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Java example source code file (LayerVertex.java)

This example Java source code file (LayerVertex.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

baserecurrentlayer, cannot, data, illegalstateexception, indarray, inputpreprocessor, invalid, layer, layervertex, override, pair, string, stringbuilder, util, vertexindices

The LayerVertex.java Java example source code

 *  * Copyright 2016 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.graph.vertex.impl;

import lombok.Data;
import org.deeplearning4j.berkeley.Pair;
import org.deeplearning4j.nn.api.Layer;
import org.deeplearning4j.nn.conf.InputPreProcessor;
import org.deeplearning4j.nn.gradient.Gradient;
import org.deeplearning4j.nn.graph.ComputationGraph;
import org.deeplearning4j.nn.graph.vertex.BaseGraphVertex;
import org.deeplearning4j.nn.graph.vertex.VertexIndices;
import org.deeplearning4j.nn.layers.BaseOutputLayer;
import org.deeplearning4j.nn.layers.recurrent.BaseRecurrentLayer;
import org.nd4j.linalg.api.ndarray.INDArray;

import java.util.Arrays;

/** * LayerVertex is a GraphVertex with a neural network Layer (and, optionally an {@link InputPreProcessor}) in it
 * @author Alex Black
public class LayerVertex extends BaseGraphVertex {

    private Layer layer;
    private InputPreProcessor layerPreProcessor;

    /** Create a network input vertex: */
    public LayerVertex(ComputationGraph graph, String name, int vertexIndex, Layer layer, InputPreProcessor layerPreProcessor){
        this(graph, name, vertexIndex, null, null, layer, layerPreProcessor);

    public LayerVertex(ComputationGraph graph, String name, int vertexIndex, VertexIndices[] inputVertices, VertexIndices[] outputVertices,
                        Layer layer, InputPreProcessor layerPreProcessor){
        this.graph = graph;
        this.vertexName = name;
        this.vertexIndex = vertexIndex;
        this.inputVertices = inputVertices;
        this.outputVertices = outputVertices;
        this.layer = layer;
        this.layerPreProcessor = layerPreProcessor;

        this.inputs = new INDArray[(inputVertices != null ? inputVertices.length : 0)];
        this.epsilons = new INDArray[(outputVertices != null ? outputVertices.length : 0)];

    public boolean hasLayer(){
        return true;

    public boolean isOutputVertex(){
        return layer instanceof BaseOutputLayer;

    public Layer getLayer(){
        return layer;

    public INDArray doForward(boolean training){
        if(!canDoForward()) throw new IllegalStateException("Cannot do forward pass: all inputs not set");

        return layer.activate(training);

    public Pair<Gradient,INDArray[]> doBackward(boolean tbptt){
        if(!canDoBackward()) throw new IllegalStateException("Cannot do backward pass: all epsilons not set");

        INDArray epsTotal = null;
        if(epsilons != null && epsilons.length == 1 ) epsTotal = epsilons[0];
        else if(epsilons != null && epsilons.length > 1 ){
            //TODO: check the math on this... I think it's correct though
            //This is the "output connected to multiple other layers" case
            epsTotal = epsilons[0].dup();
            for( int i=1; i<epsilons.length; i++ ){

        Pair<Gradient,INDArray> pair;
        if(tbptt && layer instanceof BaseRecurrentLayer<?>){
            //Truncated BPTT for recurrent layers
            pair = ((BaseRecurrentLayer<?>)layer).tbpttBackpropGradient(epsTotal, graph.getConfiguration().getTbpttBackLength());
        } else {
            //Normal backprop
            pair = layer.backpropGradient(epsTotal);    //epsTotal may be null for OutputLayers

        if(layerPreProcessor != null){
            INDArray eps = pair.getSecond();
            eps = layerPreProcessor.backprop(eps,graph.batchSize());

        //Layers always have single activations input -> always have single epsilon output during backprop
        return new Pair<>(pair.getFirst(), new INDArray[]{pair.getSecond()});

    public void setInput(int inputNumber, INDArray input){
        if(inputNumber > 0) throw new IllegalArgumentException("Invalid input number: LayerVertex instances have only ");
        inputs[inputNumber] = input;

        INDArray currInput = inputs[0];
        if(layerPreProcessor != null){
            currInput = layerPreProcessor.preProcess(currInput, graph.batchSize());

    public void setBackpropGradientsViewArray(INDArray backpropGradientsViewArray) {

    public String toString(){
        StringBuilder sb = new StringBuilder();
        return sb.toString();


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