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Java example source code file (FeedForwardToRnnPreProcessor.java)

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Java - Java tags/keywords

clonenotsupportedexception, data, edge, feedforwardtornnpreprocessor, illegalargumentexception, indarray, inputpreprocessor, invalid, noargsconstructor, override, permute, runtimeexception

The FeedForwardToRnnPreProcessor.java Java example source code

package org.deeplearning4j.nn.conf.preprocessor;

import lombok.Data;
import lombok.NoArgsConstructor;

import org.deeplearning4j.nn.conf.InputPreProcessor;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.api.shape.Shape;

/**A preprocessor to allow RNN and feed-forward network layers to be used together.<br>
 * For example, DenseLayer -> GravesLSTM<br>
 * This does two things:<br>
 * (a) Reshapes activations out of FeedFoward layer (which is 2D with shape 
 * [miniBatchSize*timeSeriesLength,layerSize]) into 3d activations (with shape
 * [miniBatchSize,layerSize,timeSeriesLength]) suitable to feed into RNN layers.<br>
 * (b) Reshapes 3d epsilons (weights*deltas from RNN layer, with shape
 * [miniBatchSize,layerSize,timeSeriesLength]) into 2d epsilons (with shape
 * [miniBatchSize*timeSeriesLength,layerSize]) for use in feed forward layer
 * @author Alex Black
 * @see RnnToFeedForwardPreProcessor for opposite case (i.e., GravesLSTM -> DenseLayer etc)
@Data @NoArgsConstructor
public class FeedForwardToRnnPreProcessor implements InputPreProcessor {

	public INDArray preProcess(INDArray input, int miniBatchSize) {
		//Need to reshape FF activations (2d) activations to 3d (for input into RNN layer)
		if( input.rank() != 2 ) throw new IllegalArgumentException("Invalid input: expect NDArray with rank 2 (i.e., activations for FF layer)");
		if(input.ordering() == 'c') input = Shape.toOffsetZeroCopy(input,'f');

		int[] shape = input.shape();
		INDArray reshaped = input.reshape('f',miniBatchSize,shape[0]/miniBatchSize,shape[1]);
		return reshaped.permute(0,2,1);

	public INDArray backprop(INDArray output, int miniBatchSize) {
		//Need to reshape RNN epsilons (3d) to 2d (for use in FF layer backprop calculations)
		if( output.rank() != 3 ) throw new IllegalArgumentException("Invalid input: expect NDArray with rank 3 (i.e., epsilons from RNN layer)");
		if(output.ordering() != 'f') output = output.dup('f');
		int[] shape = output.shape();
		if(shape[0]==1) return output.tensorAlongDimension(0,1,2).permutei(1,0);	//Edge case: miniBatchSize==1
		if(shape[2]==1) return output.tensorAlongDimension(0,1,0);	//Edge case: timeSeriesLength=1
		INDArray permuted = output.permute(0,2,1);	//Permute, so we get correct order after reshaping
		return permuted.reshape('f',shape[0]*shape[2],shape[1]);

	public FeedForwardToRnnPreProcessor clone() {
		try {
			FeedForwardToRnnPreProcessor clone = (FeedForwardToRnnPreProcessor) super.clone();
			return clone;
		} catch (CloneNotSupportedException e) {
			throw new RuntimeException(e);

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