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Java example source code file (FeedForwardToCnnPreProcessor.java)
The FeedForwardToCnnPreProcessor.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.conf.preprocessor; import com.fasterxml.jackson.annotation.JsonCreator; import com.fasterxml.jackson.annotation.JsonProperty; import lombok.*; import org.deeplearning4j.nn.conf.InputPreProcessor; import org.nd4j.linalg.api.ndarray.INDArray; import org.nd4j.linalg.api.shape.Shape; import org.nd4j.linalg.util.ArrayUtil; import java.util.Arrays; /**A preprocessor to allow CNN and standard feed-forward network layers to be used together.<br> * For example, DenseLayer -> CNN<br> * This does two things:<br> * (a) Reshapes activations out of FeedFoward layer (which is 2D or 3D with shape * [numExamples, inputHeight*inputWidth*numChannels]) into 4d activations (with shape * [numExamples, numChannels, inputHeight, inputWidth]) suitable to feed into CNN layers.<br> * (b) Reshapes 4d epsilons (weights*deltas) from CNN layer, with shape * [numExamples, numChannels, inputHeight, inputWidth]) into 2d epsilons (with shape * [numExamples, inputHeight*inputWidth*numChannels]) for use in feed forward layer * Note: numChannels is equivalent to depth or featureMaps referenced in different literature * @author Adam Gibson * @see CnnToFeedForwardPreProcessor for opposite case (i.e., CNN -> DenseLayer etc) */ @Data public class FeedForwardToCnnPreProcessor implements InputPreProcessor { private int inputHeight; private int inputWidth; private int numChannels; @Getter(AccessLevel.NONE) @Setter(AccessLevel.NONE) private int[] shape; /** * Reshape to a channels x rows x columns tensor * @param inputHeight the columns * @param inputWidth the rows * @param numChannels the channels */ @JsonCreator public FeedForwardToCnnPreProcessor(@JsonProperty("inputHeight") int inputHeight, @JsonProperty("inputWidth") int inputWidth, @JsonProperty("numChannels") int numChannels) { this.inputHeight = inputHeight; this.inputWidth = inputWidth; this.numChannels = numChannels; } public FeedForwardToCnnPreProcessor(int inputWidth, int inputHeight) { this.inputHeight = inputHeight; this.inputWidth = inputWidth; this.numChannels = 1; } @Override public INDArray preProcess(INDArray input, int miniBatchSize) { if(input.ordering() != 'c' || !Shape.strideDescendingCAscendingF(input)) input = input.dup('c'); this.shape = input.shape(); if(input.shape().length == 4) return input; if(input.columns() != inputWidth * inputHeight * numChannels) throw new IllegalArgumentException("Invalid input: expect output columns must be equal to rows " + inputHeight + " x columns " + inputWidth + " x channels " + numChannels + " but was instead " + Arrays.toString(input.shape())); return input.reshape('c',input.size(0),numChannels,inputHeight,inputWidth); } @Override // return 4 dimensions public INDArray backprop(INDArray epsilons, int miniBatchSize){ if(epsilons.ordering() != 'c' || !Shape.strideDescendingCAscendingF(epsilons)) epsilons = epsilons.dup('c'); if(shape == null || ArrayUtil.prod(shape) != epsilons.length()) { if(epsilons.rank() == 2) return epsilons; //should never happen return epsilons.reshape('c',epsilons.size(0), numChannels, inputHeight, inputWidth); } return epsilons.reshape('c',shape); } @Override public FeedForwardToCnnPreProcessor clone() { try { FeedForwardToCnnPreProcessor clone = (FeedForwardToCnnPreProcessor) super.clone(); if(clone.shape != null) clone.shape = clone.shape.clone(); return clone; } catch (CloneNotSupportedException e) { throw new RuntimeException(e); } } } Other Java examples (source code examples)Here is a short list of links related to this Java FeedForwardToCnnPreProcessor.java source code file: |
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