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SameDiffRNNTestCases.java
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SameDiffRNNTestCases.java
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/*
* ******************************************************************************
* *
* *
* * This program and the accompanying materials are made available under the
* * terms of the Apache License, Version 2.0 which is available at
* * https://www.apache.org/licenses/LICENSE-2.0.
* *
* * See the NOTICE file distributed with this work for additional
* * information regarding copyright ownership.
* * 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.
* *
* * SPDX-License-Identifier: Apache-2.0
* *****************************************************************************
*/
package org.eclipse.deeplearning4j.integration.testcases.samediff;
import org.datavec.api.records.reader.SequenceRecordReader;
import org.datavec.api.records.reader.impl.csv.CSVSequenceRecordReader;
import org.datavec.api.split.NumberedFileInputSplit;
import org.deeplearning4j.datasets.datavec.SequenceRecordReaderDataSetIterator;
import org.eclipse.deeplearning4j.integration.ModelType;
import org.eclipse.deeplearning4j.integration.TestCase;
import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff;
import org.nd4j.autodiff.samediff.TrainingConfig;
import org.nd4j.evaluation.IEvaluation;
import org.nd4j.evaluation.classification.Evaluation;
import org.nd4j.evaluation.classification.EvaluationCalibration;
import org.nd4j.evaluation.classification.ROCMultiClass;
import org.nd4j.linalg.api.buffer.DataType;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.api.ops.impl.layers.recurrent.config.LSTMActivations;
import org.nd4j.linalg.api.ops.impl.layers.recurrent.config.LSTMDataFormat;
import org.nd4j.linalg.api.ops.impl.layers.recurrent.config.LSTMDirectionMode;
import org.nd4j.linalg.api.ops.impl.layers.recurrent.config.LSTMLayerConfig;
import org.nd4j.linalg.api.ops.impl.layers.recurrent.outputs.LSTMLayerOutputs;
import org.nd4j.linalg.api.ops.impl.layers.recurrent.weights.LSTMLayerWeights;
import org.nd4j.linalg.dataset.adapter.MultiDataSetIteratorAdapter;
import org.nd4j.linalg.dataset.api.MultiDataSet;
import org.nd4j.linalg.dataset.api.MultiDataSetPreProcessor;
import org.nd4j.linalg.dataset.api.iterator.DataSetIterator;
import org.nd4j.linalg.dataset.api.iterator.MultiDataSetIterator;
import org.nd4j.linalg.dataset.api.preprocessor.CompositeMultiDataSetPreProcessor;
import org.nd4j.linalg.dataset.api.preprocessor.MultiDataNormalization;
import org.nd4j.linalg.dataset.api.preprocessor.MultiNormalizerStandardize;
import org.nd4j.linalg.factory.Nd4j;
import org.nd4j.linalg.indexing.NDArrayIndex;
import org.nd4j.linalg.learning.config.Adam;
import org.nd4j.common.resources.Resources;
import org.nd4j.shade.guava.io.Files;
import java.io.File;
import java.util.ArrayList;
import java.util.Collections;
import java.util.List;
import java.util.Map;
public class SameDiffRNNTestCases {
public static TestCase getRnnCsvSequenceClassificationTestCase1() {
return new SameDiffRNNTestCases.RnnCsvSequenceClassificationTestCase1();
}
protected static class RnnCsvSequenceClassificationTestCase1 extends TestCase {
protected RnnCsvSequenceClassificationTestCase1() {
testName = "RnnCsvSequenceClassification1";
testType = TestType.RANDOM_INIT;
testPredictions = true;
testTrainingCurves = false;
testGradients = false;
testParamsPostTraining = false;
testEvaluation = true;
testOverfitting = false; //Not much point on this one - it already fits very well...
