416 lines
16 KiB
Java
416 lines
16 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.exceptions;
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import org.deeplearning4j.BaseDL4JTest;
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import org.deeplearning4j.exception.DL4JException;
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import org.deeplearning4j.nn.conf.ConvolutionMode;
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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.inputs.InputType;
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import org.deeplearning4j.nn.conf.layers.*;
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import org.deeplearning4j.nn.conf.preprocessor.CnnToFeedForwardPreProcessor;
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import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
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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.factory.Nd4j;
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import static org.junit.Assert.fail;
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/**
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* A set of tests to ensure that useful exceptions are thrown on invalid network configurations
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*/
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public class TestInvalidConfigurations extends BaseDL4JTest {
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public static MultiLayerNetwork getDensePlusOutput(int nIn, int nOut) {
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MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().list()
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.layer(0, new DenseLayer.Builder().nIn(nIn).nOut(10).build())
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.layer(1, new OutputLayer.Builder().nIn(10).nOut(nOut).build()).build();
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MultiLayerNetwork net = new MultiLayerNetwork(conf);
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net.init();
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return net;
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}
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public static MultiLayerNetwork getLSTMPlusRnnOutput(int nIn, int nOut) {
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MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().list()
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.layer(0, new GravesLSTM.Builder().nIn(nIn).nOut(10).build())
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.layer(1, new RnnOutputLayer.Builder().nIn(10).nOut(nOut).build()).build();
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MultiLayerNetwork net = new MultiLayerNetwork(conf);
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net.init();
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return net;
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}
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public static MultiLayerNetwork getCnnPlusOutputLayer(int depthIn, int inH, int inW, int nOut) {
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MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().list()
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.layer(0, new ConvolutionLayer.Builder().nIn(depthIn).nOut(5).build())
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.layer(1, new OutputLayer.Builder().nOut(nOut).build())
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.setInputType(InputType.convolutional(inH, inW, depthIn)).build();
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MultiLayerNetwork net = new MultiLayerNetwork(conf);
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net.init();
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return net;
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}
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@Test
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public void testDenseNin0() {
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try {
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getDensePlusOutput(0, 10);
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fail("Expected exception");
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} catch (DL4JException e) {
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System.out.println("testDenseNin0(): " + e.getMessage());
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} catch (Exception e) {
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e.printStackTrace();
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fail();
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}
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}
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@Test
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public void testDenseNout0() {
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try {
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MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().list()
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.layer(0, new DenseLayer.Builder().nIn(10).nOut(0).build())
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.layer(1, new OutputLayer.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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fail("Expected exception");
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} catch (DL4JException e) {
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System.out.println("testDenseNout0(): " + e.getMessage());
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} catch (Exception e) {
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e.printStackTrace();
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fail();
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}
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}
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@Test
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public void testOutputLayerNin0() {
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try {
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getDensePlusOutput(10, 0);
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fail("Expected exception");
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} catch (DL4JException e) {
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System.out.println("testOutputLayerNin0(): " + e.getMessage());
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} catch (Exception e) {
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e.printStackTrace();
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fail();
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}
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}
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@Test
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public void testRnnOutputLayerNin0() {
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try {
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getLSTMPlusRnnOutput(10, 0);
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fail("Expected exception");
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} catch (DL4JException e) {
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System.out.println("testRnnOutputLayerNin0(): " + e.getMessage());
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} catch (Exception e) {
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e.printStackTrace();
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fail();
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}
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}
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@Test
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public void testLSTMNIn0() {
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try {
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getLSTMPlusRnnOutput(0, 10);
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fail("Expected exception");
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} catch (DL4JException e) {
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System.out.println("testLSTMNIn0(): " + e.getMessage());
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} catch (Exception e) {
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e.printStackTrace();
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fail();
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}
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}
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@Test
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public void testLSTMNOut0() {
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try {
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MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().list()
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.layer(0, new GravesLSTM.Builder().nIn(10).nOut(0).build())
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.layer(1, new RnnOutputLayer.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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fail("Expected exception");
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} catch (DL4JException e) {
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System.out.println("testLSTMNOut0(): " + e.getMessage());
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} catch (Exception e) {
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e.printStackTrace();
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fail();
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}
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}
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@Test
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public void testConvolutionalNin0() {
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try {
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getCnnPlusOutputLayer(0, 10, 10, 10);
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fail("Expected exception");
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} catch (DL4JException e) {
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System.out.println("testConvolutionalNin0(): " + e.getMessage());
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} catch (Exception e) {
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e.printStackTrace();
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fail();
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}
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}
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@Test
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public void testConvolutionalNOut0() {
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try {
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MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().list()
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.layer(0, new ConvolutionLayer.Builder().nIn(5).nOut(0).build())
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.layer(1, new OutputLayer.Builder().nOut(10).build())
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.setInputType(InputType.convolutional(10, 10, 5)).build();
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MultiLayerNetwork net = new MultiLayerNetwork(conf);
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net.init();
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fail("Expected exception");
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} catch (DL4JException e) {
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System.out.println("testConvolutionalNOut0(): " + e.getMessage());
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} catch (Exception e) {
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e.printStackTrace();
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fail();
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}
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}
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@Test
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public void testCnnInvalidConfigPaddingStridesHeight() {
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//Idea: some combination of padding/strides are invalid.
