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Java example source code file (MergeVertex.java)

This example Java source code file (MergeVertex.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

cnn, graphvertex, indarray, invalid, invalidinputtypeexception, mergevertex, override, size, string

The MergeVertex.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.conf.graph;


import org.deeplearning4j.nn.conf.inputs.InputType;
import org.deeplearning4j.nn.conf.inputs.InvalidInputTypeException;
import org.deeplearning4j.nn.graph.ComputationGraph;
import org.nd4j.linalg.api.ndarray.INDArray;

/** A MergeVertex is used to combine the activations of two or more layers/GraphVertex by means of concatenation/merging.<br>
 * Exactly how this is done depends on the type of input.<br>
 * For 2d (feed forward layer) inputs: MergeVertex([numExamples,layerSize1],[numExamples,layerSize2]) -> [numExamples,layerSize1 + layerSize2]<br>
 * For 3d (time series) inputs: MergeVertex([numExamples,layerSize1,timeSeriesLength],[numExamples,layerSize2,timeSeriesLength])
 *      -> [numExamples,layerSize1 + layerSize2,timeSeriesLength]<br>
 * For 4d (convolutional) inputs: MergeVertex([numExamples,depth1,width,height],[numExamples,depth2,width,height])
 *      -> [numExamples,depth1 + depth2,width,height]<br>
 * @author Alex Black
 */
public class MergeVertex extends GraphVertex {

    @Override
    public MergeVertex clone() {
        return new MergeVertex();
    }

    @Override
    public boolean equals(Object o) {
        return o instanceof MergeVertex;
    }

    @Override
    public int hashCode(){
        return 433682566;
    }

    @Override
    public int numParams(boolean backprop){
        return 0;
    }

    @Override
    public org.deeplearning4j.nn.graph.vertex.GraphVertex instantiate(ComputationGraph graph, String name, int idx,
                                                                      INDArray paramsView, boolean initializeParams) {
        return new org.deeplearning4j.nn.graph.vertex.impl.MergeVertex(graph,name,idx);
    }

    @Override
    public InputType getOutputType(InputType... vertexInputs) throws InvalidInputTypeException {
        if(vertexInputs.length == 1) return vertexInputs[0];
        InputType first = vertexInputs[0];
        if(first.getType() != InputType.Type.CNN){
            //FF or RNN data inputs
            int size = 0;
            boolean ff = true;
            for( int i=0; i<vertexInputs.length; i++ ){
                if(vertexInputs[i].getType() != first.getType()){
                    throw new InvalidInputTypeException("Invalid input: MergeVertex cannot merge activations of different types:"
                            + " first type = " + first.getType() + ", input type " + (i+1) + " = " + vertexInputs[i].getType());
                }

                int thisSize;
                if(vertexInputs[i] instanceof InputType.InputTypeFeedForward) thisSize = ((InputType.InputTypeFeedForward)vertexInputs[i]).getSize();
                else{
                    thisSize = ((InputType.InputTypeRecurrent)vertexInputs[i]).getSize();
                    ff = false;
                }
                if(thisSize <= 0){//Size is not defined
                    size = -1;
                } else {
                    size += thisSize;
                }
            }

            if(size > 0){
                //Size is specified
                if(ff) return InputType.feedForward(size);
                else return InputType.recurrent(size);
            } else {
                //size is unknown
                if(ff) return InputType.feedForward(-1);
                else return InputType.recurrent(-1);
            }
        } else {
            //CNN inputs... also check that the depth, width and heights match:
            InputType.InputTypeConvolutional firstConv = (InputType.InputTypeConvolutional)first;
            int fd = firstConv.getDepth();
            int fw = firstConv.getWidth();
            int fh = firstConv.getHeight();

            int depthSum = fd;

            for( int i=1; i<vertexInputs.length; i++ ){
                if(vertexInputs[i].getType() != InputType.Type.CNN){
                    throw new InvalidInputTypeException("Invalid input: MergeVertex cannot process activations of different types:"
                            + " first type = " + InputType.Type.CNN + ", input type " + (i+1) + " = " + vertexInputs[i].getType());
                }

                InputType.InputTypeConvolutional otherConv = (InputType.InputTypeConvolutional) vertexInputs[i];

                int od = otherConv.getDepth();
                int ow = otherConv.getWidth();
                int oh = otherConv.getHeight();

                if(fw != ow || fh != oh){
                    throw new InvalidInputTypeException("Invalid input: MergeVertex cannot merge CNN activations of different width/heights:"
                            + "first [depth,width,height] = [" + fd + "," + fw + "," + fh + "], input " + i + " = [" + od + "," + ow + "," + oh + "]");
                }

                depthSum += od;
            }

            return InputType.convolutional(fh,fw,depthSum);
        }
    }
}

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