Java example source code file (ManualTests.java)
The ManualTests.java Java example source codepackage org.deeplearning4j.ui;
import org.canova.api.util.ClassPathResource;
import org.canova.image.loader.LFWLoader;
import org.nd4j.linalg.dataset.api.iterator.DataSetIterator;
import org.deeplearning4j.datasets.iterator.impl.LFWDataSetIterator;
import org.deeplearning4j.datasets.iterator.impl.MnistDataSetIterator;
import org.deeplearning4j.eval.Evaluation;
import org.deeplearning4j.models.embeddings.reader.impl.BasicModelUtils;
import org.deeplearning4j.models.word2vec.VocabWord;
import org.deeplearning4j.models.word2vec.Word2Vec;
import org.deeplearning4j.nn.api.OptimizationAlgorithm;
import org.deeplearning4j.nn.conf.GradientNormalization;
import org.deeplearning4j.nn.conf.MultiLayerConfiguration;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.deeplearning4j.nn.conf.Updater;
import org.deeplearning4j.nn.conf.layers.*;
import org.deeplearning4j.nn.conf.layers.setup.ConvolutionLayerSetup;
import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
import org.deeplearning4j.nn.weights.WeightInit;
import org.deeplearning4j.optimize.api.IterationListener;
import org.deeplearning4j.optimize.listeners.ScoreIterationListener;
import org.deeplearning4j.text.sentenceiterator.BasicLineIterator;
import org.deeplearning4j.text.sentenceiterator.SentenceIterator;
import org.deeplearning4j.text.tokenization.tokenizer.preprocessor.CommonPreprocessor;
import org.deeplearning4j.text.tokenization.tokenizerfactory.DefaultTokenizerFactory;
import org.deeplearning4j.text.tokenization.tokenizerfactory.TokenizerFactory;
import org.deeplearning4j.ui.activation.UpdateActivationIterationListener;
import org.deeplearning4j.ui.flow.FlowIterationListener;
import org.deeplearning4j.ui.weights.HistogramIterationListener;
import org.deeplearning4j.ui.weights.ConvolutionalIterationListener;
import org.junit.Ignore;
import org.junit.Test;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.dataset.DataSet;
import org.nd4j.linalg.dataset.SplitTestAndTrain;
import org.nd4j.linalg.factory.Nd4j;
import org.nd4j.linalg.lossfunctions.LossFunctions;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import javax.imageio.ImageIO;
import java.awt.image.BufferedImage;
import java.io.File;
import java.io.IOException;
import java.util.*;
/**
* Test environment for building/debugging UI.
*
* Please, do NOT remove @Ignore annotation
*
* @author raver119@gmail.com
*/
@Ignore
public class ManualTests {
private static Logger log = LoggerFactory.getLogger(ManualTests.class);
@Test
public void testLaunch() throws Exception {
UiServer server = UiServer.getInstance();
System.out.println("http://localhost:" + server.getPort()+ "/");
Thread.sleep(10000000000L);
new ScoreIterationListener(100);
}
@Test
public void testHistograms() throws Exception {
final int numRows = 28;
final int numColumns = 28;
int outputNum = 10;
int numSamples = 60000;
int batchSize = 100;
int iterations = 10;
int seed = 123;
int listenerFreq = batchSize / 5;
log.info("Load data....");
DataSetIterator iter = new MnistDataSetIterator(batchSize,numSamples,true);
log.info("Build model....");
MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()
.seed(seed)
.gradientNormalization(GradientNormalization.ClipElementWiseAbsoluteValue)
.gradientNormalizationThreshold(1.0)
.iterations(iterations)
.momentum(0.5)
.momentumAfter(Collections.singletonMap(3, 0.9))
.optimizationAlgo(OptimizationAlgorithm.CONJUGATE_GRADIENT)
.list()
.layer(0, new RBM.Builder().nIn(numRows*numColumns).nOut(500)
.weightInit(WeightInit.XAVIER).lossFunction(LossFunctions.LossFunction.RMSE_XENT)
.visibleUnit(RBM.VisibleUnit.BINARY)
.hiddenUnit(RBM.HiddenUnit.BINARY)
.build())
.layer(1, new RBM.Builder().nIn(500).nOut(250)
.weightInit(WeightInit.XAVIER).lossFunction(LossFunctions.LossFunction.RMSE_XENT)
.visibleUnit(RBM.VisibleUnit.BINARY)
.hiddenUnit(RBM.HiddenUnit.BINARY)
.build())
.layer(2, new RBM.Builder().nIn(250).nOut(200)