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int depthIn = 3;
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int hIn = 10;
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int wIn = 10;
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//Using kernel size of 3, stride of 2:
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//(10-3+2*0)/2+1 = 7/2 + 1
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try {
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MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().convolutionMode(ConvolutionMode.Strict)
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.list()
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.layer(0, new ConvolutionLayer.Builder().kernelSize(3, 2).stride(2, 2).padding(0, 0).nOut(5)
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.build())
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.layer(1, new OutputLayer.Builder().nOut(10).build())
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.setInputType(InputType.convolutional(hIn, wIn, depthIn)).build();
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MultiLayerNetwork net = new MultiLayerNetwork(conf);
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net.init();
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fail("Expected exception");
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} catch (DL4JException e) {
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System.out.println("testCnnInvalidConfigPaddingStridesHeight(): " + e.getMessage());
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} catch (Exception e) {
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e.printStackTrace();
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fail();
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}
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}
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@Test
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public void testCnnInvalidConfigOrInput_SmallerDataThanKernel() {
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//Idea: same as testCnnInvalidConfigPaddingStridesHeight() but network is fed incorrect sized data
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// or equivalently, network is set up without using InputType functionality (hence missing validation there)
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int depthIn = 3;
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int hIn = 10;
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int wIn = 10;
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MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().list()
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.layer(0, new ConvolutionLayer.Builder().kernelSize(7, 7).stride(1, 1).padding(0, 0).nOut(5)
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.build())
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.layer(1, new OutputLayer.Builder().nOut(10).activation(Activation.SOFTMAX).build())
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.setInputType(InputType.convolutional(hIn, wIn, depthIn)).build();
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MultiLayerNetwork net = new MultiLayerNetwork(conf);
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net.init();
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try {
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net.feedForward(Nd4j.create(3, depthIn, 5, 5));
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fail("Expected exception");
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} catch (DL4JException e) {
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System.out.println("testCnnInvalidConfigOrInput_SmallerDataThanKernel(): " + e.getMessage());
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} catch (Exception e) {
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e.printStackTrace();
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fail();
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}
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}
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@Test
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public void testCnnInvalidConfigOrInput_BadStrides() {
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//Idea: same as testCnnInvalidConfigPaddingStridesHeight() but network is fed incorrect sized data
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// or equivalently, network is set up without using InputType functionality (hence missing validation there)
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int depthIn = 3;
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int hIn = 10;
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int wIn = 10;
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//Invalid: (10-3+0)/2+1 = 4.5
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MultiLayerConfiguration conf =
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new NeuralNetConfiguration.Builder().convolutionMode(ConvolutionMode.Strict).list()
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.layer(0, new ConvolutionLayer.Builder().kernelSize(3, 3).stride(2, 2)
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.padding(0, 0).nIn(depthIn).nOut(5).build())
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.layer(1, new OutputLayer.Builder().nIn(5 * 4 * 4).nOut(10).activation(Activation.SOFTMAX).build())
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.inputPreProcessor(1, new CnnToFeedForwardPreProcessor()).build();
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MultiLayerNetwork net = new MultiLayerNetwork(conf);
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net.init();
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try {
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net.feedForward(Nd4j.create(3, depthIn, hIn, wIn));
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fail("Expected exception");
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} catch (DL4JException e) {
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System.out.println("testCnnInvalidConfigOrInput_BadStrides(): " + e.getMessage());
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} catch (Exception e) {
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e.printStackTrace();
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fail();
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}
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}
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@Test
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public void testCnnInvalidConfigPaddingStridesWidth() {
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//Idea: some combination of padding/strides are invalid.
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int depthIn = 3;
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int hIn = 10;
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int wIn = 10;
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//Using kernel size of 3, stride of 2:
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//(10-3+2*0)/2+1 = 7/2 + 1
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try {
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MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().list()
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.layer(0, new ConvolutionLayer.Builder().kernelSize(2, 3).stride(2, 2).padding(0, 0).nOut(5)
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.build())
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.layer(1, new OutputLayer.Builder().nOut(10).activation(Activation.SOFTMAX).build())
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.setInputType(InputType.convolutional(hIn, wIn, depthIn)).build();
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} catch (Exception e) {
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fail("Did not expect exception with default (truncate)");
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}
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try {
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MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().convolutionMode(ConvolutionMode.Strict)
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.list()
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.layer(0, new ConvolutionLayer.Builder().kernelSize(2, 3).stride(2, 2).padding(0, 0).nOut(5)
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.build())
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.layer(1, new OutputLayer.Builder().nOut(10).build())
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.setInputType(InputType.convolutional(hIn, wIn, depthIn)).build();
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MultiLayerNetwork net = new MultiLayerNetwork(conf);
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net.init();
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fail("Expected exception");
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} catch (DL4JException e) {
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System.out.println("testCnnInvalidConfigPaddingStridesWidth(): " + e.getMessage());
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} catch (Exception e) {
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e.printStackTrace();
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fail();
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}
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}
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@Test
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public void testCnnInvalidConfigPaddingStridesWidthSubsampling() {
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//Idea: some combination of padding/strides are invalid.
