430 lines
20 KiB
Java
430 lines
20 KiB
Java
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/*******************************************************************************
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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.nn.conf;
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import lombok.AllArgsConstructor;
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import lombok.Data;
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import lombok.EqualsAndHashCode;
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import lombok.NoArgsConstructor;
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import org.deeplearning4j.BaseDL4JTest;
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import org.deeplearning4j.exception.DL4JInvalidConfigException;
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import org.deeplearning4j.nn.api.OptimizationAlgorithm;
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import org.deeplearning4j.nn.conf.distribution.NormalDistribution;
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import org.deeplearning4j.nn.conf.graph.ElementWiseVertex;
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import org.deeplearning4j.nn.conf.graph.GraphVertex;
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import org.deeplearning4j.nn.conf.graph.MergeVertex;
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import org.deeplearning4j.nn.conf.graph.SubsetVertex;
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import org.deeplearning4j.nn.conf.inputs.InputType;
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import org.deeplearning4j.nn.conf.inputs.InvalidInputTypeException;
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import org.deeplearning4j.nn.conf.layers.*;
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import org.deeplearning4j.nn.conf.layers.recurrent.Bidirectional;
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import org.deeplearning4j.nn.conf.memory.MemoryReport;
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import org.deeplearning4j.nn.conf.misc.TestGraphVertex;
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import org.deeplearning4j.nn.conf.preprocessor.CnnToFeedForwardPreProcessor;
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import org.deeplearning4j.nn.conf.preprocessor.FeedForwardToCnnPreProcessor;
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import org.deeplearning4j.nn.graph.ComputationGraph;
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import org.junit.Test;
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import org.nd4j.linalg.activations.Activation;
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import org.nd4j.linalg.api.buffer.DataType;
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import org.nd4j.linalg.api.ndarray.INDArray;
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import org.nd4j.linalg.learning.config.NoOp;
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import org.nd4j.linalg.lossfunctions.LossFunctions;
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import static org.junit.Assert.*;
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public class ComputationGraphConfigurationTest extends BaseDL4JTest {
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@Test
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public void testJSONBasic() {
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ComputationGraphConfiguration conf = new NeuralNetConfiguration.Builder().seed(12345)
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.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
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.dist(new NormalDistribution(0, 1)).updater(new NoOp())
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.graphBuilder().addInputs("input")
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.appendLayer("firstLayer",
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new DenseLayer.Builder().nIn(4).nOut(5).activation(Activation.TANH).build())
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.addLayer("outputLayer",
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new OutputLayer.Builder().lossFunction(LossFunctions.LossFunction.MCXENT)
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.activation(Activation.SOFTMAX).nIn(5).nOut(3).build(),
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"firstLayer")
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.setOutputs("outputLayer").build();
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String json = conf.toJson();
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ComputationGraphConfiguration conf2 = ComputationGraphConfiguration.fromJson(json);
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assertEquals(json, conf2.toJson());
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assertEquals(conf, conf2);
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}
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@Test
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public void testJSONBasic2() {
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ComputationGraphConfiguration conf =
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new NeuralNetConfiguration.Builder()
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.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
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.graphBuilder().addInputs("input")
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.addLayer("cnn1",
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new ConvolutionLayer.Builder(2, 2).stride(2, 2).nIn(1).nOut(5)
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.build(),
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"input")
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.addLayer("cnn2",
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new ConvolutionLayer.Builder(2, 2).stride(2, 2).nIn(1).nOut(5)
