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MultiLayerTest.java
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MultiLayerTest.java
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/*-
*
* * Copyright 2015 Skymind,Inc.
* *
* * Licensed under the Apache License, Version 2.0 (the "License");
* * you may not use this file except in compliance with the License.
* * You may obtain a copy of the License at
* *
* * http://www.apache.org/licenses/LICENSE-2.0
* *
* * Unless required by applicable law or agreed to in writing, software
* * distributed under the License is distributed on an "AS IS" BASIS,
* * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* * See the License for the specific language governing permissions and
* * limitations under the License.
*
*/
package org.deeplearning4j.nn.multilayer;
import org.nd4j.linalg.primitives.Pair;
import org.deeplearning4j.datasets.iterator.impl.IrisDataSetIterator;
import org.deeplearning4j.datasets.iterator.impl.MnistDataSetIterator;
import org.deeplearning4j.eval.Evaluation;
import org.deeplearning4j.exception.DL4JException;
import org.deeplearning4j.nn.api.Layer;
import org.deeplearning4j.nn.api.OptimizationAlgorithm;
import org.deeplearning4j.nn.conf.*;
import org.deeplearning4j.nn.conf.distribution.NormalDistribution;
import org.deeplearning4j.nn.conf.distribution.UniformDistribution;
import org.deeplearning4j.nn.conf.inputs.InputType;
import org.deeplearning4j.nn.conf.layers.*;
import org.deeplearning4j.nn.conf.preprocessor.CnnToFeedForwardPreProcessor;
import org.deeplearning4j.nn.conf.preprocessor.FeedForwardToRnnPreProcessor;
import org.deeplearning4j.nn.conf.preprocessor.RnnToCnnPreProcessor;
import org.deeplearning4j.nn.gradient.DefaultGradient;
import org.deeplearning4j.nn.gradient.Gradient;
import org.deeplearning4j.nn.layers.BaseOutputLayer;
import org.deeplearning4j.nn.params.DefaultParamInitializer;
import org.deeplearning4j.nn.params.PretrainParamInitializer;
import org.deeplearning4j.nn.transferlearning.TransferLearning;
import org.deeplearning4j.nn.weights.WeightInit;
import org.deeplearning4j.optimize.api.IterationListener;
import org.deeplearning4j.optimize.listeners.ScoreIterationListener;
import org.deeplearning4j.util.ModelSerializer;
import org.junit.Ignore;
import org.junit.Test;
import org.nd4j.linalg.activations.Activation;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.dataset.DataSet;
import org.nd4j.linalg.dataset.SplitTestAndTrain;
import org.nd4j.linalg.dataset.api.iterator.DataSetIterator;
import org.nd4j.linalg.factory.Nd4j;
import org.nd4j.linalg.heartbeat.Heartbeat;
import org.nd4j.linalg.heartbeat.reports.Environment;
import org.nd4j.linalg.heartbeat.reports.Event;
import org.nd4j.linalg.heartbeat.reports.Task;
import org.nd4j.linalg.heartbeat.utils.EnvironmentUtils;
import org.nd4j.linalg.heartbeat.utils.TaskUtils;
import org.nd4j.linalg.lossfunctions.LossFunctions;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import java.io.ByteArrayInputStream;
import java.io.ByteArrayOutputStream;
import java.io.IOException;
import java.io.ObjectOutputStream;
import java.util.*;
import static org.junit.Assert.*;
/**
* Created by agibsonccc on 12/27/14.
*/
public class MultiLayerTest {
private static final Logger log = LoggerFactory.getLogger(MultiLayerTest.class);
@Test
public void testSetParams() {
Nd4j.MAX_ELEMENTS_PER_SLICE = Integer.MAX_VALUE;
Nd4j.MAX_SLICES_TO_PRINT = Integer.MAX_VALUE;
MultiLayerConfiguration conf =
new NeuralNetConfiguration.Builder()
.list().layer(0,
new RBM.Builder(RBM.HiddenUnit.RECTIFIED,
RBM.VisibleUnit.GAUSSIAN).nIn(4).nOut(3)
.activation(Activation.TANH)
.build())
.layer(1, new RBM.Builder(RBM.HiddenUnit.GAUSSIAN, RBM.VisibleUnit.GAUSSIAN)
.nIn(3).nOut(2).build())
.build();
MultiLayerNetwork network3 = new MultiLayerNetwork(conf);
network3.init();
INDArray params = network3.params();
INDArray weights = network3.getLayer(0).getParam(DefaultParamInitializer.WEIGHT_KEY).dup();
INDArray bias = network3.getLayer(0).getParam(DefaultParamInitializer.BIAS_KEY).dup();
network3.setParameters(params);
assertEquals(weights, network3.getLayer(0).getParam(DefaultParamInitializer.WEIGHT_KEY));
assertEquals(bias, network3.getLayer(0).getParam(DefaultParamInitializer.BIAS_KEY));
INDArray params4 = network3.params();
assertEquals(params, params4);
}
@Test
public void testBatchNorm() {
Nd4j.getRandom().setSeed(123);
MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()
.optimizationAlgo(OptimizationAlgorithm.LINE_GRADIENT_DESCENT).iterations(5).seed(123).list()
