cavis/deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/nn/multilayer/TestSetGetParameters.java

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package org.deeplearning4j.nn.multilayer;

import org.deeplearning4j.BaseDL4JTest;
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.junit.Test;
import org.nd4j.linalg.activations.Activation;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.factory.Nd4j;
import org.nd4j.linalg.lossfunctions.LossFunctions.LossFunction;

import java.util.Map;

import static org.junit.Assert.*;

public class TestSetGetParameters extends BaseDL4JTest {

    @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)
                                        .dist(new NormalDistribution(0, 1)).build())
                        .layer(1, new DenseLayer.Builder().nIn(10).nOut(11)
                                        .dist(new NormalDistribution(0, 1)).build())
                        .layer(2, new AutoEncoder.Builder().corruptionLevel(0.5).nIn(11).nOut(12)
                                        .dist(new NormalDistribution(0, 1)).build())
                        .layer(3, new OutputLayer.Builder(LossFunction.MSE).nIn(12).nOut(12)
                                        .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.dataType(), 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)
                                        .dist(new NormalDistribution(0, 1)).build())
                        .layer(1, new GravesLSTM.Builder().nIn(10).nOut(11)
                                        .dist(new NormalDistribution(0, 1)).build())
                        .layer(2, new RnnOutputLayer.Builder(LossFunction.MSE)
                                        .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.dataType(), 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 ConvolutionLayer.Builder().nIn(10).nOut(10).kernelSize(2, 2).stride(2, 2)
                                        .padding(2, 2).build())
                        .layer(1, new DenseLayer.Builder().nIn(10).nOut(10).build())
                        .layer(2, new GravesLSTM.Builder().nIn(10).nOut(10).build())
                        .layer(3, new GravesBidirectionalLSTM.Builder().nIn(10).nOut(10).build())
                        .layer(4, new OutputLayer.Builder(LossFunction.MCXENT).activation(Activation.SOFTMAX).nIn(10).nOut(10).build())
                        .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));
        }
    }
}