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int depthIn = 3;
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int hIn = 10;
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int wIn = 10;
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//Using kernel size of 3, stride of 2:
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//(10-3+2*0)/2+1 = 7/2 + 1
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try {
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MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().convolutionMode(ConvolutionMode.Strict)
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.list()
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.layer(0, new SubsamplingLayer.Builder().kernelSize(2, 3).stride(2, 2).padding(0, 0)
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.build())
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.layer(1, new OutputLayer.Builder().nOut(10).build())
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.setInputType(InputType.convolutional(hIn, wIn, depthIn)).build();
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MultiLayerNetwork net = new MultiLayerNetwork(conf);
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net.init();
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fail("Expected exception");
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} catch (DL4JException e) {
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System.out.println("testCnnInvalidConfigPaddingStridesWidthSubsampling(): " + e.getMessage());
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} catch (Exception e) {
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e.printStackTrace();
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fail();
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}
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}
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@Test(expected = IllegalStateException.class)
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public void testCnnInvalidKernel() {
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new ConvolutionLayer.Builder().kernelSize(3, 0).build();
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}
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@Test(expected = IllegalArgumentException.class)
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public void testCnnInvalidKernel2() {
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new ConvolutionLayer.Builder().kernelSize(2, 2, 2).build();
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}
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@Test(expected = IllegalStateException.class)
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public void testCnnInvalidStride() {
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new ConvolutionLayer.Builder().kernelSize(3, 3).stride(0, 1).build();
|
||
|
|
}
|
||
|
|
|
||
|
|
@Test(expected = IllegalArgumentException.class)
|
||
|
|
public void testCnnInvalidStride2() {
|
||
|
|
new ConvolutionLayer.Builder().kernelSize(3, 3).stride(1).build();
|
||
|
|
}
|
||
|
|
|
||
|
|
@Test(expected = IllegalArgumentException.class)
|
||
|
|
public void testCnnInvalidPadding() {
|
||
|
|
new ConvolutionLayer.Builder().kernelSize(3, 3).stride(1, 1).padding(-1, 0).build();
|
||
|
|
}
|
||
|
|
|
||
|
|
@Test(expected = IllegalArgumentException.class)
|
||
|
|
public void testCnnInvalidPadding2() {
|
||
|
|
new ConvolutionLayer.Builder().kernelSize(3, 3).stride(1, 1).padding(0, 0, 0).build();
|
||
|
|
}
|
||
|
|
|
||
|
|
@Test(expected = IllegalStateException.class)
|
||
|
|
public void testSubsamplingInvalidKernel() {
|
||
|
|
new SubsamplingLayer.Builder().kernelSize(3, 0).build();
|
||
|
|
}
|
||
|
|
|
||
|
|
@Test(expected = IllegalArgumentException.class)
|
||
|
|
public void testSubsamplingInvalidKernel2() {
|
||
|
|
new SubsamplingLayer.Builder().kernelSize(2).build();
|
||
|
|
}
|
||
|
|
|
||
|
|
@Test(expected = IllegalStateException.class)
|
||
|
|
public void testSubsamplingInvalidStride() {
|
||
|
|
new SubsamplingLayer.Builder().kernelSize(3, 3).stride(0, 1).build();
|
||
|
|
}
|
||
|
|
|
||
|
|
@Test(expected = RuntimeException.class)
|
||
|
|
public void testSubsamplingInvalidStride2() {
|
||
|
|
new SubsamplingLayer.Builder().kernelSize(3, 3).stride(1, 1, 1).build();
|
||
|
|
}
|
||
|
|
|
||
|
|
@Test(expected = IllegalArgumentException.class)
|
||
|
|
public void testSubsamplingInvalidPadding() {
|
||
|
|
new SubsamplingLayer.Builder().kernelSize(3, 3).stride(1, 1).padding(-1, 0).build();
|
||
|
|
}
|
||
|
|
|
||
|
|
@Test(expected = RuntimeException.class)
|
||
|
|
public void testSubsamplingInvalidPadding2() {
|
||
|
|
new SubsamplingLayer.Builder().kernelSize(3, 3).stride(1, 1).padding(0).build();
|
||
|
|
}
|
||
|
|
|
||
|
|
}
|