.layer(0, new DenseLayer.Builder().nIn(4).nOut(3).weightInit(WeightInit.XAVIER)
.activation(Activation.TANH).build())
.layer(1, new DenseLayer.Builder().nIn(3).nOut(2).weightInit(WeightInit.XAVIER)
.activation(Activation.TANH).build())
.layer(2, new BatchNormalization.Builder().nOut(2).build())
.layer(3, new org.deeplearning4j.nn.conf.layers.OutputLayer.Builder(
LossFunctions.LossFunction.MCXENT).weightInit(WeightInit.XAVIER)
.activation(Activation.SOFTMAX).nIn(2).nOut(3).build())
.backprop(true).pretrain(false).build();
MultiLayerNetwork network = new MultiLayerNetwork(conf);
network.init();
network.setListeners(new ScoreIterationListener(1));
DataSetIterator iter = new IrisDataSetIterator(150, 150);
DataSet next = iter.next();
next.normalizeZeroMeanZeroUnitVariance();
SplitTestAndTrain trainTest = next.splitTestAndTrain(110);
network.setLabels(trainTest.getTrain().getLabels());
network.init();
network.fit(trainTest.getTrain());
}
@Test
public void testBackProp() {
Nd4j.getRandom().setSeed(123);
MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()
.optimizationAlgo(OptimizationAlgorithm.LINE_GRADIENT_DESCENT).iterations(5).seed(123).list()
.layer(0, new DenseLayer.Builder().nIn(4).nOut(3).weightInit(WeightInit.XAVIER)
.activation(Activation.TANH).build())
.layer(1, new DenseLayer.Builder().nIn(3).nOut(2).weightInit(WeightInit.XAVIER)
.activation(Activation.TANH).build())
.layer(2, new org.deeplearning4j.nn.conf.layers.OutputLayer.Builder(
LossFunctions.LossFunction.MCXENT).weightInit(WeightInit.XAVIER)
.activation(Activation.SOFTMAX).nIn(2).nOut(3).build())
.backprop(true).pretrain(false).build();
MultiLayerNetwork network = new MultiLayerNetwork(conf);
network.init();
network.setListeners(new ScoreIterationListener(1));
DataSetIterator iter = new IrisDataSetIterator(150, 150);
DataSet next = iter.next();
next.normalizeZeroMeanZeroUnitVariance();
SplitTestAndTrain trainTest = next.splitTestAndTrain(110);
network.setInput(trainTest.getTrain().getFeatureMatrix());
network.setLabels(trainTest.getTrain().getLabels());
network.init();
network.fit(trainTest.getTrain());
DataSet test = trainTest.getTest();
Evaluation eval = new Evaluation();
INDArray output = network.output(test.getFeatureMatrix());
eval.eval(test.getLabels(), output);
log.info("Score " + eval.stats());
}
@Test
public void testDbn() throws Exception {
Nd4j.MAX_SLICES_TO_PRINT = -1;
Nd4j.MAX_ELEMENTS_PER_SLICE = -1;
MultiLayerConfiguration conf =
new NeuralNetConfiguration.Builder().iterations(100).momentum(0.9)
.optimizationAlgo(OptimizationAlgorithm.LBFGS).regularization(true).l2(2e-4)
.list().layer(0,
new RBM.Builder(RBM.HiddenUnit.GAUSSIAN,
RBM.VisibleUnit.GAUSSIAN).nIn(4).nOut(3)
.weightInit(WeightInit.DISTRIBUTION)
.dist(new UniformDistribution(0,
1))
.activation(Activation.TANH)
.lossFunction(LossFunctions.LossFunction.KL_DIVERGENCE)
.build())
.layer(1, new org.deeplearning4j.nn.conf.layers.OutputLayer.Builder(
LossFunctions.LossFunction.MCXENT).nIn(3).nOut(3)
.weightInit(WeightInit.DISTRIBUTION)
.dist(new UniformDistribution(0, 1))
.activation(Activation.SOFTMAX).build())
.build();
MultiLayerNetwork d = new MultiLayerNetwork(conf);
DataSetIterator iter = new IrisDataSetIterator(150, 150);
DataSet next = iter.next();
Nd4j.writeTxt(next.getFeatureMatrix(), "iris.txt", "\t");
next.normalizeZeroMeanZeroUnitVariance();
SplitTestAndTrain testAndTrain = next.splitTestAndTrain(110);
DataSet train = testAndTrain.getTrain();
d.fit(train);
DataSet test = testAndTrain.getTest();
Evaluation eval = new Evaluation();
INDArray output = d.output(test.getFeatureMatrix());
eval.eval(test.getLabels(), output);
log.info("Score " + eval.stats());
}
@Test
public void testGradientWithAsList() {
MultiLayerNetwork net1 = new MultiLayerNetwork(getConf());
MultiLayerNetwork net2 = new MultiLayerNetwork(getConf());
net1.init();
net2.init();
DataSet x1 = new IrisDataSetIterator(1, 150).next();
DataSet all = new IrisDataSetIterator(150, 150).next();
DataSet x2 = all.asList().get(0);
//x1 and x2 contain identical data
assertArrayEquals(asFloat(x1.getFeatureMatrix()), asFloat(x2.getFeatureMatrix()), 0.0f);
assertArrayEquals(asFloat(x1.getLabels()), asFloat(x2.getLabels()), 0.0f);
assertEquals(x1, x2);
//Set inputs/outputs so gradient can be calculated:
net1.feedForward(x1.getFeatureMatrix());
net2.feedForward(x2.getFeatureMatrix());
((BaseOutputLayer) net1.getLayer(1)).setLabels(x1.getLabels());
((BaseOutputLayer) net2.getLayer(1)).setLabels(x2.getLabels());
net1.gradient();
net2.gradient();
}
/**
* This test intended only to test activateSelectedLayers method, it does not involves fully-working AutoEncoder.
