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Java example source code file (ElementWiseVertex.java)

This example Java source code file (ElementWiseVertex.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

add, cnn, data, elementwise, elementwisevertex, graphvertex, indarray, invalidinputtypeexception, override, product, runtimeexception, same, subtract

The ElementWiseVertex.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 com.fasterxml.jackson.annotation.JsonProperty;
import lombok.Data;
import lombok.EqualsAndHashCode;
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;

/** An ElementWiseVertex is used to combine the activations of two or more layer in an element-wise manner<br>
 * For example, the activations may be combined by addition, subtraction or multiplication.
 * Addition may use an arbitrary number of input arrays. Note that in the case of subtraction, only two inputs may be used.
 * @author Alex Black
 */
@Data @EqualsAndHashCode(callSuper=false)
public class ElementWiseVertex extends GraphVertex {

    public ElementWiseVertex(@JsonProperty("op") Op op) {
        this.op = op;
    }

    public enum Op {Add, Subtract, Product};

    protected Op op;

    @Override
    public ElementWiseVertex clone() {
        return new ElementWiseVertex(op);
    }

    @Override
    public boolean equals(Object o){
        if(!(o instanceof ElementWiseVertex)) return false;
        return ((ElementWiseVertex)o).op == op;
    }

    @Override
    public int hashCode(){
        return op.hashCode();
    }

    @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) {
        org.deeplearning4j.nn.graph.vertex.impl.ElementWiseVertex.Op op;
        switch(this.op){
            case Add:
                op = org.deeplearning4j.nn.graph.vertex.impl.ElementWiseVertex.Op.Add;
                break;
            case Subtract:
                op = org.deeplearning4j.nn.graph.vertex.impl.ElementWiseVertex.Op.Subtract;
                break;
            case Product:
                op = org.deeplearning4j.nn.graph.vertex.impl.ElementWiseVertex.Op.Product;
                break;
            default:
                throw new RuntimeException();
        }
        return new org.deeplearning4j.nn.graph.vertex.impl.ElementWiseVertex(graph,name,idx,op);
    }

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

            for( int i=1; i<vertexInputs.length; i++ ){
                if(vertexInputs[i].getType() != InputType.Type.CNN){
                    throw new InvalidInputTypeException("Invalid input: ElementWise vertex 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(fd != od || fw != ow || fh != oh){
                    throw new InvalidInputTypeException("Invalid input: ElementWise vertex cannot process CNN activations of different sizes:"
                            + "first [depth,width,height] = [" + fd + "," + fw + "," + fh + "], input " + i + " = [" + od + "," + ow + "," + oh + "]");
                }
            }
        }
        return first;   //Same output shape/size as
    }
}

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