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Java example source code file (ActivationLayer.java)

This example Java source code file (ActivationLayer.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

activationlayer, defaultgradient, gradient, illegalargumentexception, indarray, not, override, pair, type, unsupportedoperationexception

The ActivationLayer.java Java example source code

/*
 *
 *  * Copyright 2015 Skymind,Inc.
 *  *
 *  *    Licensed under the Apache License, Version 2.0 (the "License");
 *  *    you may not use this file except in compliance with the License.
 *  *    You may obtain a copy of the License at
 *  *
 *  *        http://www.apache.org/licenses/LICENSE-2.0
 *  *
 *  *    Unless required by applicable law or agreed to in writing, software
 *  *    distributed under the License is distributed on an "AS IS" BASIS,
 *  *    WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 *  *    See the License for the specific language governing permissions and
 *  *    limitations under the License.
 *
 */

package org.deeplearning4j.nn.layers;


import org.deeplearning4j.berkeley.Pair;
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.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.factory.Nd4j;


/**
 * Activation Layer
 *
 * Used to apply activation on input and corresponding derivative on epsilon.
 * Decouples activation from the layer type and ideal for cases when applying
 * BatchNormLayer. For example, use "identity" activation on the layer prior to BatchNorm and
 * apply this layer after the BatchNorm.
 */
public class ActivationLayer extends BaseLayer<org.deeplearning4j.nn.conf.layers.ActivationLayer> {

    public ActivationLayer(NeuralNetConfiguration conf) {
        super(conf);
    }

    public ActivationLayer(NeuralNetConfiguration conf, INDArray input) {
        super(conf, input);
    }

    @Override
    public double calcL2() {
        return 0;
    }

    @Override
    public double calcL1() {
        return 0;
    }

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

    @Override
    public void fit(INDArray input) {}

    @Override
    public Pair<Gradient, INDArray> backpropGradient(INDArray epsilon) {
        INDArray activationDerivative = Nd4j.getExecutioner().execAndReturn(Nd4j.getOpFactory().createTransform(conf().getLayer().getActivationFunction(), input).derivative());
        INDArray delta = epsilon.muli(activationDerivative);

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

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

    @Override
    public INDArray activate(boolean training) {
        if(input == null)
            throw new IllegalArgumentException("No null input allowed");
        applyDropOutIfNecessary(training);

        return Nd4j.getExecutioner().execAndReturn(Nd4j.getOpFactory().createTransform(conf.getLayer().getActivationFunction(), input));

    }

    @Override
    public Layer transpose(){
        throw new UnsupportedOperationException("Not yet implemented");
    }


    @Override
    public Gradient calcGradient(Gradient layerError, INDArray indArray) {
        throw new UnsupportedOperationException("Not yet implemented");
    }

    @Override
    public void merge(Layer layer, int batchSize) {
        throw new UnsupportedOperationException();
    }

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

}

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