*/
@Test
public void testSelectedActivations() {
// Train DeepAutoEncoder on very limited trainset
final int numRows = 28;
final int numColumns = 28;
int seed = 123;
int numSamples = 3;
int iterations = 1;
int listenerFreq = iterations / 5;
log.info("Load data....");
float[][] trainingData = new float[numSamples][numColumns * numRows];
Arrays.fill(trainingData[0], 0.95f);
Arrays.fill(trainingData[1], 0.5f);
Arrays.fill(trainingData[2], 0.05f);
log.info("Build model....");
MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().seed(seed).iterations(iterations)
.optimizationAlgo(OptimizationAlgorithm.LINE_GRADIENT_DESCENT).list()
.layer(0, new RBM.Builder().nIn(numRows * numColumns).nOut(1000)
.lossFunction(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD).build())
.layer(1, new RBM.Builder().nIn(1000).nOut(500)
.lossFunction(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD).build())
.layer(2, new RBM.Builder().nIn(500).nOut(250)
.lossFunction(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD).build())
.layer(3, new RBM.Builder().nIn(250).nOut(100)
.lossFunction(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD).build())
.layer(4, new RBM.Builder().nIn(100).nOut(30)
.lossFunction(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD).build()) //encoding stops
.layer(5, new RBM.Builder().nIn(30).nOut(100)
.lossFunction(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD).build()) //decoding starts
.layer(6, new RBM.Builder().nIn(100).nOut(250)
.lossFunction(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD).build())
.layer(7, new RBM.Builder().nIn(250).nOut(500)
.lossFunction(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD).build())
.layer(8, new RBM.Builder().nIn(500).nOut(1000)
.lossFunction(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD).build())
.layer(9, new OutputLayer.Builder(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD).nIn(1000)
.nOut(numRows * numColumns).build())
.pretrain(true).backprop(true).build();
MultiLayerNetwork model = new MultiLayerNetwork(conf);
model.init();
model.setListeners(Arrays.asList((IterationListener) new ScoreIterationListener(listenerFreq)));
log.info("Train model....");
int cnt = 0;
while (cnt < numSamples) {
INDArray input = Nd4j.create(trainingData[cnt]);
model.fit(new DataSet(input, input));
cnt++;
}
// Make two separate selective calls
log.info("Testing full cycle...");
List<INDArray> comparableResult = model.feedForward(Nd4j.create(trainingData[0]));
INDArray encodeResult = model.activateSelectedLayers(0, 4, Nd4j.create(trainingData[0]));
log.info("Compare feedForward results with selectedActivation");
assertEquals(comparableResult.get(5), encodeResult);
INDArray decodeResults = model.activateSelectedLayers(5, 9, encodeResult);
log.info("Decode results: " + decodeResults.columns() + " " + decodeResults);
log.info("Comparable results: " + comparableResult.get(10).columns() + " " + comparableResult.get(10));
assertEquals(comparableResult.get(10), decodeResults);
}
private static MultiLayerConfiguration getConf() {
MultiLayerConfiguration conf =
new NeuralNetConfiguration.Builder().seed(12345L)
.list().layer(0,
new RBM.Builder(RBM.HiddenUnit.RECTIFIED,
RBM.VisibleUnit.GAUSSIAN).nIn(4).nOut(3)
.weightInit(WeightInit.DISTRIBUTION)
.dist(new NormalDistribution(0,
1))
.build())
.layer(1, new org.deeplearning4j.nn.conf.layers.OutputLayer.Builder(
LossFunctions.LossFunction.MCXENT)
.activation(Activation.SOFTMAX).nIn(3).nOut(3)
.weightInit(WeightInit.DISTRIBUTION)
.dist(new NormalDistribution(0, 1)).build())
.build();
return conf;
}
public static float[] asFloat(INDArray arr) {
int len = arr.length();
float[] f = new float[len];
for (int i = 0; i < len; i++)
f[i] = arr.getFloat(i);
return f;
}
@Test
public void testFeedForwardToLayer() {
int nIn = 30;
int nOut = 25;
MultiLayerConfiguration conf =
new NeuralNetConfiguration.Builder().optimizationAlgo(OptimizationAlgorithm.CONJUGATE_GRADIENT)
.iterations(5).learningRate(1e-3)
.list().layer(
0, new RBM.Builder(RBM.HiddenUnit.RECTIFIED,
RBM.VisibleUnit.GAUSSIAN).nIn(nIn)
.nOut(600)
.weightInit(WeightInit.DISTRIBUTION)
.dist(new NormalDistribution(0,
1e-5))
.lossFunction(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD)
.build())
.layer(1, new RBM.Builder(RBM.HiddenUnit.RECTIFIED, RBM.VisibleUnit.GAUSSIAN)
.nIn(600).nOut(250).weightInit(WeightInit.DISTRIBUTION)
.dist(new NormalDistribution(0, 1e-5))
.lossFunction(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD)