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.build(),
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"input")
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.addLayer("max1",
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new SubsamplingLayer.Builder(SubsamplingLayer.PoolingType.MAX)
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.kernelSize(2, 2).build(),
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"cnn1", "cnn2")
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.addLayer("dnn1", new DenseLayer.Builder().nOut(7).build(), "max1")
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.addLayer("max2", new SubsamplingLayer.Builder().build(), "max1")
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.addLayer("output", new OutputLayer.Builder().nIn(7).nOut(10).activation(Activation.SOFTMAX).build(), "dnn1",
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"max2")
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.setOutputs("output")
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.inputPreProcessor("cnn1", new FeedForwardToCnnPreProcessor(32, 32, 3))
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.inputPreProcessor("cnn2", new FeedForwardToCnnPreProcessor(32, 32, 3))
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.inputPreProcessor("dnn1", new CnnToFeedForwardPreProcessor(8, 8, 5))
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.build();
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String json = conf.toJson();
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ComputationGraphConfiguration conf2 = ComputationGraphConfiguration.fromJson(json);
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assertEquals(json, conf2.toJson());
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assertEquals(conf, conf2);
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}
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@Test
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public void testJSONWithGraphNodes() {
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ComputationGraphConfiguration conf =
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new NeuralNetConfiguration.Builder()
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.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
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.graphBuilder().addInputs("input1", "input2")
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.addLayer("cnn1",
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new ConvolutionLayer.Builder(2, 2).stride(2, 2).nIn(1).nOut(5)
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.build(),
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"input1")
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.addLayer("cnn2",
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new ConvolutionLayer.Builder(2, 2).stride(2, 2).nIn(1).nOut(5)
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.build(),
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"input2")
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.addVertex("merge1", new MergeVertex(), "cnn1", "cnn2")
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.addVertex("subset1", new SubsetVertex(0, 1), "merge1")
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.addLayer("dense1", new DenseLayer.Builder().nIn(20).nOut(5).build(), "subset1")
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.addLayer("dense2", new DenseLayer.Builder().nIn(20).nOut(5).build(), "subset1")
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.addVertex("add", new ElementWiseVertex(ElementWiseVertex.Op.Add), "dense1",
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"dense2")
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.addLayer("out", new OutputLayer.Builder().nIn(1).nOut(1).activation(Activation.TANH).lossFunction(LossFunctions.LossFunction.MSE).build(), "add")
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.setOutputs("out").build();
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String json = conf.toJson();
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System.out.println(json);
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ComputationGraphConfiguration conf2 = ComputationGraphConfiguration.fromJson(json);
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assertEquals(json, conf2.toJson());
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assertEquals(conf, conf2);
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}
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@Test
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public void testInvalidConfigurations() {
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//Test no inputs for a layer:
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try {
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new NeuralNetConfiguration.Builder().graphBuilder().addInputs("input1")
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.addLayer("dense1", new DenseLayer.Builder().nIn(2).nOut(2).build(), "input1")
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.addLayer("out", new OutputLayer.Builder().nIn(2).nOut(2).build()).setOutputs("out")
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.build();
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fail("No exception thrown for invalid configuration");
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} catch (IllegalStateException e) {
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//OK - exception is good
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//e.printStackTrace();
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}
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// Use appendLayer on first layer
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try {
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new NeuralNetConfiguration.Builder().graphBuilder()
