285 lines
14 KiB
Markdown
285 lines
14 KiB
Markdown
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---
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title: Tour of Eclipse Deeplearning4j Examples
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short_title: Examples Tour
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description: Brief tour of available examples in DL4J.
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category: Get Started
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weight: 10
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---
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## Survey of DeepLearning4j Examples
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Deeplearning4j's Github repository has many examples to cover its functionality. The [Quick Start Guide](./deeplearning4j-quickstart) shows you how to set up Intellij and clone the repository. This page provides an overview of some of those examples.
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## DataVec examples
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Most of the examples make use of DataVec, a toolkit for preprocessing and clearning data through normalization, standardization, search and replace, column shuffles and vectorization. Reading raw data and transforming it into a DataSet object for your Neural Network is often the first step toward training that network. If you're unfamiliar with DataVec, here is a description and some links to useful examples.
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### IrisAnalysis.java
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This example takes the canonical Iris dataset of the flower species of the same name, whose relevant measurements are sepal length, sepal width, petal length and petal width. It builds a Spark RDD from the relatively small dataset and runs an analysis against it.
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[Show me the code](http://github.com/deeplearning4j/dl4j-examples/blob/master/datavec-examples/src/main/java/org/datavec/transform/analysis/IrisAnalysis.java)
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### BasicDataVecExample.java
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This example loads data into a Spark RDD. All DataVec transform operations use Spark RDDs. Here, we use DataVec to filter data, apply time transformations and remove columns.
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[Show me the code](http://github.com/deeplearning4j/dl4j-examples/blob/master/datavec-examples/src/main/java/org/datavec/transform/basic/BasicDataVecExample.java)
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### PrintSchemasAtEachStep.java
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This example shows the print Schema tools that are useful to visualize and to ensure that the code for the transform is behaving as expected.
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[Show me the code](http://github.com/deeplearning4j/dl4j-examples/blob/master/datavec-examples/src/main/java/org/datavec/transform/debugging/PrintSchemasAtEachStep.java)
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### JoinExample.java
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You may need to join datasets before passing to a neural network. You can do that in DataVec, and this example shows you how.
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[Show me the code](http://github.com/deeplearning4j/dl4j-examples/blob/master/datavec-examples/src/main/java/org/datavec/transform/join/JoinExample.java)
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### LogDataExample.java
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This is an example of parsing log data using DataVec. The obvious use cases are cybersecurity and customer relationship management.
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[Show me the code](http://github.com/deeplearning4j/dl4j-examples/blob/master/datavec-examples/src/main/java/org/datavec/transform/logdata/LogDataExample.java)
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### MnistImagePipelineExample.java
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This example is from the video below, which demonstrates the ParentPathLabelGenerator and ImagePreProcessing scaler.
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<iframe width="560" height="315" src="http://www.youtube.com/embed/GLC8CIoHDnI" frameborder="0" allowfullscreen></iframe>
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[Show me the code](http://github.com/deeplearning4j/dl4j-examples/blob/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/dataExamples/MnistImagePipelineExample.java)
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### PreprocessNormalizerExample.java
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This example demonstrates preprocessing features available in DataVec.
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[Show me the code](https://github.com/deeplearning4j/dl4j-examples/blob/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/dataexamples/PreprocessNormalizerExample.java)
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### CSVExampleEvaluationMetaData.java
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DataMeta data tracking - i.e. seeing where data for each example comes from - is useful when tracking down malformed data that causes errors and other issues. This example demostrates the functionality in the RecordMetaData class.
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[Show me the code](https://github.com/deeplearning4j/dl4j-examples/blob/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/dataexamples/CSVExampleEvaluationMetaData.java)
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---
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## DeepLearning4J Examples
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To build a neural net, you will use either `MultiLayerNetwork` or `ComputationGraph`. Both options work using a Builder interface. A few highlights from the examples are described below.
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### MNIST dataset of handwritten digits
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MNIST is the "Hello World" of deep learning. Simple, straightforward, and focussed on image recognition, a task that Neural Networks do well.
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### MLPMnistSingleLayerExample.java
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This is a Single Layer Perceptron for recognizing digits. Note that this pulls the images from a binary package containing the dataset, a rather special case for data ingestion.