.build())
.layer(2, new RBM.Builder(RBM.HiddenUnit.RECTIFIED, RBM.VisibleUnit.GAUSSIAN)
.nIn(250).nOut(100).weightInit(WeightInit.DISTRIBUTION)
.dist(new NormalDistribution(0, 1e-5))
.lossFunction(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD)
.build())
.layer(3, new org.deeplearning4j.nn.conf.layers.OutputLayer.Builder(
LossFunctions.LossFunction.MCXENT).nIn(100).nOut(25)
.weightInit(WeightInit.DISTRIBUTION)
.dist(new NormalDistribution(0, 1e-5)).build())
.build();
MultiLayerNetwork network = new MultiLayerNetwork(conf);
network.init();
INDArray input = Nd4j.rand(5, nIn);
List<INDArray> activations = network.feedForward(input);
assertEquals(5, activations.size()); //4 layers + input
List<INDArray> activationsAll = network.feedForwardToLayer(3, input);
assertEquals(activations, activationsAll);
for (int i = 3; i >= 0; i--) {
List<INDArray> activationsPartial = network.feedForwardToLayer(i, input);
assertEquals(i + 2, activationsPartial.size()); //i+2: for layer 3: input + activations of {0,1,2,3} -> 5 total = 3+2
for (int j = 0; j <= i; j++) {
INDArray exp = activationsAll.get(j);
INDArray act = activationsPartial.get(j);
assertEquals(exp, act);
}
}
}
@Test
public void testBackpropGradient() {
//Testing: MultiLayerNetwork.backpropGradient()
//i.e., specifically without an output layer
int nIn = 10;
int nOut = 40;
int miniBatch = 5;
MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()
.updater(org.deeplearning4j.nn.conf.Updater.SGD).learningRate(0.1).list()
.layer(0, new DenseLayer.Builder().nIn(nIn).nOut(20).activation(Activation.RELU)
.weightInit(WeightInit.XAVIER).build())
.layer(1, new DenseLayer.Builder().nIn(20).nOut(30).activation(Activation.RELU)
.weightInit(WeightInit.XAVIER).build())
.layer(2, new DenseLayer.Builder().nIn(30).nOut(nOut).activation(Activation.RELU)
.weightInit(WeightInit.XAVIER).build())
.build();
MultiLayerNetwork net = new MultiLayerNetwork(conf);
net.init();
Nd4j.getRandom().setSeed(12345);
INDArray eps = Nd4j.rand(miniBatch, nOut);
INDArray input = Nd4j.rand(miniBatch, nIn);
net.feedForward(input); //Need to feed forward before backprop
Pair<Gradient, INDArray> pair = net.backpropGradient(eps);
INDArray epsOut = pair.getSecond();
assertNotNull(epsOut);
assertArrayEquals(new int[] {miniBatch, nIn}, epsOut.shape());
Gradient g = pair.getFirst();
Map<String, INDArray> gradMap = g.gradientForVariable();
assertEquals(6, gradMap.size()); //3 layers, weight + bias gradients for each
String[] expKeys = {"0_" + DefaultParamInitializer.WEIGHT_KEY, "0_" + DefaultParamInitializer.BIAS_KEY,
"1_" + DefaultParamInitializer.WEIGHT_KEY, "2_" + DefaultParamInitializer.BIAS_KEY,
"2_" + DefaultParamInitializer.WEIGHT_KEY, "2_" + DefaultParamInitializer.BIAS_KEY};
Set<String> keys = gradMap.keySet();
for (String s : expKeys) {
assertTrue(keys.contains(s));
}
/*
System.out.println(pair);
//Use updater to go from raw gradients -> updates
//Apply learning rate, gradient clipping, adagrad/momentum/rmsprop etc
Updater updater = UpdaterCreator.getUpdater(net);
updater.update(net, g, 0, miniBatch);
StepFunction stepFunction = new NegativeGradientStepFunction();
INDArray params = net.params();
System.out.println(Arrays.toString(params.get(NDArrayIndex.all(), NDArrayIndex.interval(0, 10)).dup().data().asFloat()));
stepFunction.step(params, g.gradient());
net.setParams(params); //params() may not be in-place
System.out.println(Arrays.toString(params.get(NDArrayIndex.all(), NDArrayIndex.interval(0, 10)).dup().data().asFloat()));
*/
}
@Test
public void testLayerNames() {
int nIn = 10;
int nOut = 40;
List<String> layerNameList = new ArrayList<>();
layerNameList.add("dnn1");
layerNameList.add("dnn2");
layerNameList.add("dnn3");
MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()
.updater(org.deeplearning4j.nn.conf.Updater.SGD).learningRate(0.1).list()
.layer(0, new DenseLayer.Builder().name("dnn1").nIn(nIn).nOut(20).activation(Activation.RELU)
.weightInit(WeightInit.XAVIER).build())
.layer(1, new DenseLayer.Builder().name("dnn2").nIn(20).nOut(30).activation(Activation.RELU)
.weightInit(WeightInit.XAVIER).build())
.layer(2, new DenseLayer.Builder().name("dnn3").nIn(30).nOut(nOut)
.activation(Activation.SOFTMAX).weightInit(WeightInit.XAVIER).build())
.build();
MultiLayerNetwork net = new MultiLayerNetwork(conf);
net.init();
assertEquals(layerNameList.get(0), net.getLayer(0).conf().getLayer().getLayerName());
assertEquals(layerNameList, net.getLayerNames());
