Java example source code file (LayerVertex.java)

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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
 */
@Data
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){
        super(graph,name,vertexIndex,inputVertices,outputVertices);
        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)];
    }

    @Override
    public boolean hasLayer(){
        return true;
    }

    @Override
    public boolean isOutputVertex(){
        return layer instanceof BaseOutputLayer;
    }

    @Override
    public Layer getLayer(){
        return layer;
    }

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

        return layer.activate(training);
    }

    @Override
    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++ ){
                epsTotal.addi(epsilons[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());
            pair.setSecond(eps);
        }

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

    @Override
    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());
        }
        layer.setInput(currInput);
    }

    @Override
    public void setBackpropGradientsViewArray(INDArray backpropGradientsViewArray) {
        layer.setBackpropGradientsViewArray(backpropGradientsViewArray);
    }


    @Override
    public String toString(){
        StringBuilder sb = new StringBuilder();
        sb.append("LayerVertex(id=").append(vertexIndex).append(",name=\"").append(vertexName)
                .append("\",inputs=").append(Arrays.toString(inputVertices)).append(",outputs=").append(Arrays.toString(outputVertices))
                .append(")");
        return sb.toString();
    }

}