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Graves LSTM example adapted for DL4J 1.0
package com.syndatis.deeplearning;
import java.io.File;
import java.io.IOException;
import java.net.URL;
import java.nio.charset.Charset;
import java.util.Random;
import org.apache.commons.io.FileUtils;
import org.deeplearning4j.nn.api.Layer;
import org.deeplearning4j.nn.api.OptimizationAlgorithm;
import org.deeplearning4j.nn.conf.BackpropType;
import org.deeplearning4j.nn.conf.MultiLayerConfiguration;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.deeplearning4j.nn.conf.layers.GravesLSTM;
import org.deeplearning4j.nn.conf.layers.RnnOutputLayer;
import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
import org.deeplearning4j.nn.weights.WeightInit;
import org.deeplearning4j.optimize.listeners.ScoreIterationListener;
import org.nd4j.linalg.activations.Activation;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.dataset.DataSet;
import org.nd4j.linalg.factory.Nd4j;
import org.nd4j.linalg.learning.config.RmsProp;
import org.nd4j.linalg.lossfunctions.LossFunctions.LossFunction;
/**
* GravesLSTM Character modelling example
*
* @author Alex Black
* <p>
* Example: Train a LSTM RNN to generates text, one character at a time.
* This example is somewhat inspired by Andrej Karpathy's blog post,
* "The Unreasonable Effectiveness of Recurrent Neural Networks"
* http://karpathy.github.io/2015/05/21/rnn-effectiveness/
* <p>
* This example is set up to train on the Complete Works of William Shakespeare, downloaded
* from Project Gutenberg. Training on other text sources should be relatively easy to implement.
* <p>
* For more details on RNNs in DL4J, see the following:
* http://deeplearning4j.org/usingrnns
* http://deeplearning4j.org/lstm
* http://deeplearning4j.org/recurrentnetwork
*/
public class GravesLSTMCharModellingExample {
public static void main(String[] args) throws Exception {
int lstmLayerSize = 200; //Number of units in each GravesLSTM layer
int miniBatchSize = 32; //Size of mini batch to use when training
int exampleLength = 1000; //Length of each training example sequence to use. This could certainly be increased
int tbpttLength = 50; //Length for truncated backpropagation through time. i.e., do parameter updates ever 50 characters
int numEpochs = 5; //Total number of training epochs
int generateSamplesEveryNMinibatches = 5; //How frequently to generate samples from the network? 1000 characters / 50 tbptt length: 20 parameter updates per minibatch
int nSamplesToGenerate = 6; //Number of samples to generate after each training epoch
int nCharactersToSample = 300; //Length of each sample to generate
String generationInitialization = null; //Optional character initialization; a random character is used if null
// Above is Used to 'prime' the LSTM with a character sequence to continue/complete.
// Initialization characters must all be in CharacterIterator.getMinimalCharacterSet() by default
Random rng = new Random(12345);
//Get a DataSetIterator that handles vectorization of text into something we can use to train
// our GravesLSTM network.
CharacterIterator iter = getShakespeareIterator(miniBatchSize, exampleLength);
int nOut = iter.totalOutcomes();
//Set up network configuration:
MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
.seed(12345)
.l2(0.001)
.weightInit(WeightInit.XAVIER)
.updater(new RmsProp.Builder().rmsDecay(0.95).learningRate(0.1).build())
.list()
.layer(0, new GravesLSTM.Builder().nIn(iter.inputColumns()).nOut(lstmLayerSize)
.activation(Activation.TANH).build())
.layer(1, new GravesLSTM.Builder().nIn(lstmLayerSize).nOut(lstmLayerSize)
.activation(Activation.TANH).build())
.layer(2, new RnnOutputLayer.Builder(LossFunction.MCXENT).activation(Activation.SOFTMAX) //MCXENT + softmax for classification
.nIn(lstmLayerSize).nOut(nOut).build())
.backpropType(BackpropType.TruncatedBPTT).tBPTTForwardLength(tbpttLength).tBPTTBackwardLength(tbpttLength)
.build();
MultiLayerNetwork net = new MultiLayerNetwork(conf);
net.init();
net.setListeners(new ScoreIterationListener(1));
//Print the number of parameters in the network (and for each layer)
Layer[] layers = net.getLayers();
int totalNumParams = 0;
for (int i = 0; i < layers.length; i++) {
long nParams = layers[i].numParams();
System.out.println("Number of parameters in layer " + i + ": " + nParams);
totalNumParams += nParams;
}
System.out.println("Total number of network parameters: " + totalNumParams);
//Do training, and then generate and print samples from network
int miniBatchNumber = 0;
for (int i = 0; i < numEpochs; i++) {
while (iter.hasNext()) {
DataSet ds = iter.next();
net.fit(ds);
if (++miniBatchNumber % generateSamplesEveryNMinibatches == 0) {
System.out.println("--------------------");
System.out.println("Completed " + miniBatchNumber + " minibatches of size " + miniBatchSize + "x" + exampleLength + " characters");
System.out.println("Sampling characters from network given initialization \"" + (generationInitialization == null ? "" : generationInitialization) + "\"");
String[] samples = sampleCharactersFromNetwork(generationInitialization, net, iter, rng, nCharactersToSample, nSamplesToGenerate);
for (int j = 0; j < samples.length; j++) {
System.out.println("----- Sample " + j + " -----");
System.out.println(samples[j]);
System.out.println();
}
}
}
iter.reset(); //Reset iterator for another epoch
}
System.out.println("\n\nExample complete");
}
/**
* Downloads Shakespeare training data and stores it locally (temp directory). Then set up and return a simple
* DataSetIterator that does vectorization based on the text.
