325 lines
12 KiB
Java
325 lines
12 KiB
Java
/*******************************************************************************
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* Copyright (c) 2015-2018 Skymind, Inc.
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*
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* This program and the accompanying materials are made available under the
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* terms of the Apache License, Version 2.0 which is available at
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* https://www.apache.org/licenses/LICENSE-2.0.
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
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* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
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* License for the specific language governing permissions and limitations
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* under the License.
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*
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* SPDX-License-Identifier: Apache-2.0
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******************************************************************************/
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package org.deeplearning4j.optimizer.listener;
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import lombok.Data;
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import lombok.extern.slf4j.Slf4j;
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import org.deeplearning4j.BaseDL4JTest;
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import org.deeplearning4j.api.storage.StatsStorageRouter;
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import org.deeplearning4j.api.storage.listener.RoutingIterationListener;
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import org.deeplearning4j.datasets.iterator.impl.IrisDataSetIterator;
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import org.deeplearning4j.nn.api.Layer;
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import org.deeplearning4j.nn.api.Model;
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import org.deeplearning4j.nn.conf.ComputationGraphConfiguration;
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import org.deeplearning4j.nn.conf.MultiLayerConfiguration;
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import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
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import org.deeplearning4j.nn.conf.layers.AutoEncoder;
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import org.deeplearning4j.nn.conf.layers.OutputLayer;
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import org.deeplearning4j.nn.conf.layers.variational.VariationalAutoencoder;
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import org.deeplearning4j.nn.graph.ComputationGraph;
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import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
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import org.deeplearning4j.optimize.api.BaseTrainingListener;
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import org.deeplearning4j.optimize.api.TrainingListener;
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import org.deeplearning4j.optimize.listeners.ComposableIterationListener;
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import org.deeplearning4j.optimize.listeners.PerformanceListener;
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import org.deeplearning4j.optimize.listeners.ScoreIterationListener;
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import org.deeplearning4j.optimize.listeners.TimeIterationListener;
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import org.deeplearning4j.optimize.listeners.CheckpointListener;
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import org.deeplearning4j.optimize.solvers.BaseOptimizer;
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import org.junit.Rule;
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import org.junit.Test;
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import org.junit.rules.TemporaryFolder;
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import org.nd4j.linalg.activations.Activation;
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import org.nd4j.linalg.api.ndarray.INDArray;
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import org.nd4j.linalg.dataset.api.iterator.DataSetIterator;
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import org.nd4j.linalg.lossfunctions.LossFunctions;
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import org.nd4j.linalg.primitives.Triple;
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import java.io.ByteArrayInputStream;
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import java.io.ByteArrayOutputStream;
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import java.io.ObjectInputStream;
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import java.io.ObjectOutputStream;
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import java.util.ArrayList;
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import java.util.Collection;
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import java.util.List;
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import java.util.Map;
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import static org.junit.Assert.assertEquals;
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import static org.junit.Assert.assertTrue;
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/**
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* Created by Alex on 01/01/2017.
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*/
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@Slf4j
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public class TestListeners extends BaseDL4JTest {
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@Rule
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public TemporaryFolder tempDir = new TemporaryFolder();
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@Test
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public void testSettingListenersUnsupervised() {
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//Pretrain layers should get copies of the listeners, in addition to the
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MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().list()
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.layer(0, new AutoEncoder.Builder().nIn(10).nOut(10).build())
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.layer(1, new VariationalAutoencoder.Builder().nIn(10).nOut(10).build()).build();
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MultiLayerNetwork net = new MultiLayerNetwork(conf);
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net.init();
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net.setListeners(new ScoreIterationListener(), new TestRoutingListener());
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for (Layer l : net.getLayers()) {
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Collection<TrainingListener> layerListeners = l.getListeners();
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assertEquals(l.getClass().toString(), 2, layerListeners.size());
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TrainingListener[] lArr = layerListeners.toArray(new TrainingListener[2]);
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assertTrue(lArr[0] instanceof ScoreIterationListener);
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assertTrue(lArr[1] instanceof TestRoutingListener);
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}
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Collection<TrainingListener> netListeners = net.getListeners();
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assertEquals(2, netListeners.size());
