org.deeplearning4j.nn.layers.convolution.ZeroPadding1DLayer Maven / Gradle / Ivy

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package org.deeplearning4j.nn.layers.convolution;

import lombok.val;
import org.deeplearning4j.nn.api.Layer;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.deeplearning4j.nn.gradient.DefaultGradient;
import org.deeplearning4j.nn.gradient.Gradient;
import org.deeplearning4j.nn.layers.AbstractLayer;
import org.nd4j.linalg.api.buffer.DataType;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.indexing.INDArrayIndex;
import org.nd4j.linalg.indexing.NDArrayIndex;
import org.nd4j.common.primitives.Pair;
import org.deeplearning4j.nn.workspace.ArrayType;
import org.deeplearning4j.nn.workspace.LayerWorkspaceMgr;

public class ZeroPadding1DLayer extends AbstractLayer {

    private int[] padding; // [padLeft, padRight]

    public ZeroPadding1DLayer(NeuralNetConfiguration conf, DataType dataType) {
        super(conf, dataType);
        this.padding = ((org.deeplearning4j.nn.conf.layers.ZeroPadding1DLayer) conf.getLayer()).getPadding();
    }

    @Override
    public boolean isPretrainLayer() {
        return false;
    }

    @Override
    public void clearNoiseWeightParams() {
        //No op
    }

    @Override
    public Type type() {
        return Type.RECURRENT;
    }

    @Override
    public Pair backpropGradient(INDArray epsilon, LayerWorkspaceMgr workspaceMgr) {
        assertInputSet(true);
        val inShape = input.shape();

        INDArray epsNext = epsilon.get(NDArrayIndex.all(), NDArrayIndex.all(),
                NDArrayIndex.interval(padding[0], padding[0] + inShape[2]));

        return new Pair<>((Gradient) new DefaultGradient(), workspaceMgr.leverageTo(ArrayType.ACTIVATION_GRAD, epsNext));
    }


    @Override
    public INDArray activate(boolean training, LayerWorkspaceMgr workspaceMgr) {
        assertInputSet(false);
        val inShape = input.shape();
        val paddedOut = inShape[2] + padding[0] + padding[1];
        val outShape = new long[] {inShape[0], inShape[1], paddedOut};

        INDArray out = workspaceMgr.create(ArrayType.ACTIVATIONS, dataType, outShape, 'c');
        out.put(new INDArrayIndex[] {NDArrayIndex.all(), NDArrayIndex.all(),
                NDArrayIndex.interval(padding[0], padding[0] + inShape[2])}, input);

        return out;
    }

    @Override
    public Layer clone() {
        return new ZeroPadding1DLayer(conf.clone(), dataType);
    }

    @Override
    public double calcRegularizationScore(boolean backpropParamsOnly){
        return 0;
    }
}