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Java example source code file (RnnOutputLayer.java)

This example Java source code file (RnnOutputLayer.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

edge, illegalargumentexception, indarray, input, invalid, override, pair, permute, rnnoutputlayer, softmax, type, unsupportedoperationexception

The RnnOutputLayer.java Java example source code

/*
 *
 *  * Copyright 2015 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.layers.recurrent;

import org.deeplearning4j.berkeley.Pair;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.deeplearning4j.nn.gradient.Gradient;
import org.deeplearning4j.nn.layers.BaseOutputLayer;
import org.deeplearning4j.nn.params.DefaultParamInitializer;
import org.deeplearning4j.util.Dropout;
import org.deeplearning4j.util.TimeSeriesUtils;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.api.ops.impl.transforms.SoftMax;
import org.nd4j.linalg.api.shape.Shape;
import org.nd4j.linalg.factory.Nd4j;

/**Recurrent Neural Network Output Layer.<br>
 * Handles calculation of gradients etc for various objective functions.<br>
 * Functionally the same as OutputLayer, but handles output and label reshaping
 * automatically.<br>
 * Input and output activations are same as other RNN layers: 3 dimensions with shape
 * [miniBatchSize,nIn,timeSeriesLength] and [miniBatchSize,nOut,timeSeriesLength] respectively.
 * @author Alex Black
 * @see BaseOutputLayer, OutputLayer
 */
public class RnnOutputLayer extends BaseOutputLayer<org.deeplearning4j.nn.conf.layers.RnnOutputLayer> {

	public RnnOutputLayer(NeuralNetConfiguration conf) {
		super(conf);
	}

	public RnnOutputLayer(NeuralNetConfiguration conf, INDArray input) {
        super(conf, input);
    }
	
	private INDArray reshape3dTo2d(INDArray in){
		if( in.rank() != 3 ) throw new IllegalArgumentException("Invalid input: expect NDArray with rank 3");
		int[] shape = in.shape();
		if(shape[0]==1) return in.tensorAlongDimension(0,1,2).permutei(1,0);	//Edge case: miniBatchSize==1
		if(shape[2]==1) return in.tensorAlongDimension(0,1,0);	//Edge case: timeSeriesLength=1
		INDArray permuted = in.permute(0, 2, 1);	//Permute, so we get correct order after reshaping
        return permuted.reshape('f',shape[0] * shape[2], shape[1]);
	}
	
	private INDArray reshape2dTo3d(INDArray in, int miniBatchSize){
		if( in.rank() != 2 ) throw new IllegalArgumentException("Invalid input: expect NDArray with rank 2");
		//Based on: RnnToFeedForwardPreProcessor
		int[] shape = in.shape();
        if(in.ordering() != 'f') in = Shape.toOffsetZeroCopy(in, 'f');
		INDArray reshaped = in.reshape('f',miniBatchSize, shape[0] / miniBatchSize, shape[1]);
		return reshaped.permute(0, 2, 1);
	}

    @Override
    public Pair<Gradient,INDArray> backpropGradient(INDArray epsilon) {
        if(input.rank() != 3) throw new UnsupportedOperationException("Input is not rank 3");
        INDArray inputTemp = input;
        this.input = reshape3dTo2d(input);
    	Pair<Gradient,INDArray> gradAndEpsilonNext = super.backpropGradient(epsilon);
        this.input = inputTemp;
    	INDArray epsilon2d = gradAndEpsilonNext.getSecond();
    	INDArray epsilon3d = reshape2dTo3d(epsilon2d, input.size(0));
		return new Pair<>(gradAndEpsilonNext.getFirst(),epsilon3d);
    }

    /**{@inheritDoc}
     */
    @Override
    public double f1Score(INDArray examples, INDArray labels) {
        if(examples.rank() == 3) examples = reshape3dTo2d(examples);
        if(labels.rank() == 3) labels = reshape3dTo2d(labels);
        return super.f1Score(examples, labels);
    }
    
    public INDArray getInput() {
        return input;
    }

    @Override
    public Type type() {
        return Type.RECURRENT;
    }
    
    @Override
    public INDArray preOutput(INDArray x, boolean training){
        setInput(x);
        return reshape2dTo3d(preOutput2d(training),input.size(0));
    }

    @Override
    protected INDArray preOutput2d(boolean training){
        if(input.rank() == 3 ) {
            //Case when called from RnnOutputLayer
            INDArray inputTemp = input;
            input = reshape3dTo2d(input);
            INDArray out = super.preOutput(input, training);
            this.input = inputTemp;
            return out;
        } else {
            //Case when called from BaseOutputLayer
            INDArray out = super.preOutput(input, training);
            return out;
        }
    }
    
    @Override
    protected INDArray output2d(INDArray input){
    	return reshape3dTo2d(output(input));
    }
    
    @Override
    protected INDArray getLabels2d(){
    	if(labels.rank()==3) return reshape3dTo2d(labels);
    	return labels;
    }

    @Override
    public INDArray output(INDArray input) {
        if(input.rank() != 3) throw new IllegalArgumentException("Input must be rank 3 (is: " + input.rank());
        //Returns 3d activations from 3d input
        setInput(input);
        return output(false);
    }

    @Override
    public INDArray output(boolean training){
        //Assume that input is 3d
        if(input.rank() != 3 ) throw new IllegalArgumentException("input must be rank 3");
        INDArray preOutput2d = preOutput2d(training);

        if(conf.getLayer().getActivationFunction().equals("softmax")) {
            INDArray out2d = Nd4j.getExecutioner().execAndReturn(new SoftMax(preOutput2d));
            if(maskArray != null){
                out2d.muliColumnVector(maskArray);
            }
            return reshape2dTo3d(out2d,input.size(0));
        }

        if(training)
            applyDropOutIfNecessary(training);
        INDArray origInput = input;
        this.input = reshape3dTo2d(input);
        INDArray out = super.activate(true);
        this.input = origInput;
        if(maskArray != null){
            out.muliColumnVector(maskArray);
        }
        return reshape2dTo3d(out,input.size(0));
    }

    @Override
    public INDArray activate(boolean training) {
        if(input.rank() != 3) throw new UnsupportedOperationException("Input must be rank 3");
        INDArray b = getParam(DefaultParamInitializer.BIAS_KEY);
        INDArray W = getParam(DefaultParamInitializer.WEIGHT_KEY);
        if(conf.isUseDropConnect() && training) {
            W = Dropout.applyDropConnect(this, DefaultParamInitializer.WEIGHT_KEY);
        }

        INDArray input2d = reshape3dTo2d(input);

        INDArray act2d = Nd4j.getExecutioner().execAndReturn(Nd4j.getOpFactory().createTransform(conf.getLayer().getActivationFunction(),
                input2d.mmul(W).addiRowVector(b)));
        if(maskArray != null){
            act2d.muliColumnVector(maskArray);
        }
        return reshape2dTo3d(act2d, input.size(0));
    }

    @Override
    public void setMaskArray(INDArray maskArray) {
        if(maskArray != null && maskArray.size(1) != 1){
            maskArray = TimeSeriesUtils.reshapeTimeSeriesMaskToVector(maskArray);
        }
        this.maskArray = maskArray;
    }
}

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