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TrainingListener[] lArr = netListeners.toArray(new TrainingListener[2]);
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assertTrue(lArr[0] instanceof ScoreIterationListener);
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assertTrue(lArr[1] instanceof TestRoutingListener);
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ComputationGraphConfiguration gConf = new NeuralNetConfiguration.Builder().graphBuilder().addInputs("in")
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.addLayer("0", new AutoEncoder.Builder().nIn(10).nOut(10).build(), "in")
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.addLayer("1", new VariationalAutoencoder.Builder().nIn(10).nOut(10).build(), "0")
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.setOutputs("1").build();
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ComputationGraph cg = new ComputationGraph(gConf);
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cg.init();
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cg.setListeners(new ScoreIterationListener(), new TestRoutingListener());
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for (Layer l : cg.getLayers()) {
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Collection<TrainingListener> layerListeners = l.getListeners();
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assertEquals(2, layerListeners.size());
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lArr = layerListeners.toArray(new TrainingListener[2]);
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assertTrue(lArr[0] instanceof ScoreIterationListener);
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assertTrue(lArr[1] instanceof TestRoutingListener);
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}
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netListeners = cg.getListeners();
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assertEquals(2, netListeners.size());
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lArr = netListeners.toArray(new TrainingListener[2]);
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assertTrue(lArr[0] instanceof ScoreIterationListener);
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assertTrue(lArr[1] instanceof TestRoutingListener);
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}
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private static class TestRoutingListener extends BaseTrainingListener implements RoutingIterationListener {
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@Override
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public void setStorageRouter(StatsStorageRouter router) {}
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@Override
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public StatsStorageRouter getStorageRouter() {
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return null;
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}
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@Override
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public void setWorkerID(String workerID) {}
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@Override
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public String getWorkerID() {
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return null;
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}
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@Override
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public void setSessionID(String sessionID) {}
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@Override
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public String getSessionID() {
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return null;
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}
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@Override
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public RoutingIterationListener clone() {
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return null;
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}
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@Override
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public void iterationDone(Model model, int iteration, int epoch) {}
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}
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@Test
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public void testListenerSerialization() throws Exception {
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//Note: not all listeners are (or should be) serializable. But some should be - for Spark etc
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List<TrainingListener> listeners = new ArrayList<>();
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listeners.add(new ScoreIterationListener());
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listeners.add(new PerformanceListener(1, true, true));
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listeners.add(new TimeIterationListener(10000));
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listeners.add(new ComposableIterationListener(new ScoreIterationListener(), new PerformanceListener(1, true, true)));
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listeners.add(new CheckpointListener.Builder(tempDir.newFolder()).keepAll().saveEveryNIterations(3).build()); //Doesn't usually need to be serialized, but no reason it can't be...
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DataSetIterator iter = new IrisDataSetIterator(10, 150);
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MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()
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.list()
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.layer(new OutputLayer.Builder().nIn(4).nOut(3)
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.activation(Activation.SOFTMAX)
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.lossFunction(LossFunctions.LossFunction.MCXENT).build())
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.build();
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MultiLayerNetwork net = new MultiLayerNetwork(conf);
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net.init();
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net.setListeners(listeners);
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net.fit(iter);
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List<TrainingListener> listeners2 = new ArrayList<>();
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for(TrainingListener il : listeners){
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log.info("------------------");
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log.info("Testing listener: {}", il);
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ByteArrayOutputStream baos = new ByteArrayOutputStream();
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ObjectOutputStream oos = new ObjectOutputStream(baos);
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oos.writeObject(il);
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byte[] bytes = baos.toByteArray();
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ObjectInputStream ois = new ObjectInputStream(new ByteArrayInputStream(bytes));
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TrainingListener il2 = (TrainingListener) ois.readObject();
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listeners2.add(il2);
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}
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net.setListeners(listeners2);
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net.fit(iter);
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}
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@Test
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public void testListenerCalls(){