BaseLayer b = (BaseLayer) net.getLayer(layerNameList.get(2)).conf().getLayer();
assertEquals("softmax", b.getActivationFn().toString());
}
@Test
public void testTranspose() {
MultiLayerConfiguration conf =
new NeuralNetConfiguration.Builder().iterations(100).momentum(0.9).regularization(true).l2(2e-4)
.list().layer(0,
new RBM.Builder(RBM.HiddenUnit.GAUSSIAN,
RBM.VisibleUnit.GAUSSIAN).nIn(4).nOut(3)
.weightInit(WeightInit.DISTRIBUTION)
.dist(new UniformDistribution(0,
1))
.activation(Activation.TANH)
.lossFunction(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD)
.build())
.layer(1, new org.deeplearning4j.nn.conf.layers.OutputLayer.Builder(
LossFunctions.LossFunction.MCXENT).nIn(3).nOut(3)
.weightInit(WeightInit.DISTRIBUTION)
.dist(new UniformDistribution(0, 1))
.activation(Activation.SOFTMAX).build())
.build();
MultiLayerNetwork net = new MultiLayerNetwork(conf);
net.init();
Layer layer = net.getLayer(0);
int nParamsBackprop = layer.numParams(true);
int nParamsBoth = layer.numParams(false);
Layer transposed = layer.transpose();
assertArrayEquals(new int[] {4, 3}, layer.getParam(DefaultParamInitializer.WEIGHT_KEY).shape());
assertArrayEquals(new int[] {1, 3}, layer.getParam(DefaultParamInitializer.BIAS_KEY).shape());
assertArrayEquals(new int[] {1, 4}, layer.getParam(PretrainParamInitializer.VISIBLE_BIAS_KEY).shape());
assertArrayEquals(new int[] {3, 4}, transposed.getParam(DefaultParamInitializer.WEIGHT_KEY).shape());
assertArrayEquals(new int[] {1, 4}, transposed.getParam(DefaultParamInitializer.BIAS_KEY).shape());
assertArrayEquals(new int[] {1, 3}, transposed.getParam(PretrainParamInitializer.VISIBLE_BIAS_KEY).shape());
INDArray origWeights = layer.getParam(DefaultParamInitializer.WEIGHT_KEY);
INDArray transposedWeights = transposed.getParam(DefaultParamInitializer.WEIGHT_KEY);
assertEquals(origWeights.transpose(), transposedWeights);
assertEquals(layer.getParam(PretrainParamInitializer.VISIBLE_BIAS_KEY),
transposed.getParam(DefaultParamInitializer.BIAS_KEY));
assertEquals(layer.getParam(DefaultParamInitializer.BIAS_KEY),
transposed.getParam(PretrainParamInitializer.VISIBLE_BIAS_KEY));
assertEquals(3, ((FeedForwardLayer) transposed.conf().getLayer()).getNIn());
assertEquals(4, ((FeedForwardLayer) transposed.conf().getLayer()).getNOut());
}
@Test
public void testScoreExamples() {
Nd4j.getRandom().setSeed(12345);
int nIn = 5;
int nOut = 6;
MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().seed(12345).regularization(true).l1(0.01)
.l2(0.01).learningRate(0.1).activation(Activation.TANH).weightInit(WeightInit.XAVIER).list()
.layer(0, new DenseLayer.Builder().nIn(nIn).nOut(20).build())
.layer(1, new DenseLayer.Builder().nIn(20).nOut(30).build()).layer(2, new OutputLayer.Builder()
.lossFunction(LossFunctions.LossFunction.MSE).nIn(30).nOut(nOut).build())
.build();
MultiLayerConfiguration confNoReg = new NeuralNetConfiguration.Builder().seed(12345).regularization(false)
.learningRate(0.1).activation(Activation.TANH).weightInit(WeightInit.XAVIER).list()
.layer(0, new DenseLayer.Builder().nIn(nIn).nOut(20).build())
.layer(1, new DenseLayer.Builder().nIn(20).nOut(30).build()).layer(2, new OutputLayer.Builder()
.lossFunction(LossFunctions.LossFunction.MSE).nIn(30).nOut(nOut).build())
.build();
MultiLayerNetwork net = new MultiLayerNetwork(conf);
net.init();
MultiLayerNetwork netNoReg = new MultiLayerNetwork(confNoReg);
netNoReg.init();
netNoReg.setParameters(net.params().dup());
//Score single example, and compare to scoreExamples:
INDArray input = Nd4j.rand(3, nIn);
INDArray output = Nd4j.rand(3, nOut);
DataSet ds = new DataSet(input, output);
INDArray scoresWithRegularization = net.scoreExamples(ds, true);
INDArray scoresNoRegularization = net.scoreExamples(ds, false);
assertArrayEquals(new int[] {3, 1}, scoresWithRegularization.shape());
assertArrayEquals(new int[] {3, 1}, scoresNoRegularization.shape());
for (int i = 0; i < 3; i++) {
DataSet singleEx = new DataSet(input.getRow(i), output.getRow(i));
double score = net.score(singleEx);
double scoreNoReg = netNoReg.score(singleEx);
double scoreUsingScoreExamples = scoresWithRegularization.getDouble(i);
double scoreUsingScoreExamplesNoReg = scoresNoRegularization.getDouble(i);
assertEquals(score, scoreUsingScoreExamples, 1e-4);
assertEquals(scoreNoReg, scoreUsingScoreExamplesNoReg, 1e-4);
assertTrue(scoreUsingScoreExamples > scoreUsingScoreExamplesNoReg); //Regularization term increases score
// System.out.println(score + "\t" + scoreUsingScoreExamples + "\t|\t" + scoreNoReg + "\t" + scoreUsingScoreExamplesNoReg);
