format (in config). (NUMERIC type)
@@ -121,22 +121,22 @@ public class NDRNN {
 
   /**
    * Long Short-Term Memory layer - Hochreiter 1997.<br>
-   * SUPPORTS following data formats:\n<br>
+   * SUPPORTS following data formats:<br>
-   * for unidirectional: \n" +<br>
+   * for unidirectional:<br>
-   * TNS: shapes [timeLength, numExamples, inOutSize]\n<br>
+   * TNS: shapes [timeLength, numExamples, inOutSize]<br>
-   * NST: shapes [numExamples, inOutSize, timeLength]\n<br>
+   * NST: shapes [numExamples, inOutSize, timeLength]<br>
    * NTS: shapes [numExamples, timeLength, inOutSize]<br>
-   * for bidirectional:\n<br>
+   * for bidirectional:<br>
-   * T2NS: shapes [timeLength, 2, numExamples, inOutSize] (for ONNX)\n<br>
+   * T2NS: shapes [timeLength, 2, numExamples, inOutSize] (for ONNX)<br>
-   * SUPPORTS following direction modes:\n<br>
+   * SUPPORTS following direction modes:<br>
    * FWD: forward<br>
    * BWD: backward<br>
-   * BIDIR_SUM: bidirectional sum\n<br>
+   * BIDIR_SUM: bidirectional sum<br>
-   * BIDIR_CONCAT: bidirectional concat\n" +<br>
+   * BIDIR_CONCAT: bidirectional concat<br>
-   * BIDIR_EXTRA_DIM: bidirectional extra output dim (in conjunction with format dataFormat - T2NS)"<br>
+   * BIDIR_EXTRA_DIM: bidirectional extra output dim (in conjunction with format dataFormat - T2NS)<br>
    * You may use different gate configurations:<br>
-   * specify gate/cell/out aplha/beta and numbers of activations for gate/cell/out described in activations enum\n<br>
+   * specify gate/cell/out aplha/beta and numbers of activations for gate/cell/out described in activations enum<br>
-   * ("RELU","SIGMOID","AFFINE","LEAKY_RELU","THRESHHOLD_RELU","SCALED_TAHN","HARD_SIGMOID","ELU","SOFTSIGN","SOFTPLUS")\n<br>
+   * ("RELU","SIGMOID","AFFINE","LEAKY_RELU","THRESHHOLD_RELU","SCALED_TAHN","HARD_SIGMOID","ELU","SOFTSIGN","SOFTPLUS")<br>
    * Also this layer supports MKLDNN (DNNL) and cuDNN acceleration<br>
    *
    * @param x  Input, with shape dependent on the data format (in config). (NUMERIC type)

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/factory/ops/NDRandom.java

@@ -1,25 +1,25 @@
-/*
+/*******************************************************************************
- *  ******************************************************************************
+ * Copyright (c) 2019-2020 Konduit K.K.
- *  *
+ *
- *  *
+ * This program and the accompanying materials are made available under the
- *  * This program and the accompanying materials are made available under the
+ * terms of the Apache License, Version 2.0 which is available at
- *  * terms of the Apache License, Version 2.0 which is available at
+ * https://www.apache.org/licenses/LICENSE-2.0.
- *  * https://www.apache.org/licenses/LICENSE-2.0.
+ *
- *  *
+ * Unless required by applicable law or agreed to in writing, software
- *  *  See the NOTICE file distributed with this work for additional
+ * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
- *  *  information regarding copyright ownership.
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
- *  * Unless required by applicable law or agreed to in writing, software
+ * License for the specific language governing permissions and limitations
- *  * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+ * under the License.
- *  * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+ *
- *  * License for the specific language governing permissions and limitations
+ * SPDX-License-Identifier: Apache-2.0
- *  * under the License.
+ ******************************************************************************/
- *  *
+
- *  * SPDX-License-Identifier: Apache-2.0
+//================== GENERATED CODE - DO NOT MODIFY THIS FILE ==================
- *  *****************************************************************************
- */
 
 package org.nd4j.linalg.factory.ops;
 
+import static org.nd4j.linalg.factory.NDValidation.isSameType;
+
 import org.nd4j.common.base.Preconditions;
 import org.nd4j.linalg.api.buffer.DataType;
 import org.nd4j.linalg.api.ndarray.INDArray;