}
protected MultiDataNormalization normalizer;
protected MultiDataNormalization getNormalizer() throws Exception {
if (normalizer != null) {
return normalizer;
}
normalizer = new MultiNormalizerStandardize();
normalizer.fit(getTrainingDataUnnormalized());
return normalizer;
}
@Override
public ModelType modelType() {
return ModelType.SAMEDIFF;
}
@Override
public Object getConfiguration() throws Exception {
Nd4j.getRandom().setSeed(12345);
int miniBatchSize = 10;
int numLabelClasses = 6;
int nIn = 60;
int numUnits = 7;
int timeSteps = 3;
SameDiff sd = SameDiff.create();
SDVariable in = sd.placeHolder("in", DataType.FLOAT, miniBatchSize, timeSteps, nIn);
SDVariable label = sd.placeHolder("label", DataType.FLOAT, miniBatchSize, numLabelClasses);
SDVariable cLast = sd.var("cLast", Nd4j.zeros(DataType.FLOAT, miniBatchSize, numUnits));
SDVariable yLast = sd.var("yLast", Nd4j.zeros(DataType.FLOAT, miniBatchSize, numUnits));
LSTMLayerConfig c = LSTMLayerConfig.builder()
.lstmdataformat(LSTMDataFormat.NTS)
.directionMode(LSTMDirectionMode.FWD)
.gateAct(LSTMActivations.SIGMOID)
.cellAct(LSTMActivations.TANH)
.outAct(LSTMActivations.TANH)
.retFullSequence(true)
.retLastC(true)
.retLastH(true)
.build();
LSTMLayerOutputs outputs = new LSTMLayerOutputs(sd.rnn.lstmLayer(
in, cLast, yLast, null,
LSTMLayerWeights.builder()
.weights(sd.var("weights", Nd4j.rand(DataType.FLOAT, nIn, 4 * numUnits)))
.rWeights(sd.var("rWeights", Nd4j.rand(DataType.FLOAT, numUnits, 4 * numUnits)))
.peepholeWeights(sd.var("inputPeepholeWeights", Nd4j.rand(DataType.FLOAT, 3 * numUnits)))
.bias(sd.var("bias", Nd4j.rand(DataType.FLOAT, 4 * numUnits)))
.build(),
c), c);
// Behaviour with default settings: 3d (time series) input with shape
// [miniBatchSize, vectorSize, timeSeriesLength] -> 2d output [miniBatchSize, vectorSize]
SDVariable layer0 = outputs.getOutput();
SDVariable layer1 = layer0.mean(1);
SDVariable w1 = sd.var("w1", Nd4j.rand(DataType.FLOAT, numUnits, numLabelClasses));
SDVariable b1 = sd.var("b1", Nd4j.rand(DataType.FLOAT, numLabelClasses));
SDVariable out = sd.nn.softmax("out", layer1.mmul(w1).add(b1));
SDVariable loss = sd.loss.logLoss("loss", label, out);
//Also set the training configuration:
sd.setTrainingConfig(TrainingConfig.builder()
.updater(new Adam(5e-2))
.l1(1e-3).l2(1e-3)
.dataSetFeatureMapping("in") //features[0] -> "in" placeholder
.dataSetLabelMapping("label") //labels[0] -> "label" placeholder
.build());
return sd;
}
@Override
public List<Map<String, INDArray>> getPredictionsTestDataSameDiff() throws Exception {
MultiDataSet mds = getTrainingData().next();
List<Map<String, INDArray>> list = new ArrayList<>();
list.add(Collections.singletonMap("in", mds.getFeatures()[0].reshape(10, 1, 60)));
//[batchsize, insize]
return list;
}
@Override
public List<String> getPredictionsNamesSameDiff() throws Exception {
return Collections.singletonList("out");
}
@Override
public MultiDataSetIterator getTrainingData() throws Exception {
MultiDataSetIterator iter = getTrainingDataUnnormalized();
MultiDataSetPreProcessor pp = multiDataSet -> {
INDArray l = multiDataSet.getLabels(0);
l = l.get(NDArrayIndex.all(), NDArrayIndex.all(), NDArrayIndex.point(l.size(2) - 1));