.weightInit(WeightInit.XAVIER).lossFunction(LossFunctions.LossFunction.RMSE_XENT)
.visibleUnit(RBM.VisibleUnit.BINARY)
.hiddenUnit(RBM.HiddenUnit.BINARY)
.build())
.layer(3, new OutputLayer.Builder(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD).activation("softmax")
.nIn(200).nOut(outputNum).build())
.pretrain(true).backprop(false)
.build();
;
UiServer server = UiServer.getInstance();
UiConnectionInfo connectionInfo = server.getConnectionInfo();
connectionInfo.setSessionId("my session here");
MultiLayerNetwork model = new MultiLayerNetwork(conf);
model.init();
model.setListeners(Arrays.asList((IterationListener) new ScoreIterationListener(listenerFreq),(IterationListener) new HistogramIterationListener(connectionInfo, listenerFreq), new FlowIterationListener(connectionInfo, listenerFreq)));
log.info("Train model....");
model.fit(iter); // achieves end to end pre-training
log.info("Evaluate model....");
Evaluation eval = new Evaluation(outputNum);
DataSetIterator testIter = new MnistDataSetIterator(100,10000);
while(testIter.hasNext()) {
DataSet testMnist = testIter.next();
INDArray predict2 = model.output(testMnist.getFeatureMatrix());
eval.eval(testMnist.getLabels(), predict2);
}
log.info(eval.stats());
log.info("****************Example finished********************");
}
@Test
public void testRenderWeights() throws Exception {
final int numRows = 28;
final int numColumns = 28;
int outputNum = 10;
int numSamples = 60000;
int batchSize = 100;
int iterations = 10;
int seed = 123;
int listenerFreq = batchSize / 5;
log.info("Load data....");
DataSetIterator iter = new MnistDataSetIterator(batchSize,numSamples,true);
log.info("Build model....");
MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()
.seed(seed)
.gradientNormalization(GradientNormalization.ClipElementWiseAbsoluteValue)
.gradientNormalizationThreshold(1.0)
.iterations(iterations)
.momentum(0.5)
.momentumAfter(Collections.singletonMap(3, 0.9))
.optimizationAlgo(OptimizationAlgorithm.CONJUGATE_GRADIENT)
.list()
.layer(0, new RBM.Builder().nIn(numRows*numColumns).nOut(500)
.weightInit(WeightInit.XAVIER).lossFunction(LossFunctions.LossFunction.RMSE_XENT)
.visibleUnit(RBM.VisibleUnit.BINARY)
.hiddenUnit(RBM.HiddenUnit.BINARY)
.build())
.layer(1, new RBM.Builder().nIn(500).nOut(250)
.weightInit(WeightInit.XAVIER).lossFunction(LossFunctions.LossFunction.RMSE_XENT)
.visibleUnit(RBM.VisibleUnit.BINARY)
.hiddenUnit(RBM.HiddenUnit.BINARY)
.build())
.layer(2, new RBM.Builder().nIn(250).nOut(200)
.weightInit(WeightInit.XAVIER).lossFunction(LossFunctions.LossFunction.RMSE_XENT)
.visibleUnit(RBM.VisibleUnit.BINARY)
.hiddenUnit(RBM.HiddenUnit.BINARY)
.build())
.layer(3, new OutputLayer.Builder(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD).activation("softmax")
.nIn(200).nOut(outputNum).build())
.pretrain(true).backprop(false)
.build();
;
MultiLayerNetwork model = new MultiLayerNetwork(conf);
model.init();
model.setListeners(Arrays.asList((IterationListener) new ScoreIterationListener(listenerFreq),(IterationListener) new UpdateActivationIterationListener(listenerFreq)));
log.info("Train model....");
model.fit(iter); // achieves end to end pre-training
log.info("Evaluate model....");
Evaluation eval = new Evaluation(outputNum);
DataSetIterator testIter = new MnistDataSetIterator(100,10000);
while(testIter.hasNext()) {
DataSet testMnist = testIter.next();
INDArray predict2 = model.output(testMnist.getFeatureMatrix());
eval.eval(testMnist.getLabels(), predict2);
}
log.info(eval.stats());
log.info("****************Example finished********************");
}
/**
* This test is for manual execution only, since it's here just to get working CNN and visualize it's layers
*
* @throws Exception
*/
@Test
public void testCNNActivationsVisualization() throws Exception {
final int numRows = 40;
final int numColumns = 40;
int nChannels = 3;
int outputNum = LFWLoader.NUM_LABELS;
int numSamples = LFWLoader.NUM_IMAGES;
boolean useSubset = false;
int batchSize = 200;// numSamples/10;
int iterations = 5;
