org.deeplearning4j.nn.layers.DropoutLayer Maven / Gradle / Ivy

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 *  * https://www.apache.org/licenses/LICENSE-2.0.
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 *  *  information regarding copyright ownership.
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package org.deeplearning4j.nn.layers;

import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.deeplearning4j.nn.gradient.DefaultGradient;
import org.deeplearning4j.nn.gradient.Gradient;
import org.nd4j.linalg.api.buffer.DataType;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.common.primitives.Pair;
import org.deeplearning4j.nn.workspace.ArrayType;
import org.deeplearning4j.nn.workspace.LayerWorkspaceMgr;

public class DropoutLayer extends BaseLayer {

    public DropoutLayer(NeuralNetConfiguration conf, DataType dataType) {
        super(conf, dataType);
    }

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

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

    @Override
    public void fit(INDArray input, LayerWorkspaceMgr workspaceMgr) {
        throw new UnsupportedOperationException("Not supported");
    }

    @Override
    public Pair backpropGradient(INDArray epsilon, LayerWorkspaceMgr workspaceMgr) {
        INDArray delta = workspaceMgr.dup(ArrayType.ACTIVATION_GRAD, epsilon);

        if (maskArray != null) {
            delta.muliColumnVector(maskArray);
        }

        Gradient ret = new DefaultGradient();
        delta = backpropDropOutIfPresent(delta);
        return new Pair<>(ret, delta);
    }

    @Override
    public INDArray activate(boolean training, LayerWorkspaceMgr workspaceMgr) {
        assertInputSet(false);

        INDArray ret;
        if(!training){
            ret = input;
        } else {
            if(layerConf().getIDropout() != null){
                INDArray result;
                if(inputModificationAllowed) {
                    result = input;
                } else {
                    result = workspaceMgr.createUninitialized(ArrayType.INPUT, input.dataType(), input.shape(), input.ordering());
                }

                ret = layerConf().getIDropout().applyDropout(input, result, getIterationCount(), getEpochCount(), workspaceMgr);
            } else {
                ret = workspaceMgr.leverageTo(ArrayType.ACTIVATIONS, input);
            }
        }

        if (maskArray != null) {
            ret.muliColumnVector(maskArray);
        }

        ret = workspaceMgr.leverageTo(ArrayType.ACTIVATIONS, ret);
        return ret;
    }

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

    @Override
    public INDArray params() {
        return null;
    }
}