*
* @param miniBatchSize Number of text segments in each training mini-batch
* @param sequenceLength Number of characters in each text segment.
*/
public static CharacterIterator getShakespeareIterator(int miniBatchSize, int sequenceLength) throws Exception {
//The Complete Works of William Shakespeare
//5.3MB file in UTF-8 Encoding, ~5.4 million characters
//https://www.gutenberg.org/ebooks/100
String url = "https://s3.amazonaws.com/dl4j-distribution/pg100.txt";
String tempDir = System.getProperty("java.io.tmpdir");
String fileLocation = tempDir + "/Shakespeare.txt"; //Storage location from downloaded file
File f = new File(fileLocation);
if (!f.exists()) {
FileUtils.copyURLToFile(new URL(url), f);
System.out.println("File downloaded to " + f.getAbsolutePath());
} else {
System.out.println("Using existing text file at " + f.getAbsolutePath());
}
if (!f.exists()) throw new IOException("File does not exist: " + fileLocation); //Download problem?
char[] validCharacters = CharacterIterator.getMinimalCharacterSet(); //Which characters are allowed? Others will be removed
return new CharacterIterator(fileLocation, Charset.forName("UTF-8"),
miniBatchSize, sequenceLength, validCharacters, new Random(12345));
}
/**
* Generate a sample from the network, given an (optional, possibly null) initialization. Initialization
* can be used to 'prime' the RNN with a sequence you want to extend/continue.<br>
* Note that the initalization is used for all samples
*
* @param initialization String, may be null. If null, select a random character as initialization for all samples
* @param charactersToSample Number of characters to sample from network (excluding initialization)
* @param net MultiLayerNetwork with one or more GravesLSTM/RNN layers and a softmax output layer
* @param iter CharacterIterator. Used for going from indexes back to characters
*/
private static String[] sampleCharactersFromNetwork(String initialization, MultiLayerNetwork net,
CharacterIterator iter, Random rng, int charactersToSample, int numSamples) {
//Set up initialization. If no initialization: use a random character
if (initialization == null) {
initialization = String.valueOf(iter.getRandomCharacter());
}
//Create input for initialization
INDArray initializationInput = Nd4j.zeros(numSamples, iter.inputColumns(), initialization.length());
char[] init = initialization.toCharArray();
for (int i = 0; i < init.length; i++) {
int idx = iter.convertCharacterToIndex(init[i]);
for (int j = 0; j < numSamples; j++) {
initializationInput.putScalar(new int[]{j, idx, i}, 1.0f);
}
}
StringBuilder[] sb = new StringBuilder[numSamples];
for (int i = 0; i < numSamples; i++) sb[i] = new StringBuilder(initialization);
//Sample from network (and feed samples back into input) one character at a time (for all samples)
//Sampling is done in parallel here
net.rnnClearPreviousState();
INDArray output = net.rnnTimeStep(initializationInput);
output = output.tensorAlongDimension(output.size(2) - 1, 1, 0); //Gets the last time step output
for (int i = 0; i < charactersToSample; i++) {
//Set up next input (single time step) by sampling from previous output
INDArray nextInput = Nd4j.zeros(numSamples, iter.inputColumns());
//Output is a probability distribution. Sample from this for each example we want to generate, and add it to the new input
for (int s = 0; s < numSamples; s++) {
double[] outputProbDistribution = new double[iter.totalOutcomes()];
for (int j = 0; j < outputProbDistribution.length; j++)
outputProbDistribution[j] = output.getDouble(s, j);
int sampledCharacterIdx = sampleFromDistribution(outputProbDistribution, rng);
nextInput.putScalar(new int[]{s, sampledCharacterIdx}, 1.0f); //Prepare next time step input
sb[s].append(iter.convertIndexToCharacter(sampledCharacterIdx)); //Add sampled character to StringBuilder (human readable output)
}
output = net.rnnTimeStep(nextInput); //Do one time step of forward pass
}
String[] out = new String[numSamples];
for (int i = 0; i < numSamples; i++) out[i] = sb[i].toString();
return out;
}
/**
* Given a probability distribution over discrete classes, sample from the distribution
* and return the generated class index.
*
* @param distribution Probability distribution over classes. Must sum to 1.0
*/
public static int sampleFromDistribution(double[] distribution, Random rng) {
double d = rng.nextDouble();
double sum = 0.0;
for (int i = 0; i < distribution.length; i++) {
sum += distribution[i];
if (d <= sum) return i;
}
//Should never happen if distribution is a valid probability distribution
throw new IllegalArgumentException("Distribution is invalid? d=" + d + ", sum=" + sum);
}
}
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