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[Show me the code](http://github.com/deeplearning4j/dl4j-examples/blob/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/feedforward/mnist/MLPMnistSingleLayerExample.java)
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### MLPMnistTwoLayerExample.java
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A two-layer perceptron for MNIST, showing there is more than one useful network for a given dataset.
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[Show me the code](http://github.com/deeplearning4j/dl4j-examples/blob/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/feedforward/mnist/MLPMnistTwoLayerExample.java)
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### Feedforward Examples
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Data flows through feed-forward neural networks in a single pass from input via hidden layers to output.
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These networks can be used for a wide range of tasks depending on they are configured. Along with image classification over MNIST data, this directory has examples demonstrating regression, classification, and anomoly detection.
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[Show me the code](http://github.com/deeplearning4j/dl4j-examples/tree/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/feedforward)
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### Convolutional Neural Networks
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Convolutional Neural Networks are mainly used for image recognition, although they apply to sound and text as well.
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### AnimalsClassification.java
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This example can be run using either LeNet or AlexNet.
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[Show me the code](http://github.com/deeplearning4j/dl4j-examples/blob/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/convolution/AnimalsClassification.java)
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---
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## Saving and Loading Models
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Training a network over a large volume of training data takes time. Fortunately, you can save a trained model and
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load the model for later training or inference.
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### SaveLoadComputationGraph.java
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This demonstrates saving and loading a network build using the class ComputationGraph.
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[Show me the code](http://github.com/deeplearning4j/dl4j-examples/blob/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/misc/modelsaving/SaveLoadComputationGraph.java)
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### SaveLoadMultiLayerNetwork.java
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Demonstrates saving and loading a Neural Network built with the class MultiLayerNetwork.
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### Saving/loading a trained model and passing it new input
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Our video series shows code that includes saving and loading models, as well as inference.
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[Our YouTube channel](https://www.youtube.com/channel/UCa-HKBJwkfzs4AgZtdUuBXQ)
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---
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## Custom Loss Functions and Layers
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Do you need to add a Loss Function that is not available or prebuilt yet? Check out these examples.
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### CustomLossExample.java
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[Show me the code](http://github.com/deeplearning4j/dl4j-examples/blob/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/misc/lossfunctions/CustomLossExample.java)
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### CustomLossL1L2.java
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[Show me the code](http://github.com/deeplearning4j/dl4j-examples/blob/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/misc/lossfunctions/CustomLossL1L2.java)
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### Custom Layer
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Do you need to add a layer with features that aren't available in DeepLearning4J core? This example show where to begin.
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### CustomLayerExample.java
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[Show me the code](http://github.com/deeplearning4j/dl4j-examples/blob/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/misc/customlayers/CustomLayerExample.java)
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---
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## Natural Language Processing
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Neural Networks for NLP? We have those, too.
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### GloVe
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Global Vectors for Word Representation are useful for detecting relationships between words.
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[Show me the code](http://github.com/deeplearning4j/dl4j-examples/blob/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/nlp/glove/GloVeExample.java)
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### Paragraph Vectors
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A vectorized representation of words. Described [here](https://cs.stanford.edu/~quocle/paragraph_vector.pdf)
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[Show me the code](http://github.com/deeplearning4j/dl4j-examples/blob/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/nlp/paragraphvectors/ParagraphVectorsClassifierExample.java)
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### Sequence Vectors
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One way to represent sentences is as a sequence of words.
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[Show me the code](http://github.com/deeplearning4j/dl4j-examples/blob/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/nlp/sequencevectors/SequenceVectorsTextExample.java)
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### Word2Vec
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Described [here](https://deeplearning4j.org/word2vec.html)
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[Show me the code](http://github.com/deeplearning4j/dl4j-examples/blob/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/nlp/word2vec/Word2VecRawTextExample.java)
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---
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## Data Visualization
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t-Distributed Stochastic Neighbor Embedding (t-SNE) is useful for data visualization. We include an example in the NLP section since word similarity visualization is a common use.
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[Show me the code](http://github.com/deeplearning4j/dl4j-examples/blob/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/nlp/tsne/TSNEStandardExample.java)
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---
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## Recurrent Neural Networks
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Recurrent Neural Networks are useful for processing time series data or other sequentially fed data like video.
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The examples folder for Recurrent Neural Networks has the following:
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### BasicRNNExample.java
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An RNN learns a string of characters.