int splitTrainNum = (int) (batchSize*.8);
int seed = 123;
int listenerFreq = iterations/5;
DataSet lfwNext;
SplitTestAndTrain trainTest;
DataSet trainInput;
List<INDArray> testInput = new ArrayList<>();
List<INDArray> testLabels = new ArrayList<>();
log.info("Load data....");
DataSetIterator lfw = new LFWDataSetIterator(batchSize, numSamples, new int[] {numRows, numColumns, nChannels}, outputNum, useSubset, true, 1.0, new Random(seed));
log.info("Build model....");
MultiLayerConfiguration.Builder builder = new NeuralNetConfiguration.Builder()
.seed(seed)
.iterations(iterations)
.activation("relu")
.weightInit(WeightInit.XAVIER)
.gradientNormalization(GradientNormalization.RenormalizeL2PerLayer)
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
.learningRate(0.01)
.momentum(0.9)
.regularization(true)
.updater(Updater.ADAGRAD)
.useDropConnect(true)
.list()
.layer(0, new ConvolutionLayer.Builder(4, 4)
.name("cnn1")
.nIn(nChannels)
.stride(1, 1)
.nOut(20)
.build())
.layer(1, new SubsamplingLayer.Builder(SubsamplingLayer.PoolingType.MAX, new int[]{2, 2})
.name("pool1")
.build())
.layer(2, new ConvolutionLayer.Builder(3, 3)
.name("cnn2")
.stride(1,1)
.nOut(40)
.build())
.layer(3, new SubsamplingLayer.Builder(SubsamplingLayer.PoolingType.MAX, new int[]{2, 2})
.name("pool2")
.build())
.layer(4, new ConvolutionLayer.Builder(3, 3)
.name("cnn3")
.stride(1,1)
.nOut(60)
.build())
.layer(5, new SubsamplingLayer.Builder(SubsamplingLayer.PoolingType.MAX, new int[]{2, 2})
.name("pool3")
.build())
.layer(6, new ConvolutionLayer.Builder(2, 2)
.name("cnn3")
.stride(1,1)
.nOut(80)
.build())
.layer(7, new DenseLayer.Builder()
.name("ffn1")
.nOut(160)
.dropOut(0.5)
.build())
.layer(8, new OutputLayer.Builder(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD)
.nOut(outputNum)
.activation("softmax")
.build())
.backprop(true).pretrain(false);
new ConvolutionLayerSetup(builder,numRows,numColumns,nChannels);
MultiLayerNetwork model = new MultiLayerNetwork(builder.build());
model.init();
log.info("Train model....");
model.setListeners(Arrays.asList(new ScoreIterationListener(listenerFreq), new ConvolutionalIterationListener(listenerFreq)));
while(lfw.hasNext()) {
lfwNext = lfw.next();
lfwNext.scale();
trainTest = lfwNext.splitTestAndTrain(splitTrainNum, new Random(seed)); // train set that is the result
trainInput = trainTest.getTrain(); // get feature matrix and labels for training
testInput.add(trainTest.getTest().getFeatureMatrix());
testLabels.add(trainTest.getTest().getLabels());
model.fit(trainInput);
}
log.info("Evaluate model....");
Evaluation eval = new Evaluation(lfw.getLabels());
for(int i = 0; i < testInput.size(); i++) {
INDArray output = model.output(testInput.get(i));
eval.eval(testLabels.get(i), output);
}
INDArray output = model.output(testInput.get(0));
eval.eval(testLabels.get(0), output);
log.info(eval.stats());
log.info("****************Example finished********************");
}
@Test
public void testFlowActivationsMLN1() throws Exception {
final int numRows = 40;
final int numColumns = 40;
int nChannels = 3;
int outputNum = LFWLoader.NUM_LABELS;
int numSamples = LFWLoader.NUM_IMAGES;
boolean useSubset = false;
int batchSize = 200;// numSamples/10;
int iterations = 5;
int splitTrainNum = (int) (batchSize*.8);
int seed = 123;
int listenerFreq = iterations/5;
DataSet lfwNext;
SplitTestAndTrain trainTest;
DataSet trainInput;
List<INDArray> testInput = new ArrayList<>();
List<INDArray> testLabels = new ArrayList<>();
log.info("Load data....");
DataSetIterator lfw = new LFWDataSetIterator(batchSize, numSamples, new int[] {numRows, numColumns, nChannels}, outputNum, useSubset, true, 1.0, new Random(seed));
log.info("Build model....");
MultiLayerConfiguration.Builder builder = new NeuralNetConfiguration.Builder()
.seed(seed)
.iterations(iterations)
.activation("relu")
.weightInit(WeightInit.XAVIER)
.gradientNormalization(GradientNormalization.RenormalizeL2PerLayer)
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
.learningRate(0.01)
