cavis/cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/nn/weights/WeightInitIdentityTest.java

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 *  * terms of the Apache License, Version 2.0 which is available at
 *  * https://www.apache.org/licenses/LICENSE-2.0.
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package org.deeplearning4j.nn.weights;

import org.deeplearning4j.BaseDL4JTest;
import org.deeplearning4j.nn.conf.ConvolutionMode;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.deeplearning4j.nn.conf.RNNFormat;
import org.deeplearning4j.nn.conf.inputs.InputType;
import org.deeplearning4j.nn.conf.layers.*;
import org.deeplearning4j.nn.graph.ComputationGraph;

import org.junit.jupiter.api.Test;
import org.nd4j.linalg.activations.impl.ActivationIdentity;
import org.nd4j.linalg.api.buffer.DataType;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.factory.Nd4j;

import static org.junit.jupiter.api.Assertions.assertEquals;

public class WeightInitIdentityTest extends BaseDL4JTest {

    /**
     * Test identity mapping for 1d convolution
     */
    @Test
    //@Ignore("Ignore for now. Underlying logic changed. Gradient checker passes so implementatin is valid.")
    public void testIdConv1D() {
        final INDArray input = Nd4j.randn(DataType.FLOAT, 1,5,7);
        final String inputName = "input";
        final String conv = "conv";
        final String output = "output";
        final ComputationGraph graph = new ComputationGraph(new NeuralNetConfiguration.Builder()
                .graphBuilder()
                .addInputs(inputName)
                .setOutputs(output)
                .layer(conv, new Convolution1DLayer.Builder(7)
                        .convolutionMode(ConvolutionMode.Same)
                        .nOut(input.size(1))
                        .weightInit(new WeightInitIdentity())
                        .activation(new ActivationIdentity())
                        .build(), inputName)
                .layer(output, new RnnLossLayer.Builder().activation(new ActivationIdentity()).build(), conv)
                .setInputTypes(InputType.recurrent(5,7,RNNFormat.NCW))
                .build());
        graph.init();

        INDArray reshape = graph.outputSingle(input).reshape(input.shape());
        assertEquals( input, reshape, "Mapping was not identity!");
    }

    /**
     * Test identity mapping for 2d convolution
     */
    @Test
    public void testIdConv2D() {
        final INDArray input = Nd4j.randn(DataType.FLOAT,1,5,7,11);
        final String inputName = "input";
        final String conv = "conv";
        final String output = "output";
        final ComputationGraph graph = new ComputationGraph(new NeuralNetConfiguration.Builder()
                .graphBuilder()
                .setInputTypes(InputType.inferInputType(input))
                .addInputs(inputName)
                .setOutputs(output)
                .layer(conv, new ConvolutionLayer.Builder(3,5)
                        .convolutionMode(ConvolutionMode.Same)
                        .nOut(input.size(1))
                        .weightInit(new WeightInitIdentity())
                        .activation(new ActivationIdentity())
                        .build(), inputName)
                .layer(output, new CnnLossLayer.Builder().activation(new ActivationIdentity()).build(), conv)
                .build());
        graph.init();

        assertEquals( input, graph.outputSingle(input), "Mapping was not identity!");
    }

    /**
     * Test identity mapping for 3d convolution
     */
    @Test
    public void testIdConv3D() {
        final INDArray input = Nd4j.randn(DataType.FLOAT, 1,5,7,11,13);
        final String inputName = "input";
        final String conv = "conv";
        final String output = "output";
        final ComputationGraph graph = new ComputationGraph(new NeuralNetConfiguration.Builder()
                .graphBuilder()
                .setInputTypes(InputType.inferInputType(input))
                .addInputs(inputName)
                .setOutputs(output)
                .layer(conv, new Convolution3D.Builder(3,7,5)
                        .convolutionMode(ConvolutionMode.Same)
                        .dataFormat(Convolution3D.DataFormat.NCDHW)
                        .nOut(input.size(1))
                        .weightInit(new WeightInitIdentity())
                        .activation(new ActivationIdentity())
                        .build(), inputName)
                .layer(output, new Cnn3DLossLayer.Builder(Convolution3D.DataFormat.NCDHW).activation(new ActivationIdentity()).build(), conv)
                .build());
        graph.init();

        assertEquals( input, graph.outputSingle(input), "Mapping was not identity!");
    }
}