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.appendLayer("dense1", new DenseLayer.Builder().nIn(2).nOut(2).build())
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.addLayer("out", new OutputLayer.Builder().nIn(2).nOut(2).build()).setOutputs("out")
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.build();
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fail("No exception thrown for invalid configuration");
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} catch (IllegalStateException e) {
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//OK - exception is good
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//e.printStackTrace();
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}
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//Test no network inputs
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try {
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new NeuralNetConfiguration.Builder().graphBuilder()
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.addLayer("dense1", new DenseLayer.Builder().nIn(2).nOut(2).build(), "input1")
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.addLayer("out", new OutputLayer.Builder().nIn(2).nOut(2).build(), "dense1")
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.setOutputs("out").build();
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fail("No exception thrown for invalid configuration");
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} catch (IllegalStateException e) {
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//OK - exception is good
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//e.printStackTrace();
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}
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//Test no network outputs
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try {
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new NeuralNetConfiguration.Builder().graphBuilder().addInputs("input1")
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.addLayer("dense1", new DenseLayer.Builder().nIn(2).nOut(2).build(), "input1")
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.addLayer("out", new OutputLayer.Builder().nIn(2).nOut(2).build(), "dense1").build();
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fail("No exception thrown for invalid configuration");
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} catch (IllegalStateException e) {
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//OK - exception is good
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//e.printStackTrace();
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}
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//Test: invalid input
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try {
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new NeuralNetConfiguration.Builder().graphBuilder().addInputs("input1")
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.addLayer("dense1", new DenseLayer.Builder().nIn(2).nOut(2).build(), "input1")
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.addLayer("out", new OutputLayer.Builder().nIn(2).nOut(2).build(), "thisDoesntExist")
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.setOutputs("out").build();
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fail("No exception thrown for invalid configuration");
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} catch (IllegalStateException e) {
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//OK - exception is good
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//e.printStackTrace();
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}
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//Test: graph with cycles
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try {
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ComputationGraphConfiguration conf = new NeuralNetConfiguration.Builder().graphBuilder().addInputs("input1")
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.addLayer("dense1", new DenseLayer.Builder().nIn(2).nOut(2).build(), "input1", "dense3")
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.addLayer("dense2", new DenseLayer.Builder().nIn(2).nOut(2).build(), "dense1")
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.addLayer("dense3", new DenseLayer.Builder().nIn(2).nOut(2).build(), "dense2")
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.addLayer("out", new OutputLayer.Builder().nIn(2).nOut(2).lossFunction(LossFunctions.LossFunction.MSE).build(), "dense1")
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.setOutputs("out").build();
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//Cycle detection happens in ComputationGraph.init()
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ComputationGraph graph = new ComputationGraph(conf);
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graph.init();
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fail("No exception thrown for invalid configuration");
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} catch (IllegalStateException e) {
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//OK - exception is good
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//e.printStackTrace();
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}
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//Test: input != inputType count mismatch
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try {
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new NeuralNetConfiguration.Builder().graphBuilder().addInputs("input1", "input2")
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.setInputTypes(new InputType.InputTypeRecurrent(10, 12))
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.addLayer("cnn1",
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new ConvolutionLayer.Builder(2, 2).stride(2, 2).nIn(1).nOut(5)
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.build(),