.momentum(0.9)
.regularization(true)
.updater(Updater.ADAGRAD)
.useDropConnect(true)
.list()
.layer(0, new ConvolutionLayer.Builder(4, 4)
.name("cnn1")
.nIn(nChannels)
.stride(1, 1)
.nOut(20)
.build())
.layer(1, new SubsamplingLayer.Builder(SubsamplingLayer.PoolingType.MAX, new int[]{2, 2})
.name("pool1")
.build())
.layer(2, new ConvolutionLayer.Builder(3, 3)
.name("cnn2")
.stride(1,1)
.nOut(40)
.build())
.layer(3, new SubsamplingLayer.Builder(SubsamplingLayer.PoolingType.MAX, new int[]{2, 2})
.name("pool2")
.build())
.layer(4, new ConvolutionLayer.Builder(3, 3)
.name("cnn3")
.stride(1,1)
.nOut(60)
.build())
.layer(5, new SubsamplingLayer.Builder(SubsamplingLayer.PoolingType.MAX, new int[]{2, 2})
.name("pool3")
.build())
.layer(6, new ConvolutionLayer.Builder(2, 2)
.name("cnn3")
.stride(1,1)
.nOut(80)
.build())
.layer(7, new DenseLayer.Builder()
.name("ffn1")
.nOut(160)
.dropOut(0.5)
.build())
.layer(8, new OutputLayer.Builder(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD)
.nOut(outputNum)
.activation("softmax")
.build())
.backprop(true).pretrain(false);
new ConvolutionLayerSetup(builder,numRows,numColumns,nChannels);
MultiLayerNetwork model = new MultiLayerNetwork(builder.build());
model.init();
log.info("Train model....");
model.setListeners(Arrays.asList(new ScoreIterationListener(listenerFreq), new FlowIterationListener(listenerFreq)));
while(lfw.hasNext()) {
lfwNext = lfw.next();
lfwNext.scale();
trainTest = lfwNext.splitTestAndTrain(splitTrainNum, new Random(seed)); // train set that is the result
trainInput = trainTest.getTrain(); // get feature matrix and labels for training
testInput.add(trainTest.getTest().getFeatureMatrix());
testLabels.add(trainTest.getTest().getLabels());
model.fit(trainInput);
}
log.info("Evaluate model....");
Evaluation eval = new Evaluation(lfw.getLabels());
for(int i = 0; i < testInput.size(); i++) {
INDArray output = model.output(testInput.get(i));
eval.eval(testLabels.get(i), output);
}
INDArray output = model.output(testInput.get(0));
eval.eval(testLabels.get(0), output);
log.info(eval.stats());
log.info("****************Example finished********************");
}
@Test
public void testFlowActivationsCG1() throws Exception {
}
@Test
public void testWord2VecPlot() throws Exception {
File inputFile = new ClassPathResource("/big/raw_sentences.txt").getFile();
SentenceIterator iter = new BasicLineIterator(inputFile.getAbsolutePath());
TokenizerFactory t = new DefaultTokenizerFactory();
t.setTokenPreProcessor(new CommonPreprocessor());
Word2Vec vec = new Word2Vec.Builder()
.minWordFrequency(5)
.iterations(2)
.batchSize(1000)
.learningRate(0.025)
.layerSize(100)
.seed(42)
.sampling(0)
.negativeSample(0)
.windowSize(5)
.modelUtils(new BasicModelUtils<VocabWord>())
.useAdaGrad(false)
.iterate(iter)
.workers(10)
.tokenizerFactory(t)
.build();
vec.fit();
UiConnectionInfo connectionInfo = UiServer.getInstance().getConnectionInfo();
vec.getLookupTable().plotVocab(100, connectionInfo);
Thread.sleep(10000000000L);
}
@Test
public void testImage() throws Exception {
INDArray array = Nd4j.create(11,13);
for (int i = 0; i < array.rows(); i++) {
array.putRow(i, Nd4j.create(new double[]{0.0f, 0.1f, 0.2f, 0.3f, 0.4f, 0.5f, 0.6f, 0.7f, 0.8f, 0.9f, 1.0f, 1.2f, 1.3f}));
}
writeImage(array, new File("test.png"));
}
private void writeImage(INDArray array, File file) {
// BufferedImage image = ImageLoader.toImage(array);
log.info("Array.rank(): " + array.rank());
log.info("Size(-1): " + array.size(-1));
log.info("Size(-2): " + array.size(-2));
BufferedImage imageToRender = new BufferedImage(array.columns(),array.rows(),BufferedImage.TYPE_BYTE_GRAY);
for( int x = 0; x < array.columns(); x++ ){
for (int y = 0; y < array.rows(); y++ ) {
log.info("x: " + (x) + " y: " + y);
imageToRender.getRaster().setSample(x, y, 0, (int) (255 * array.getRow(y).getDouble(x)));
}
}
try {
ImageIO.write(imageToRender, "png", file);
} catch (IOException e) {
e.printStackTrace();
}
}
}
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