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MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()
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.list()
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.layer(new OutputLayer.Builder().nIn(4).nOut(3).activation(Activation.SOFTMAX).lossFunction(LossFunctions.LossFunction.MCXENT).build())
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.build();
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MultiLayerNetwork net = new MultiLayerNetwork(conf);
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net.init();
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TestListener tl = new TestListener();
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net.setListeners(tl);
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DataSetIterator irisIter = new IrisDataSetIterator(50, 150);
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net.fit(irisIter, 2);
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List<Triple<Call,Integer,Integer>> exp = new ArrayList<>();
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exp.add(new Triple<>(Call.EPOCH_START, 0, 0));
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exp.add(new Triple<>(Call.ON_FWD, 0, 0));
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exp.add(new Triple<>(Call.ON_BWD, 0, 0));
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exp.add(new Triple<>(Call.ON_GRAD, 0, 0));
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exp.add(new Triple<>(Call.ITER_DONE, 0, 0));
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exp.add(new Triple<>(Call.ON_FWD, 1, 0));
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exp.add(new Triple<>(Call.ON_BWD, 1, 0));
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exp.add(new Triple<>(Call.ON_GRAD, 1, 0));
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exp.add(new Triple<>(Call.ITER_DONE, 1, 0));
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exp.add(new Triple<>(Call.ON_FWD, 2, 0));
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exp.add(new Triple<>(Call.ON_BWD, 2, 0));
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exp.add(new Triple<>(Call.ON_GRAD, 2, 0));
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exp.add(new Triple<>(Call.ITER_DONE, 2, 0));
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exp.add(new Triple<>(Call.EPOCH_END, 3, 0)); //Post updating iter count, pre update epoch count
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exp.add(new Triple<>(Call.EPOCH_START, 3, 1));
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exp.add(new Triple<>(Call.ON_FWD, 3, 1));
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exp.add(new Triple<>(Call.ON_BWD, 3, 1));
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exp.add(new Triple<>(Call.ON_GRAD, 3, 1));
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exp.add(new Triple<>(Call.ITER_DONE, 3, 1));
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exp.add(new Triple<>(Call.ON_FWD, 4, 1));
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exp.add(new Triple<>(Call.ON_BWD, 4, 1));
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exp.add(new Triple<>(Call.ON_GRAD, 4, 1));
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exp.add(new Triple<>(Call.ITER_DONE, 4, 1));
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exp.add(new Triple<>(Call.ON_FWD, 5, 1));
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exp.add(new Triple<>(Call.ON_BWD, 5, 1));
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exp.add(new Triple<>(Call.ON_GRAD, 5, 1));
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exp.add(new Triple<>(Call.ITER_DONE, 5, 1));
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exp.add(new Triple<>(Call.EPOCH_END, 6, 1));
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assertEquals(exp, tl.getCalls());
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tl = new TestListener();
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ComputationGraph cg = net.toComputationGraph();
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cg.setListeners(tl);
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cg.fit(irisIter, 2);
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assertEquals(exp, tl.getCalls());
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}
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private static enum Call {
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ITER_DONE,
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EPOCH_START,
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EPOCH_END,
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ON_FWD,
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ON_GRAD,
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ON_BWD
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}
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@Data
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private static class TestListener implements TrainingListener {
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private List<Triple<Call,Integer,Integer>> calls = new ArrayList<>();
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@Override
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public void iterationDone(Model model, int iteration, int epoch) {
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calls.add(new Triple<>(Call.ITER_DONE, iteration, epoch));
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}
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@Override
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public void onEpochStart(Model model) {
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calls.add(new Triple<>(Call.EPOCH_START, BaseOptimizer.getIterationCount(model), BaseOptimizer.getEpochCount(model)));
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}
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@Override
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public void onEpochEnd(Model model) {
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calls.add(new Triple<>(Call.EPOCH_END, BaseOptimizer.getIterationCount(model), BaseOptimizer.getEpochCount(model)));
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}
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@Override
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public void onForwardPass(Model model, List<INDArray> activations) {
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calls.add(new Triple<>(Call.ON_FWD, BaseOptimizer.getIterationCount(model), BaseOptimizer.getEpochCount(model)));
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}
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@Override
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public void onForwardPass(Model model, Map<String, INDArray> activations) {
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calls.add(new Triple<>(Call.ON_FWD, BaseOptimizer.getIterationCount(model), BaseOptimizer.getEpochCount(model)));
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}
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@Override
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public void onGradientCalculation(Model model) {
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calls.add(new Triple<>(Call.ON_GRAD, BaseOptimizer.getIterationCount(model), BaseOptimizer.getEpochCount(model)));
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}
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@Override
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public void onBackwardPass(Model model) {
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calls.add(new Triple<>(Call.ON_BWD, BaseOptimizer.getIterationCount(model), BaseOptimizer.getEpochCount(model)));
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}
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}
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}
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