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[Show me the code](http://github.com/deeplearning4j/dl4j-examples/blob/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/recurrent/basic/BasicRNNExample.java)
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### GravesLSTMCharModellingExample.java
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Takes the complete works of Shakespeare as a sequence of characters and Trains a Neural Net to generate "Shakespeare" one character at a time.
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[Show me the code](http://github.com/deeplearning4j/dl4j-examples/blob/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/recurrent/character/GravesLSTMCharModellingExample.java)
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### SingleTimestepRegressionExample.java
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Regression with an LSTM (Long Short Term Memory) Recurrent Neural Network.
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[Show me the code](http://github.com/deeplearning4j/dl4j-examples/blob/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/recurrent/regression/SingleTimestepRegressionExample.java)
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### AdditionRNN.java
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This example trains a neural network to do addition.
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[Show me the code](https://github.com/deeplearning4j/dl4j-examples/blob/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/recurrent/seq2seq/AdditionRNN.java)
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### RegressionMathFunctions.java
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This example trains a neural network to perform various math operations.
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[Show me the code](https://github.com/deeplearning4j/dl4j-examples/blob/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/feedforward/regression/RegressionMathFunctions.java)
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### UCISequenceClassificationExample.java
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A publicly available dataset of time series data of six classes, cyclic, up-trending, etc. Example of an RNN learning to classify the time series.
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[Show me the code](https://github.com/deeplearning4j/dl4j-examples/blob/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/recurrent/seqclassification/UCISequenceClassificationExample.java)
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### VideoClassificationExample.java
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How do autonomous vehicles distinguish between a pedestrian, a stop sign and a green light? A complex neural net using Convolutional and Recurrent layers is trained on a set of training videos. The trained network is passed live onboard video and decisions based on object detection from the Neural Net determine the vehicles actions.
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This example is similar, but simplified. It combines convolutional, max pooling, dense (feed forward) and recurrent (LSTM) layers to classify frames in a video.
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[Show me the code](https://github.com/deeplearning4j/dl4j-examples/blob/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/recurrent/video/VideoClassificationExample.java)
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### SentimentExampleIterator.java
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This sentiment analysis example classifies sentiment as positive or negative using word vectors and a Recurrent Neural Network.
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[Show me the code](https://github.com/deeplearning4j/dl4j-examples/blob/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/recurrent/word2vecsentiment/Word2VecSentimentRNN.java)
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---
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## Distributed Training on Spark
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DeepLearning4j supports using a Spark Cluster for network training. Here are the examples.
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### MnistMLPExample.java
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This is an example of a Multi-Layer Perceptron training on the Mnist data set of handwritten digits.
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[Show me the code](http://github.com/deeplearning4j/dl4j-examples/blob/master/dl4j-spark-examples/dl4j-spark/src/main/java/org/deeplearning4j/mlp/MnistMLPExample.java)
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### SparkLSTMCharacterExample.java
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An LSTM recurrent Network in Spark.
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[Show me the code](http://github.com/deeplearning4j/dl4j-examples/blob/master/dl4j-spark-examples/dl4j-spark/src/main/java/org/deeplearning4j/rnn/SparkLSTMCharacterExample.java)
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---
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## ND4J Examples
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ND4J is a tensor processing library. It can be thought of as Numpy for the JVM. Neural Networks work by processing and updating MultiDimensional arrays of numeric values. In a typical Neural Net application you use DataVec to ingest and convert the data to numeric. Classes used would be RecordReader. Once you need to pass data into a Neural Network, you typically use RecordReaderDataSetIterator. RecordReaderDataSetIterator returns a DataSet object. DataSet consists of an NDArray of the input features and an NDArray of the labels.
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The learning algorithms and loss functions are executed as ND4J operations.
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### Basic ND4J examples
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This is a directory with examples for creating and manipulating NDArrays.
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[Show me the code](http://github.com/deeplearning4j/dl4j-examples/tree/master/nd4j-examples/src/main/java/org/nd4j/examples)
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---
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## Reinforcement Learning Examples
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Deep learning algorithms have learned to play Space Invaders and Doom using reinforcement learning. DeepLearning4J/RL4J examples of Reinforcement Learning are available here:
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[Show me the code](http://github.com/deeplearning4j/dl4j-examples/tree/master/rl4j-examples)
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