}
}
@Test
public void testDataSetScore() {
Nd4j.getRandom().setSeed(12345);
MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().regularization(false).learningRate(1.0)
.weightInit(WeightInit.XAVIER).seed(12345L).list()
.layer(0, new DenseLayer.Builder().nIn(4).nOut(3).activation(Activation.SIGMOID).build())
.layer(1, new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT)
.activation(Activation.SOFTMAX).nIn(3).nOut(3).build())
.pretrain(false).backprop(true).build();
MultiLayerNetwork net = new MultiLayerNetwork(conf);
net.init();
INDArray in = Nd4j.create(new double[] {1.0, 2.0, 3.0, 4.0});
INDArray out = Nd4j.create(new double[] {1, 0, 0});
double score = net.score(new DataSet(in, out));
}
@Test
public void testDataSetScoreCNN() {
int miniBatch = 3;
int depth = 2;
int width = 3;
int height = 3;
int nOut = 2;
MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().regularization(false).learningRate(1.0)
.seed(12345L).list().layer(0, new ConvolutionLayer.Builder(2, 2).nOut(1).build())
.layer(1, new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT)
.activation(Activation.SOFTMAX).nOut(2).build())
.setInputType(InputType.convolutionalFlat(height, width, depth)).pretrain(false).backprop(true)
.build();
MultiLayerNetwork net = new MultiLayerNetwork(conf);
net.init();
Nd4j.getRandom().setSeed(12345);
Random r = new Random(12345);
INDArray input = Nd4j.rand(miniBatch, depth * width * height);
INDArray labels = Nd4j.create(miniBatch, nOut);
for (int i = 0; i < miniBatch; i++) {
labels.putScalar(new int[] {i, r.nextInt(nOut)}, 1.0);
}
double score = net.score(new DataSet(input, labels));
}
@Test
public void testPredict() throws Exception {
Nd4j.getRandom().setSeed(12345);
MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().regularization(false).learningRate(1.0)
.weightInit(WeightInit.XAVIER).seed(12345L).list()
.layer(0, new DenseLayer.Builder().nIn(784).nOut(50).activation(Activation.RELU).build())
.layer(1, new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT)
.activation(Activation.SOFTMAX).nIn(50).nOut(10).build())
.pretrain(false).backprop(true).setInputType(InputType.convolutional(28, 28, 1)).build();
MultiLayerNetwork net = new MultiLayerNetwork(conf);
net.init();
DataSetIterator ds = new MnistDataSetIterator(10, 10);
net.fit(ds);
DataSetIterator testDs = new MnistDataSetIterator(1, 1);
DataSet testData = testDs.next();
testData.setLabelNames(Arrays.asList("0", "1", "2", "3", "4", "5", "6", "7", "8", "9"));
String actualLables = testData.getLabelName(0);
List<String> prediction = net.predict(testData);
assertTrue(actualLables != null);
assertTrue(prediction.get(0) != null);
}
@Test
@Ignore
public void testCid() throws Exception {
System.out.println(EnvironmentUtils.buildCId());
Environment environment = EnvironmentUtils.buildEnvironment();
environment.setSerialVersionID(EnvironmentUtils.buildCId());
Task task = TaskUtils.buildTask(Nd4j.create(new double[] {1, 2, 3, 4, 5, 6}));
Heartbeat.getInstance().reportEvent(Event.STANDALONE, environment, task);
Thread.sleep(25000);
}
@Test
public void testOutput() throws Exception {
Nd4j.getRandom().setSeed(12345);
MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().regularization(false).learningRate(1.0)
.weightInit(WeightInit.XAVIER).seed(12345L).list()
.layer(0, new DenseLayer.Builder().nIn(784).nOut(50).activation(Activation.RELU).build())
.layer(1, new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT)
.activation(Activation.SOFTMAX).nIn(50).nOut(10).build())
.pretrain(false).backprop(true).setInputType(InputType.convolutional(28, 28, 1)).build();
MultiLayerNetwork net = new MultiLayerNetwork(conf);
net.init();
DataSetIterator fullData = new MnistDataSetIterator(1, 2);
net.fit(fullData);
fullData.reset();
DataSet expectedSet = fullData.next(2);
INDArray expectedOut = net.output(expectedSet.getFeatureMatrix(), false);
fullData.reset();
INDArray actualOut = net.output(fullData);
assertEquals(expectedOut, actualOut);
}
@Test
public void testGradientUpdate() throws Exception {
DataSetIterator iter = new IrisDataSetIterator(1, 1);
Gradient expectedGradient = new DefaultGradient();
expectedGradient.setGradientFor("0_W", Nd4j.ones(4, 5));
expectedGradient.setGradientFor("0_b", Nd4j.ones(1, 5));
expectedGradient.setGradientFor("1_W", Nd4j.ones(5, 3));
expectedGradient.setGradientFor("1_b", Nd4j.ones(1, 3));
MultiLayerConfiguration conf =
