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Java example source code file (CnnToRnnPreProcessor.java)

This example Java source code file (CnnToRnnPreProcessor.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.

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Java - Java tags/keywords

cnntornnpreprocessor, data, illegalargumentexception, indarray, inputpreprocessor, invalid, jsoncreator, jsonproperty, override, util

The CnnToRnnPreProcessor.java Java example source code

package org.deeplearning4j.nn.conf.preprocessor;

import com.fasterxml.jackson.annotation.JsonCreator;
import com.fasterxml.jackson.annotation.JsonProperty;
import lombok.AccessLevel;
import lombok.Data;
import lombok.Getter;
import lombok.Setter;
import org.deeplearning4j.nn.conf.InputPreProcessor;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.util.ArrayUtil;

import java.util.Arrays;

/**A preprocessor to allow CNN and RNN layers to be used together.<br>
 * For example, ConvolutionLayer -> GravesLSTM
 * Functionally equivalent to combining CnnToFeedForwardPreProcessor + FeedForwardToRnnPreProcessor<br>
 * Specifically, this does two things:<br>
 * (a) Reshape 4d activations out of CNN layer, with shape [timeSeriesLength*miniBatchSize, numChannels, inputHeight, inputWidth])
 * into 3d (time series) activations (with shape [numExamples, inputHeight*inputWidth*numChannels, timeSeriesLength])
 * for use in RNN layers<br>
 * (b) Reshapes 3d epsilons (weights.*deltas) out of RNN layer (with shape
 * [miniBatchSize,inputHeight*inputWidth*numChannels,timeSeriesLength]) into 4d epsilons with shape
 * [miniBatchSize*timeSeriesLength, numChannels, inputHeight, inputWidth] suitable to feed into CNN layers.
 * Note: numChannels is equivalent to depth or featureMaps referenced in different literature
 * @author Alex Black
public class CnnToRnnPreProcessor implements InputPreProcessor {
    private int inputHeight;
    private int inputWidth;
    private int numChannels;

    private int product;

    public CnnToRnnPreProcessor(@JsonProperty("inputHeight") int inputHeight,
                                @JsonProperty("inputWidth") int inputWidth,
                                @JsonProperty("numChannels") int numChannels) {
        this.inputHeight = inputHeight;
        this.inputWidth = inputWidth;
        this.numChannels = numChannels;
        this.product = inputHeight*inputWidth*numChannels;

    public INDArray preProcess(INDArray input, int miniBatchSize) {
        if(input.rank() != 4) throw new IllegalArgumentException("Invalid input: expect CNN activations with rank 4 (received input with shape "
            + Arrays.toString(input.shape())+")");
        //Input: 4d activations (CNN)
        //Output: 3d activations (RNN)

        if(input.ordering() != 'c') input = input.dup('c');

        int[] shape = input.shape();    //[timeSeriesLength*miniBatchSize, numChannels, inputHeight, inputWidth]

        //First: reshape 4d to 2d, as per CnnToFeedForwardPreProcessor
        INDArray twod = input.reshape('c',input.size(0), ArrayUtil.prod(input.shape())/input.size(0));
        //Second: reshape 2d to 3d, as per FeedForwardToRnnPreProcessor
        INDArray reshaped = twod.dup('f').reshape('f',miniBatchSize,shape[0]/miniBatchSize,product);
        return reshaped.permute(0,2,1);

    public INDArray backprop(INDArray output, int miniBatchSize) {
        if(output.ordering() == 'c') output = output.dup('f');

        int[] shape = output.shape();
        INDArray output2d;
            //Edge case: miniBatchSize = 1
            output2d = output.tensorAlongDimension(0,1,2).permutei(1,0);
        } else if(shape[2]==1){
            //Edge case: timeSeriesLength = 1
            output2d = output.tensorAlongDimension(0,1,0);
        } else {
            //As per FeedForwardToRnnPreprocessor
            INDArray permuted3d = output.permute(0, 2, 1);
            output2d = permuted3d.reshape('f',shape[0]*shape[2],shape[1]);

        if(shape[1] != product)
            throw new IllegalArgumentException("Invalid input: expected output size(1)="+shape[1]+" must be equal to "
                + inputHeight + " x columns " + inputWidth + " x depth " + numChannels +" = " + product + ", received: " + shape[1]);
        return output2d.dup('c').reshape('c',output2d.size(0), numChannels, inputHeight, inputWidth);

    public CnnToRnnPreProcessor clone() {
        return new CnnToRnnPreProcessor(inputHeight,inputWidth,numChannels);

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