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"input1")
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.addLayer("cnn2",
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new ConvolutionLayer.Builder(2, 2).stride(2, 2).nIn(1).nOut(5)
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.build(),
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"input2")
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.addVertex("merge1", new MergeVertex(), "cnn1", "cnn2")
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.addVertex("subset1", new SubsetVertex(0, 1), "merge1")
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.addLayer("dense1", new DenseLayer.Builder().nIn(20).nOut(5).build(), "subset1")
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.addLayer("dense2", new DenseLayer.Builder().nIn(20).nOut(5).build(), "subset1")
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.addVertex("add", new ElementWiseVertex(ElementWiseVertex.Op.Add), "dense1",
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"dense2")
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.addLayer("out", new OutputLayer.Builder().nIn(1).nOut(1).activation(Activation.TANH).lossFunction(LossFunctions.LossFunction.MSE).build(), "add")
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.setOutputs("out").build();
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fail("No exception thrown for invalid configuration");
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} catch (IllegalArgumentException e) {
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//OK - exception is good
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//e.printStackTrace();
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}
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}
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@Test
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public void testConfigurationWithRuntimeJSONSubtypes() {
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//Idea: suppose someone wants to use a ComputationGraph with a custom GraphVertex
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// (i.e., one not built into DL4J). Check that this works for JSON serialization
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// using runtime/reflection subtype mechanism in ComputationGraphConfiguration.fromJson()
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//Check a standard GraphVertex implementation, plus a static inner graph vertex
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ComputationGraphConfiguration conf = new NeuralNetConfiguration.Builder().graphBuilder().addInputs("in")
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.addVertex("test", new TestGraphVertex(3, 7), "in")
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.addVertex("test2", new StaticInnerGraphVertex(4, 5), "in").setOutputs("test", "test2").build();
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String json = conf.toJson();
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System.out.println(json);
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ComputationGraphConfiguration conf2 = ComputationGraphConfiguration.fromJson(json);
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assertEquals(conf, conf2);
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assertEquals(json, conf2.toJson());
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TestGraphVertex tgv = (TestGraphVertex) conf2.getVertices().get("test");
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assertEquals(3, tgv.getFirstVal());
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assertEquals(7, tgv.getSecondVal());
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StaticInnerGraphVertex sigv = (StaticInnerGraphVertex) conf.getVertices().get("test2");
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assertEquals(4, sigv.getFirstVal());
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assertEquals(5, sigv.getSecondVal());
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}
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@Test
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public void testOutputOrderDoesntChangeWhenCloning() {
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ComputationGraphConfiguration conf = new NeuralNetConfiguration.Builder().graphBuilder().addInputs("in")
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.addLayer("out1", new OutputLayer.Builder().nIn(1).nOut(1).build(), "in")
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.addLayer("out2", new OutputLayer.Builder().nIn(1).nOut(1).build(), "in")
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.addLayer("out3", new OutputLayer.Builder().nIn(1).nOut(1).build(), "in")
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.validateOutputLayerConfig(false)
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.setOutputs("out1", "out2", "out3").build();
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ComputationGraphConfiguration cloned = conf.clone();
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String json = conf.toJson();
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String jsonCloned = cloned.toJson();
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assertEquals(json, jsonCloned);
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}
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@Test
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public void testAllowDisconnectedLayers() {
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ComputationGraphConfiguration conf = new NeuralNetConfiguration.Builder().graphBuilder().addInputs("in")
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.addLayer("bidirectional",
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new Bidirectional(new LSTM.Builder().activation(Activation.TANH).nOut(10).build()),
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"in")