new NeuralNetConfiguration.Builder().updater(org.deeplearning4j.nn.conf.Updater.SGD)
.learningRate(1).activation(Activation.RELU).weightInit(WeightInit.XAVIER)
.list().layer(0, new DenseLayer.Builder().name("dnn1").nIn(4).nOut(5).build())
.layer(1, new OutputLayer.Builder().name("output").nIn(5).nOut(3)
.activation(Activation.SOFTMAX).weightInit(WeightInit.XAVIER)
.build())
.backprop(true).pretrain(false).build();
MultiLayerNetwork net = new MultiLayerNetwork(conf);
net.init();
net.fit(iter.next());
// TODO validate actual layer gradientView - issue getting var out of BaseLayer w/o adding MLN getter that gets confused with local gradient vars
Gradient actualGradient = net.gradient;
assertNotEquals(expectedGradient.getGradientFor("0_W"), actualGradient.getGradientFor("0_W"));
net.update(expectedGradient);
actualGradient = net.gradient;
assertEquals(expectedGradient.getGradientFor("0_W"), actualGradient.getGradientFor("0_W"));
// Update params with set
net.setParam("0_W", Nd4j.ones(4, 5));
net.setParam("0_b", Nd4j.ones(1, 5));
net.setParam("1_W", Nd4j.ones(5, 3));
net.setParam("1_b", Nd4j.ones(1, 3));
INDArray actualParams = net.params();
// Confirm params
assertEquals(expectedGradient.gradient(), actualParams);
net.update(expectedGradient);
actualParams = net.params();
assertEquals(Nd4j.ones(1, 43).addi(1), actualParams);
}
@Test(expected = DL4JException.class)
public void testCnnInvalidData() {
int miniBatch = 3;
int depth = 2;
int width = 5;
int height = 5;
MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().list()
.layer(0, new ConvolutionLayer.Builder().kernelSize(2, 2).stride(1, 1).padding(0, 0).nIn(2)
.nOut(2).build())
.layer(1, new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT)
.activation(Activation.SOFTMAX).nOut(2).build())
.setInputType(InputType.convolutional(height, width, depth)).pretrain(false).backprop(true)
.build();
MultiLayerNetwork net = new MultiLayerNetwork(conf);
net.init();
INDArray inputWrongDepth = Nd4j.rand(new int[] {miniBatch, 5, height, width}); //Order: examples, channels, height, width
net.feedForward(inputWrongDepth);
}
@Test
public void testApplyingPreTrainConfigAndParams() {
int nIn = 10;
int nOut = 10;
// Test pretrain true
MultiLayerNetwork rbmPre = getRBMModel(true, nIn, nOut);
assertTrue(rbmPre.conf().isPretrain()); // check on the network
assertTrue(rbmPre.getLayer(0).conf().isPretrain()); // check pretrain layer
assertFalse(rbmPre.getLayer(1).conf().isPretrain()); // check none pretrain layer
int actualNP = rbmPre.numParams();
assertEquals(2 * (nIn * nOut + nOut) + nIn, actualNP);
INDArray params = rbmPre.params();
assertEquals(params.length(), actualNP); // check num params
Map<String, INDArray> paramTable = rbmPre.paramTable();
assertTrue(paramTable.containsKey("0_vb")); // check vb exists for pretrain layer
rbmPre.setParam("0_vb", Nd4j.ones(10));
params = rbmPre.getParam("0_vb");
assertEquals(Nd4j.ones(10), params); // check set params for vb
// Test pretrain false, expect same for true because its not changed when applying update
MultiLayerNetwork rbmNoPre = getRBMModel(false, nIn, nOut);
assertFalse(rbmNoPre.conf().isPretrain());
assertFalse(rbmNoPre.getLayer(0).conf().isPretrain());
assertFalse(rbmPre.getLayer(1).conf().isPretrain());
actualNP = rbmNoPre.numParams();
assertEquals(2 * (nIn * nOut + nOut) + nIn, actualNP);
params = rbmNoPre.params();
assertEquals(params.length(), actualNP);
paramTable = rbmPre.paramTable();
assertTrue(paramTable.containsKey("0_vb"));
}
@Test
public void testLayerPreTrainSetFalseAfterPreTrain() {
INDArray input = Nd4j.linspace(1, 10, 10);
int nIn = 10;
int nOut = 10;
MultiLayerNetwork rbmPre = getRBMModel(true, nIn, nOut);
rbmPre.fit(input);
assertTrue(rbmPre.conf().isPretrain()); // check on the network
assertFalse(rbmPre.getLayer(0).conf().isPretrain()); // check pretrain layer
assertFalse(rbmPre.getLayer(1).conf().isPretrain()); // check none pretrain layer
}
public MultiLayerNetwork getRBMModel(boolean preTrain, int nIn, int nOut) {
MultiLayerConfiguration rbm = new NeuralNetConfiguration.Builder()
.seed(42).iterations(1).updater(Updater.NONE).epsilon(
1)
.weightInit(WeightInit.UNIFORM)
.list(new org.deeplearning4j.nn.conf.layers.RBM.Builder()
.lossFunction(LossFunctions.LossFunction.COSINE_PROXIMITY)
.activation(Activation.IDENTITY).nOut(nIn).build(),
new org.deeplearning4j.nn.conf.layers.OutputLayer.Builder(
LossFunctions.LossFunction.COSINE_PROXIMITY)
.activation(Activation.IDENTITY).nOut(nOut)
.build())
