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Java example source code file (LayerVertex.java)

This example Java source code file (LayerVertex.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

allargsconstructor, baserecurrentlayer, convolutionlayer, feedforwardtocnnpreprocessor, got, graphvertex, indarray, invalidinputtypeexception, layervertex, neuralnetconfiguration, noargsconstructor, override, rnntocnnpreprocessor, subsamplinglayer, util

The LayerVertex.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 lombok.AllArgsConstructor;
import lombok.Data;
import lombok.EqualsAndHashCode;
import lombok.NoArgsConstructor;
import org.deeplearning4j.nn.conf.InputPreProcessor;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.deeplearning4j.nn.conf.inputs.InputType;
import org.deeplearning4j.nn.conf.inputs.InvalidInputTypeException;
import org.deeplearning4j.nn.conf.layers.*;
import org.deeplearning4j.nn.conf.preprocessor.FeedForwardToCnnPreProcessor;
import org.deeplearning4j.nn.conf.preprocessor.RnnToCnnPreProcessor;
import org.deeplearning4j.nn.graph.ComputationGraph;
import org.deeplearning4j.nn.layers.convolution.KernelValidationUtil;
import org.deeplearning4j.nn.layers.factory.LayerFactories;
import org.nd4j.linalg.api.ndarray.INDArray;

import java.util.Arrays;

/** * LayerVertex is a GraphVertex with a neural network Layer (and, optionally an {@link InputPreProcessor}) in it
 * @author Alex Black
 */
@AllArgsConstructor
@NoArgsConstructor
@Data  @EqualsAndHashCode(callSuper=false)
public class LayerVertex extends GraphVertex {

    private NeuralNetConfiguration layerConf;
    private InputPreProcessor preProcessor;

    @Override
    public GraphVertex clone() {
        return new LayerVertex(layerConf.clone(), (preProcessor != null ? preProcessor.clone() : null));
    }

    @Override
    public boolean equals(Object o) {
        if (!(o instanceof LayerVertex)) return false;
        LayerVertex lv = (LayerVertex) o;
        if (!layerConf.equals(lv.layerConf)) return false;
        if (preProcessor == null && lv.preProcessor != null || preProcessor != null && lv.preProcessor == null)
            return false;
        return preProcessor == null || preProcessor.equals(lv.preProcessor);
    }

    @Override
    public int hashCode() {
        return layerConf.hashCode() ^ (preProcessor != null ? preProcessor.hashCode() : 0);
    }

    @Override
    public int numParams(boolean backprop){
        return LayerFactories.getFactory(layerConf).initializer().numParams(layerConf,backprop);
    }

    @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.LayerVertex(
                graph, name, idx,
                LayerFactories.getFactory(layerConf).create(layerConf, null, idx, paramsView, initializeParams),
                preProcessor);
    }

    @Override
    public InputType getOutputType(InputType... vertexInputs) throws InvalidInputTypeException {
        if (vertexInputs.length != 1) {
            throw new InvalidInputTypeException("LayerVertex expects exactly one input. Got: " + Arrays.toString(vertexInputs));
        }

        //Assume any necessary preprocessors have already been added
        Layer layer = layerConf.getLayer();
        if (layer instanceof ConvolutionLayer || layer instanceof SubsamplingLayer) {
            InputType.InputTypeConvolutional afterPreProcessor;
            if (preProcessor != null) {
                if (preProcessor instanceof FeedForwardToCnnPreProcessor) {
                    FeedForwardToCnnPreProcessor ffcnn = (FeedForwardToCnnPreProcessor) preProcessor;
                    afterPreProcessor = (InputType.InputTypeConvolutional) InputType.convolutional(ffcnn.getInputHeight(), ffcnn.getInputWidth(), ffcnn.getNumChannels());
                } else if (preProcessor instanceof RnnToCnnPreProcessor) {
                    RnnToCnnPreProcessor rnncnn = (RnnToCnnPreProcessor) preProcessor;
                    afterPreProcessor = (InputType.InputTypeConvolutional) InputType.convolutional(rnncnn.getInputHeight(), rnncnn.getInputWidth(), rnncnn.getNumChannels());
                } else {
                    //Assume no change to type of input...
                    //TODO checks for non convolutional input...
                    afterPreProcessor = (InputType.InputTypeConvolutional) vertexInputs[0];
                }
            } else {
                afterPreProcessor = (InputType.InputTypeConvolutional) vertexInputs[0];
            }

            int channelsOut;
            int[] kernel;
            int[] stride;
            int[] padding;
            if (layer instanceof ConvolutionLayer) {
                channelsOut = ((ConvolutionLayer) layer).getNOut();
                kernel = ((ConvolutionLayer) layer).getKernelSize();
                stride = ((ConvolutionLayer) layer).getStride();
                padding = ((ConvolutionLayer) layer).getPadding();
            } else {
                channelsOut = afterPreProcessor.getDepth();
                kernel = ((SubsamplingLayer) layer).getKernelSize();
                stride = ((SubsamplingLayer) layer).getStride();
                padding = ((SubsamplingLayer) layer).getPadding();
            }

            //First: check that the kernel size/stride/padding is valid
            int inHeight = afterPreProcessor.getHeight();
            int inWidth = afterPreProcessor.getWidth();
            new KernelValidationUtil().validateShapes(inHeight, inWidth,
                    kernel[0], kernel[1], stride[0], stride[1],padding[0], padding[1]);

            int outWidth = (inWidth - kernel[1] + 2 * padding[1]) / stride[1] + 1;
            int outHeight = (inHeight - kernel[0] + 2 * padding[0]) / stride[0] + 1;

            return InputType.convolutional(outHeight,outWidth,channelsOut);
        } else if (layer instanceof BaseRecurrentLayer) {
            return InputType.recurrent(((BaseRecurrentLayer) layer).getNOut());
        } else if (layer instanceof FeedForwardLayer) {
            //Dense, autoencoder, etc
            return InputType.feedForward(((FeedForwardLayer) layer).getNOut());
        } else {
            //Unknown... probably same as input??
            return vertexInputs[0];
        }
    }
}

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