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Java example source code file (MultiLayerNeuralNetConfigurationTest.java)

This example Java source code file (MultiLayerNeuralNetConfigurationTest.java) is included in the alvinalexander.com "Java Source Code Warehouse" project. The intent of this project is to help you "Learn Java by Example" TM.

Learn more about this Java project at its project page.

Java - Java tags/keywords

bufferedinputstream, bufferedoutputstream, exception, file, fileoutputstream, multilayerconfiguration, multilayernetwork, normaldistribution, properties, reshapepreprocessor, string, subsamplinglayer, test, unexpected, util

The MultiLayerNeuralNetConfigurationTest.java Java example source code

/*
 *
 *  * Copyright 2015 Skymind,Inc.
 *  *
 *  *    Licensed under the Apache License, Version 2.0 (the "License");
 *  *    you may not use this file except in compliance with the License.
 *  *    You may obtain a copy of the License at
 *  *
 *  *        http://www.apache.org/licenses/LICENSE-2.0
 *  *
 *  *    Unless required by applicable law or agreed to in writing, software
 *  *    distributed under the License is distributed on an "AS IS" BASIS,
 *  *    WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 *  *    See the License for the specific language governing permissions and
 *  *    limitations under the License.
 *
 */

package org.deeplearning4j.nn.conf;

import static org.junit.Assert.*;

import org.deeplearning4j.nn.api.Layer;
import org.deeplearning4j.nn.api.OptimizationAlgorithm;
import org.deeplearning4j.nn.conf.distribution.NormalDistribution;
import org.deeplearning4j.nn.conf.layers.*;
import org.deeplearning4j.nn.conf.layers.setup.ConvolutionLayerSetup;
import org.deeplearning4j.nn.conf.preprocessor.ReshapePreProcessor;
import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
import org.deeplearning4j.nn.weights.WeightInit;
import org.deeplearning4j.optimize.api.IterationListener;
import org.deeplearning4j.optimize.listeners.ScoreIterationListener;
import org.junit.Test;
import org.nd4j.linalg.factory.Nd4j;
import org.nd4j.linalg.lossfunctions.LossFunctions;

import java.io.*;
import java.util.Arrays;
import java.util.Collections;
import java.util.Properties;

/**
 * Created by agibsonccc on 11/27/14.
 */
public class MultiLayerNeuralNetConfigurationTest {

    @Test
    public void testJson() throws Exception {
        MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()
                .list()
                .layer(0,new RBM.Builder().dist(new NormalDistribution(1, 1e-1)).build())
                .inputPreProcessor(0, new ReshapePreProcessor())
                .build();

        String json = conf.toJson();
        MultiLayerConfiguration from = MultiLayerConfiguration.fromJson(json);
        assertEquals(conf.getConf(0),from.getConf(0));

        Properties props = new Properties();
        props.put("json",json);
        String key = props.getProperty("json");
        assertEquals(json,key);
        File f = new File("props");
        f.deleteOnExit();
        BufferedOutputStream bos = new BufferedOutputStream(new FileOutputStream(f));
        props.store(bos,"");
        bos.flush();
        bos.close();
        BufferedInputStream bis = new BufferedInputStream(new FileInputStream(f));
        Properties props2 = new Properties();
        props2.load(bis);
        bis.close();
        assertEquals(props2.getProperty("json"),props.getProperty("json"));
        String json2 = props2.getProperty("json");
        MultiLayerConfiguration conf3 = MultiLayerConfiguration.fromJson(json2);
        assertEquals(conf.getConf(0),conf3.getConf(0));

    }

    @Test
    public void testConvnetJson() {
        final int numRows = 75;
        final int numColumns = 75;
        int nChannels = 3;
        int outputNum = 6;
        int batchSize = 500;
        int iterations = 10;
        int seed = 123;

        //setup the network
        MultiLayerConfiguration.Builder builder = new NeuralNetConfiguration.Builder()
                .seed(seed)
                .iterations(iterations).regularization(true)
                .l1(1e-1).l2(2e-4).useDropConnect(true).dropOut(0.5)
                .miniBatch(true)
                .optimizationAlgo(OptimizationAlgorithm.CONJUGATE_GRADIENT)
                .list()
                .layer(0, new ConvolutionLayer.Builder(5, 5)
                        .nOut(5).dropOut(0.5)
                        .weightInit(WeightInit.XAVIER)
                        .activation("relu")
                        .build())