.pretrain(preTrain).setInputType(InputType.feedForward(nOut)).build();
MultiLayerNetwork network = new MultiLayerNetwork(rbm);
network.init();
return network;
}
@Test
public void testIterationCountAndPersistence() throws IOException {
Nd4j.getRandom().setSeed(123);
MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).iterations(1).seed(123)
.list()
.layer(0, new DenseLayer.Builder().nIn(4).nOut(3).weightInit(WeightInit.XAVIER)
.activation(Activation.TANH).build())
.layer(1, new org.deeplearning4j.nn.conf.layers.OutputLayer.Builder(
LossFunctions.LossFunction.MCXENT).activation(Activation.SOFTMAX).nIn(3).nOut(3)
.build())
.backprop(true).pretrain(false).build();
MultiLayerNetwork network = new MultiLayerNetwork(conf);
network.init();
DataSetIterator iter = new IrisDataSetIterator(50, 150);
assertEquals(0, network.getLayerWiseConfigurations().getIterationCount());
network.fit(iter);
assertEquals(3, network.getLayerWiseConfigurations().getIterationCount());
iter.reset();
network.fit(iter);
assertEquals(6, network.getLayerWiseConfigurations().getIterationCount());
iter.reset();
network.fit(iter.next());
assertEquals(7, network.getLayerWiseConfigurations().getIterationCount());
ByteArrayOutputStream baos = new ByteArrayOutputStream();
ModelSerializer.writeModel(network, baos, true);
byte[] asBytes = baos.toByteArray();
ByteArrayInputStream bais = new ByteArrayInputStream(asBytes);
MultiLayerNetwork net = ModelSerializer.restoreMultiLayerNetwork(bais, true);
assertEquals(7, net.getLayerWiseConfigurations().getIterationCount());
}
@Test
public void testBiasL1L2() {
Nd4j.getRandom().setSeed(123);
MultiLayerConfiguration conf1 = new NeuralNetConfiguration.Builder()
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).iterations(1)
.weightInit(WeightInit.XAVIER).activation(Activation.TANH).seed(123).list()
.layer(0, new DenseLayer.Builder().nIn(10).nOut(10).build())
.layer(1, new org.deeplearning4j.nn.conf.layers.OutputLayer.Builder(
LossFunctions.LossFunction.MSE).activation(Activation.IDENTITY).nIn(10).nOut(10)
.build())
.backprop(true).pretrain(false).build();
MultiLayerConfiguration conf2 = new NeuralNetConfiguration.Builder()
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).regularization(true)
.l1Bias(0.1).l2Bias(0.2).iterations(1).weightInit(WeightInit.XAVIER).activation(Activation.TANH)
.seed(123).list().layer(0, new DenseLayer.Builder().nIn(10).nOut(10).build())
.layer(1, new org.deeplearning4j.nn.conf.layers.OutputLayer.Builder(
LossFunctions.LossFunction.MSE).activation(Activation.IDENTITY).nIn(10).nOut(10)
.build())
.backprop(true).pretrain(false).build();
MultiLayerNetwork net1 = new MultiLayerNetwork(conf1);
net1.init();
MultiLayerNetwork net2 = new MultiLayerNetwork(conf2);
net2.init();
BaseLayer bl0 = (BaseLayer) net2.getLayer(0).conf().getLayer();
assertEquals(0.1, bl0.getL1Bias(), 1e-6);
assertEquals(0.2, bl0.getL2Bias(), 1e-6);
INDArray features = Nd4j.rand(10, 10);
INDArray labels = Nd4j.rand(10, 10);
net2.setParams(net1.params().dup());
net1.setInput(features);
net1.setLabels(labels);
net2.setInput(features);
net2.setLabels(labels);
net1.computeGradientAndScore();
net2.computeGradientAndScore();
double l1 = net1.calcL1(true);
double l2 = net1.calcL2(true);
assertEquals(0.0, l1, 0.0);
assertEquals(0.0, l2, 0.0);
l1 = net2.calcL1(true);
l2 = net2.calcL2(true);
assertEquals(0.0, l1, 0.0);
assertEquals(0.0, l2, 0.0);
double s1 = net1.score();
double s2 = net2.score();
assertEquals(s1, s2, 1e-8); //Biases initialized to 0 -> should initially have same score
for (int i = 0; i < 10; i++) {
net1.fit(features, labels);
}
net2.setParams(net1.params().dup());
net1.computeGradientAndScore();
net2.computeGradientAndScore();
l1 = net1.calcL1(true);
l2 = net1.calcL2(true);
assertEquals(0.0, l1, 0.0);
assertEquals(0.0, l2, 0.0);
l1 = net2.calcL1(true);
l2 = net2.calcL2(true);
assertTrue(l1 > 0.0);
assertTrue(l2 > 0.0);
s1 = net1.score();
s2 = net2.score();
assertNotEquals(s1, s2, 1e-6); //Scores should differ due to bias l1/l2
for (int i = 0; i < 2; i++) {
assertEquals(0.0, net1.getLayer(i).calcL1(true), 0.0);
assertEquals(0.0, net1.getLayer(i).calcL2(true), 0.0);
assertTrue(net2.getLayer(i).calcL1(true) > 0.0);
assertTrue(net2.getLayer(i).calcL2(true) > 0.0);
}
}
@Test
public void testSummary() {
int V_WIDTH = 130;
int V_HEIGHT = 130;
int V_NFRAMES = 150;
MultiLayerConfiguration confForArchitecture =
new NeuralNetConfiguration.Builder().seed(12345).regularization(true).l2(0.001) //l2 regularization on all layers