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Java example source code file (TestSetGetParameters.java)

This example Java source code file (TestSetGetParameters.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

different, indarray, map, multilayerconfiguration, multilayernetwork, normaldistribution, params, same, string, test, testsetgetparameters, util

The TestSetGetParameters.java Java example source code

package org.deeplearning4j.nn.multilayer;

import static org.junit.Assert.*;

import java.util.Map;

import org.deeplearning4j.nn.conf.MultiLayerConfiguration;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.deeplearning4j.nn.conf.distribution.NormalDistribution;
import org.deeplearning4j.nn.conf.layers.*;
import org.deeplearning4j.nn.weights.WeightInit;
import org.junit.Test;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.factory.Nd4j;
import org.nd4j.linalg.lossfunctions.LossFunctions.LossFunction;

public class TestSetGetParameters {

    @Test
    public void testSetParameters() {
        //Set up a MLN, then do set(get) on parameters. Results should be identical compared to before doing this.
        MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()
                .list()
                .layer(0, new DenseLayer.Builder().nIn(9).nOut(10)
                        .weightInit(WeightInit.DISTRIBUTION).dist(new NormalDistribution(0, 1)).build())
                .layer(1, new RBM.Builder().nIn(10).nOut(11)
                        .weightInit(WeightInit.DISTRIBUTION).dist(new NormalDistribution(0, 1)).build())
                .layer(2, new AutoEncoder.Builder().corruptionLevel(0.5).nIn(11).nOut(12)
                        .weightInit(WeightInit.DISTRIBUTION).dist(new NormalDistribution(0, 1)).build())
                .layer(3, new OutputLayer.Builder(LossFunction.MSE).nIn(12).nOut(12)
                        .weightInit(WeightInit.DISTRIBUTION).dist(new NormalDistribution(0, 1)).build())
                .build();

        MultiLayerNetwork net = new MultiLayerNetwork(conf);
        net.init();

        INDArray initParams = net.params().dup();
        Map<String, INDArray> initParams2 = net.paramTable();

        net.setParams(net.params());

        INDArray initParamsAfter = net.params();
        Map<String, INDArray> initParams2After = net.paramTable();

        for (String s : initParams2.keySet()) {
            assertTrue("Params differ: " + s, initParams2.get(s).equals(initParams2After.get(s)));
        }

        assertEquals(initParams, initParamsAfter);

        //Now, try the other way: get(set(random))
        INDArray randomParams = Nd4j.rand(initParams.shape());
        net.setParams(randomParams.dup());

        assertEquals(net.params(), randomParams);
    }

    @Test
    public void testSetParametersRNN() {
        //Set up a MLN, then do set(get) on parameters. Results should be identical compared to before doing this.

        MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()
                .list()
                .layer(0, new GravesLSTM.Builder().nIn(9).nOut(10).weightInit(WeightInit.DISTRIBUTION)
                        .dist(new NormalDistribution(0, 1)).build())
                .layer(1, new GravesLSTM.Builder().nIn(10).nOut(11).weightInit(WeightInit.DISTRIBUTION)
                        .dist(new NormalDistribution(0, 1)).build())
                .layer(2, new RnnOutputLayer.Builder(LossFunction.MSE).weightInit(WeightInit.DISTRIBUTION)
                        .dist(new NormalDistribution(0, 1)).nIn(11).nOut(12).build())
                .build();

        MultiLayerNetwork net = new MultiLayerNetwork(conf);
        net.init();

        INDArray initParams = net.params().dup();
        Map<String, INDArray> initParams2 = net.paramTable();

        net.setParams(net.params());

        INDArray initParamsAfter = net.params();
        Map<String, INDArray> initParams2After = net.paramTable();

        for (String s : initParams2.keySet()) {
            assertTrue("Params differ: " + s, initParams2.get(s).equals(initParams2After.get(s)));
        }

        assertEquals(initParams, initParamsAfter);

        //Now, try the other way: get(set(random))
        INDArray randomParams = Nd4j.rand(initParams.shape());
        net.setParams(randomParams.dup());

        assertEquals(net.params(), randomParams);
    }

    @Test
    public void testInitWithParams() {

        Nd4j.getRandom().setSeed(12345);

        //Create configuration. Doesn't matter if this doesn't actually work for forward/backward pass here
        MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()
                .seed(12345)
                .list()
                .layer(0, new DenseLayer.Builder().nIn(10).nOut(10).build())
                .layer(1, new GravesLSTM.Builder().nIn(10).nOut(10).build())
                .layer(2, new GravesBidirectionalLSTM.Builder().nIn(10).nOut(10).build())
                .layer(3, new ConvolutionLayer.Builder().nIn(10).nOut(10).kernelSize(2, 2).stride(2, 2).padding(2, 2).build())
                .layer(4, new OutputLayer.Builder(LossFunction.MCXENT).nIn(10).nOut(10).build())
                .pretrain(false).backprop(true).build();

        MultiLayerNetwork net = new MultiLayerNetwork(conf);
        net.init();
        INDArray params = net.params();


        MultiLayerNetwork net2 = new MultiLayerNetwork(conf);
        net2.init(params, true);

        MultiLayerNetwork net3 = new MultiLayerNetwork(conf);
        net3.init(params, false);

        assertEquals(params, net2.params());
        assertEquals(params, net3.params());

        assertFalse(params == net2.params());    //Different objects due to clone
        assertTrue(params == net3.params());    //Same object due to clone


        Map<String, INDArray> paramsMap = net.paramTable();
        Map<String, INDArray> paramsMap2 = net2.paramTable();
        Map<String, INDArray> paramsMap3 = net3.paramTable();
        for (String s : paramsMap.keySet()) {
            assertEquals(paramsMap.get(s), paramsMap2.get(s));
            assertEquals(paramsMap.get(s), paramsMap3.get(s));
        }
    }
}

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