                .layer(1, new SubsamplingLayer
                        .Builder(SubsamplingLayer.PoolingType.MAX, new int[]{2, 2})
                        .build())
                .layer(2, new ConvolutionLayer.Builder(3, 3)
                        .nOut(10).dropOut(0.5)
                        .weightInit(WeightInit.XAVIER)
                        .activation("relu")
                        .build())
                .layer(3, new SubsamplingLayer
                        .Builder(SubsamplingLayer.PoolingType.MAX, new int[]{2, 2})
                        .build())
                .layer(4, new DenseLayer.Builder().nOut(100).activation("relu")
                        .build())

                .layer(5, new OutputLayer.Builder(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD)
                        .nOut(outputNum)
                        .weightInit(WeightInit.XAVIER)
                        .activation("softmax")
                        .build())
                .backprop(true).pretrain(false);

        new ConvolutionLayerSetup(builder,numRows,numColumns,nChannels);
        MultiLayerConfiguration conf = builder.build();
        String json = conf.toJson();
        MultiLayerConfiguration conf2 = MultiLayerConfiguration.fromJson(json);
        assertEquals(conf, conf2);
    }


    @Test
    public void testYaml() throws Exception {
        MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()
                .list()
                .layer(0, new RBM.Builder().dist(new NormalDistribution(1, 1e-1)).build())
                .inputPreProcessor(0, new ReshapePreProcessor())
                .build();
        String json = conf.toYaml();
        MultiLayerConfiguration from = MultiLayerConfiguration.fromYaml(json);
        assertEquals(conf.getConf(0),from.getConf(0));

        Properties props = new Properties();
        props.put("json",json);
        String key = props.getProperty("json");
        assertEquals(json,key);
        File f = new File("props");
        f.deleteOnExit();
        BufferedOutputStream bos = new BufferedOutputStream(new FileOutputStream(f));
        props.store(bos,"");
        bos.flush();
        bos.close();
        BufferedInputStream bis = new BufferedInputStream(new FileInputStream(f));
        Properties props2 = new Properties();
        props2.load(bis);
        bis.close();
        assertEquals(props2.getProperty("json"),props.getProperty("json"));
        String yaml = props2.getProperty("json");
        MultiLayerConfiguration conf3 = MultiLayerConfiguration.fromYaml(yaml);
        assertEquals(conf.getConf(0),conf3.getConf(0));

    }

    @Test
    public void testClone() {
        MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()
                .list()
                .layer(0, new RBM.Builder().build())
                .layer(1, new OutputLayer.Builder().build())
                .inputPreProcessor(1, new ReshapePreProcessor(new int[] {1,2}, new int[] {3,4}))
                .build();

        MultiLayerConfiguration conf2 = conf.clone();

        assertEquals(conf, conf2);
        assertNotSame(conf, conf2);
        assertNotSame(conf.getConfs(), conf2.getConfs());
        for(int i = 0; i < conf.getConfs().size(); i++) {
            assertNotSame(conf.getConf(i), conf2.getConf(i));
        }
        assertNotSame(conf.getInputPreProcessors(), conf2.getInputPreProcessors());
        for(Integer layer : conf.getInputPreProcessors().keySet()) {
            assertNotSame(conf.getInputPreProcess(layer), conf2.getInputPreProcess(layer));
        }
    }

    @Test
    public void testRandomWeightInit() {
        MultiLayerNetwork model1 = new MultiLayerNetwork(getConf());
        model1.init();

        Nd4j.getRandom().setSeed(12345L);
        MultiLayerNetwork model2 = new MultiLayerNetwork(getConf());
        model2.init();

        float[] p1 = model1.params().data().asFloat();
        float[] p2 = model2.params().data().asFloat();
        System.out.println(Arrays.toString(p1));
        System.out.println(Arrays.toString(p2));

        org.junit.Assert.assertArrayEquals(p1, p2, 0.0f);
    }

    @Test
    public void testIterationListener(){
        MultiLayerNetwork model1 = new MultiLayerNetwork(getConf());
        model1.init();
        model1.setListeners(Collections.singletonList((IterationListener) new ScoreIterationListener(1)));

        MultiLayerNetwork model2 = new MultiLayerNetwork(getConf());
        model2.setListeners(Collections.singletonList((IterationListener) new ScoreIterationListener(1)));
        model2.init();