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.addLayer("out", new RnnOutputLayer.Builder().nOut(6)
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.lossFunction(LossFunctions.LossFunction.MCXENT)
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.activation(Activation.SOFTMAX)
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.build(), "bidirectional")
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.addLayer("disconnected_layer",
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new Bidirectional(new LSTM.Builder().activation(Activation.TANH).nOut(10).build()),
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"in")
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.setOutputs("out")
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.setInputTypes(new InputType.InputTypeRecurrent(10, 12))
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.allowDisconnected(true)
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.build();
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ComputationGraph graph = new ComputationGraph(conf);
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graph.init();
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}
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@Test
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public void testBidirectionalGraphSummary() {
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ComputationGraphConfiguration conf = new NeuralNetConfiguration.Builder().graphBuilder().addInputs("in")
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.addLayer("bidirectional",
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new Bidirectional(new LSTM.Builder().activation(Activation.TANH).nOut(10).build()),
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"in")
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.addLayer("out", new RnnOutputLayer.Builder().nOut(6)
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.lossFunction(LossFunctions.LossFunction.MCXENT)
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.activation(Activation.SOFTMAX)
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.build(), "bidirectional")
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.setOutputs("out")
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.setInputTypes(new InputType.InputTypeRecurrent(10, 12))
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.build();
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ComputationGraph graph = new ComputationGraph(conf);
|
||
|
|
graph.init();
|
||
|
|
graph.summary();
|
||
|
|
}
|
||
|
|
|
||
|
|
@AllArgsConstructor
|
||
|
|
@NoArgsConstructor
|
||
|
|
@Data
|
||
|
|
@EqualsAndHashCode(callSuper = false)
|
||
|
|
public static class StaticInnerGraphVertex extends GraphVertex {
|
||
|
|
|
||
|
|
private int firstVal;
|
||
|
|
private int secondVal;
|
||
|
|
|
||
|
|
@Override
|
||
|
|
public GraphVertex clone() {
|
||
|
|
return new TestGraphVertex(firstVal, secondVal);
|
||
|
|
}
|
||
|
|
|
||
|
|
@Override
|
||
|
|
public long numParams(boolean backprop) {
|
||
|
|
return 0;
|
||
|
|
}
|
||
|
|
|
||
|
|
@Override
|
||
|
|
public int minVertexInputs() {
|
||
|
|
return 1;
|
||
|
|
}
|
||
|
|
|
||
|
|
@Override
|
||
|
|
public int maxVertexInputs() {
|
||
|
|
return 1;
|
||
|
|
}
|
||
|
|
|
||
|
|
@Override
|
||
|
|
public org.deeplearning4j.nn.graph.vertex.GraphVertex instantiate(ComputationGraph graph, String name, int idx,
|
||
|
|
INDArray paramsView, boolean initializeParams, DataType networkDatatype) {
|
||
|
|
throw new UnsupportedOperationException("Not supported");
|
||
|
|
}
|
||
|
|
|
||
|
|
@Override
|
||
|
|
public InputType getOutputType(int layerIndex, InputType... vertexInputs) throws InvalidInputTypeException {
|
||
|
|
throw new UnsupportedOperationException();
|
||
|
|
}
|
||
|
|
|
||
|
|
@Override
|
||
|
|
public MemoryReport getMemoryReport(InputType... inputTypes) {
|
||
|
|
throw new UnsupportedOperationException();
|
||
|
|
}
|
||
|
|
}
|
||
|
|
|
||
|
|
|
||
|
|
@Test
|
||
|
|
public void testInvalidOutputLayer(){
|
||
|
|
/*
|
||
|
|
Test case (invalid configs)
|
||
|
|
1. nOut=1 + softmax
|
||
|
|
2. mcxent + tanh
|
||
|
|
3. xent + softmax
|
||
|
|
4. xent + relu
|
||
|
|
5. mcxent + sigmoid
|
||
|
|
*/
|
||
|
|
|
||
|
|
LossFunctions.LossFunction[] lf = new LossFunctions.LossFunction[]{
|
||
|
|
LossFunctions.LossFunction.MCXENT, LossFunctions.LossFunction.MCXENT, LossFunctions.LossFunction.XENT,
|
||
|
|
LossFunctions.LossFunction.XENT, LossFunctions.LossFunction.MCXENT};
|
||
|
|
int[] nOut = new int[]{1, 3, 3, 3, 3};
|
||
|
|
Activation[] activations = new Activation[]{Activation.SOFTMAX, Activation.TANH, Activation.SOFTMAX, Activation.RELU, Activation.SIGMOID};
|
||
|
|
for( int i=0; i<lf.length; i++ ){
|
||
|
|
for(boolean lossLayer : new boolean[]{false, true}) {
|
||
|
|
for (boolean validate : new boolean[]{true, false}) {
|
||
|
|
String s = "nOut=" + nOut[i] + ",lossFn=" + lf[i] + ",lossLayer=" + lossLayer + ",validate=" + validate;
|
||
|
|
if(nOut[i] == 1 && lossLayer)
|
||
|
|
continue; //nOuts are not availabel in loss layer, can't expect it to detect this case
|
||
|
|
try {
|
||
|
|
new NeuralNetConfiguration.Builder()
|
||
|
|
.graphBuilder()
|
||
|
|
.addInputs("in")
|
||
|
|
.layer("0", new DenseLayer.Builder().nIn(10).nOut(10).build(), "in")
|
||
|
|
.layer("1",
|
||
|
|
!lossLayer ? new OutputLayer.Builder().nIn(10).nOut(nOut[i]).activation(activations[i]).lossFunction(lf[i]).build()
|
||
|
|
: new LossLayer.Builder().activation(activations[i]).lossFunction(lf[i]).build(), "0")
|
||
|
|
.setOutputs("1")
|
||
|
|
.validateOutputLayerConfig(validate)
|
||
|
|
.build();
|
||
|
|
if (validate) {
|
||
|
|
fail("Expected exception: " + s);
|
||
|
|
}
|
||
|
|
} catch (DL4JInvalidConfigException e) {
|
||
|
|
if (validate) {
|
||
|
|
assertTrue(s, e.getMessage().toLowerCase().contains("invalid output"));
|
||
|
|
} else {
|
||
|
|
fail("Validation should not be enabled");
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|