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Java example source code file (SubsetVertex.java)

This example Java source code file (SubsetVertex.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

cannot, cnn, data, graphvertex, indarray, invalidinputtypeexception, jsonproperty, override, received, rnn, runtimeexception, string, subsetvertex, util

The SubsetVertex.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;

import java.util.Arrays;

/** SubsetVertex is used to select a subset of the activations out of another GraphVertex.<br>
 * For example, a subset of the activations out of a layer.<br>
 * Note that this subset is specifying by means of an interval of the original activations.
 * For example, to get the first 10 activations of a layer (or, first 10 features out of a CNN layer) use
 * new SubsetVertex(0,9).<br>
 * In the case of convolutional (4d) activations, this is done along depth.
 * @author Alex Black
 */
@Data @EqualsAndHashCode(callSuper=false)
public class SubsetVertex extends GraphVertex {

    private int from;
    private int to;

    /**
     * @param from The first column index, inclusive
     * @param to The last column index, inclusive
     */
    public SubsetVertex(@JsonProperty("from") int from, @JsonProperty("to") int to) {
        this.from = from;
        this.to = to;
    }

    @Override
    public SubsetVertex clone() {
        return new SubsetVertex(from,to);
    }

    @Override
    public boolean equals(Object o){
        if(!(o instanceof SubsetVertex)) return false;
        SubsetVertex s = (SubsetVertex)o;
        return s.from == from && s.to == to;
    }

    @Override
    public int hashCode(){
        return new Integer(from).hashCode() ^ new Integer(to).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) {
        return new org.deeplearning4j.nn.graph.vertex.impl.SubsetVertex(graph,name,idx,from,to);
    }

    @Override
    public InputType getOutputType(InputType... vertexInputs) throws InvalidInputTypeException {
        if(vertexInputs.length != 1){
            throw new InvalidInputTypeException("SubsetVertex expects single input type. Received: " + Arrays.toString(vertexInputs));
        }

        switch(vertexInputs[0].getType()){
            case FF:
                return InputType.feedForward(to-from+1);
            case RNN:
                return InputType.recurrent(to-from+1);
            case CNN:
                InputType.InputTypeConvolutional conv = (InputType.InputTypeConvolutional)vertexInputs[0];
                int depth = conv.getDepth();
                if(to >= depth){
                    throw new InvalidInputTypeException("Invalid range: Cannot select depth subset [" + from + "," + to + "] inclusive from CNN activations with "
                            + " [depth,width,height] = [" + depth + "," + conv.getWidth() + "," + conv.getHeight() + "]" );
                }
                return InputType.convolutional(from-to+1,conv.getWidth(),conv.getHeight());
            default:
                throw new RuntimeException("Unknown input type: " + vertexInputs[0]);
        }
    }
}

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