        Layer[] l1 = model1.getLayers();
        for( int i = 0; i < l1.length; i++ )
            assertTrue(l1[i].getListeners() != null && l1[i].getListeners().size() == 1);

        Layer[] l2 = model2.getLayers();
        for( int i = 0; i < l2.length; i++ )
            assertTrue(l2[i].getListeners() != null && l2[i].getListeners().size() == 1);
    }


    private static MultiLayerConfiguration getConf(){
        MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()
                .seed(12345l)
                .list()
                .layer(0, new RBM.Builder()
                        .nIn(2).nOut(2)
                        .weightInit(WeightInit.DISTRIBUTION).dist(new NormalDistribution(0, 1))
                        .build())
                .layer(1, new OutputLayer.Builder()
                        .nIn(2).nOut(1)
                        .weightInit(WeightInit.DISTRIBUTION).dist(new NormalDistribution(0, 1))
                        .build())
                .build();
        return conf;
    }

    @Test
    public void testInvalidConfig(){

        try {
            MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()
                    .seed(12345)
                    .list()
                    .pretrain(false).backprop(true)
                    .build();
            MultiLayerNetwork net = new MultiLayerNetwork(conf);
            net.init();
            fail("No exception thrown for invalid configuration");
        } catch(IllegalStateException e){
            //OK
            e.printStackTrace();
        } catch(Throwable e){
            e.printStackTrace();
            fail("Unexpected exception thrown for invalid config");
        }

        try {
            MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()
                    .seed(12345)
                    .list()
                    .layer(1, new DenseLayer.Builder().nIn(3).nOut(4).build())
                    .layer(2, new OutputLayer.Builder().nIn(4).nOut(5).build())
                    .pretrain(false).backprop(true)
                    .build();
            MultiLayerNetwork net = new MultiLayerNetwork(conf);
            net.init();
            fail("No exception thrown for invalid configuration");
        } catch(IllegalStateException e){
            //OK
            e.printStackTrace();
        } catch(Throwable e){
            e.printStackTrace();
            fail("Unexpected exception thrown for invalid config");
        }

        try {
            MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()
                    .seed(12345)
                    .list()
                    .layer(0, new DenseLayer.Builder().nIn(3).nOut(4).build())
                    .layer(2, new OutputLayer.Builder().nIn(4).nOut(5).build())
                    .pretrain(false).backprop(true)
                    .build();
            MultiLayerNetwork net = new MultiLayerNetwork(conf);
            net.init();
            fail("No exception thrown for invalid configuration");
        } catch(IllegalStateException e){
            //OK
            e.printStackTrace();
        } catch(Throwable e){
            e.printStackTrace();
            fail("Unexpected exception thrown for invalid config");
        }
    }

    @Test
    public void testListOverloads(){

        MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()
                .seed(12345)
                .list()
                .layer(0, new DenseLayer.Builder().nIn(3).nOut(4).build())
                .layer(1, new OutputLayer.Builder().nIn(4).nOut(5).build())
                .pretrain(false).backprop(true)
                .build();
        MultiLayerNetwork net = new MultiLayerNetwork(conf);
        net.init();

        DenseLayer dl = (DenseLayer)conf.getConf(0).getLayer();
        assertEquals(3,dl.getNIn());
        assertEquals(4,dl.getNOut());
        OutputLayer ol = (OutputLayer)conf.getConf(1).getLayer();
        assertEquals(4,ol.getNIn());
        assertEquals(5,ol.getNOut());

        MultiLayerConfiguration conf2 = new NeuralNetConfiguration.Builder()
                .seed(12345)
                .list()
                .layer(0, new DenseLayer.Builder().nIn(3).nOut(4).build())
                .layer(1, new OutputLayer.Builder().nIn(4).nOut(5).build())
                .pretrain(false).backprop(true)
                .build();
        MultiLayerNetwork net2 = new MultiLayerNetwork(conf2);
        net2.init();

        MultiLayerConfiguration conf3 = new NeuralNetConfiguration.Builder()
                .seed(12345)
                .list(
                    new DenseLayer.Builder().nIn(3).nOut(4).build(),
                    new OutputLayer.Builder().nIn(4).nOut(5).build())
                .pretrain(false).backprop(true)
                .build();
        MultiLayerNetwork net3 = new MultiLayerNetwork(conf3);
        net3.init();


        assertEquals(conf, conf2);
        assertEquals(conf, conf3);
    }

}

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