multiDataSet.setLabels(0, l);
multiDataSet.setLabelsMaskArray(0, null);
};
iter.setPreProcessor(new CompositeMultiDataSetPreProcessor(getNormalizer(), pp));
return iter;
}
protected MultiDataSetIterator getTrainingDataUnnormalized() throws Exception {
int miniBatchSize = 10;
int numLabelClasses = 6;
File featuresDirTrain = Files.createTempDir();
File labelsDirTrain = Files.createTempDir();
Resources.copyDirectory("dl4j-integration-tests/data/uci_seq/train/features/", featuresDirTrain);
Resources.copyDirectory("dl4j-integration-tests/data/uci_seq/train/labels/", labelsDirTrain);
SequenceRecordReader trainFeatures = new CSVSequenceRecordReader();
trainFeatures.initialize(new NumberedFileInputSplit(featuresDirTrain.getAbsolutePath() + "/%d.csv", 0, 449));
SequenceRecordReader trainLabels = new CSVSequenceRecordReader();
trainLabels.initialize(new NumberedFileInputSplit(labelsDirTrain.getAbsolutePath() + "/%d.csv", 0, 449));
DataSetIterator trainData = new SequenceRecordReaderDataSetIterator(trainFeatures, trainLabels, miniBatchSize, numLabelClasses,
false, SequenceRecordReaderDataSetIterator.AlignmentMode.ALIGN_END);
MultiDataSetIterator iter = new MultiDataSetIteratorAdapter(trainData);
return iter;
}
@Override
public IEvaluation[] getNewEvaluations() {
return new IEvaluation[]{
new Evaluation(),
new ROCMultiClass(),
new EvaluationCalibration()
};
}
@Override
public MultiDataSetIterator getEvaluationTestData() throws Exception {
int miniBatchSize = 10;
int numLabelClasses = 6;
// File featuresDirTest = new ClassPathResource("/RnnCsvSequenceClassification/uci_seq/test/features/").getFile();
// File labelsDirTest = new ClassPathResource("/RnnCsvSequenceClassification/uci_seq/test/labels/").getFile();
File featuresDirTest = Files.createTempDir();
File labelsDirTest = Files.createTempDir();
Resources.copyDirectory("dl4j-integration-tests/data/uci_seq/test/features/", featuresDirTest);
Resources.copyDirectory("dl4j-integration-tests/data/uci_seq/test/labels/", labelsDirTest);
SequenceRecordReader trainFeatures = new CSVSequenceRecordReader();
trainFeatures.initialize(new NumberedFileInputSplit(featuresDirTest.getAbsolutePath() + "/%d.csv", 0, 149));
SequenceRecordReader trainLabels = new CSVSequenceRecordReader();
trainLabels.initialize(new NumberedFileInputSplit(labelsDirTest.getAbsolutePath() + "/%d.csv", 0, 149));
DataSetIterator testData = new SequenceRecordReaderDataSetIterator(trainFeatures, trainLabels, miniBatchSize, numLabelClasses,
false, SequenceRecordReaderDataSetIterator.AlignmentMode.ALIGN_END);
MultiDataSetIterator iter = new MultiDataSetIteratorAdapter(testData);
MultiDataSetPreProcessor pp = multiDataSet -> {
INDArray l = multiDataSet.getLabels(0);
l = l.get(NDArrayIndex.all(), NDArrayIndex.all(), NDArrayIndex.point(l.size(2) - 1));
multiDataSet.setLabels(0, l);
multiDataSet.setLabelsMaskArray(0, null);
};
iter.setPreProcessor(new CompositeMultiDataSetPreProcessor(getNormalizer(), pp));
return iter;
}
@Override
public IEvaluation[] doEvaluationSameDiff(SameDiff sd, MultiDataSetIterator iter, IEvaluation[] evaluations) {
sd.evaluate(iter, "out", 0, evaluations);
return evaluations;
}
}
}