Java example source code file (ConvolutionParamInitializer.java)

This example Java source code file (ConvolutionParamInitializer.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

bias_key, convolutionparaminitializer, distribution, filter, illegalargumentexception, indarray, linkedhashmap, map, neuralnetconfiguration, override, paraminitializer, string, util, weight_key

The ConvolutionParamInitializer.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.params;


import org.canova.api.conf.Configuration;
import org.deeplearning4j.nn.api.ParamInitializer;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.deeplearning4j.nn.conf.distribution.Distributions;
import org.deeplearning4j.nn.weights.WeightInitUtil;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.api.rng.distribution.Distribution;
import org.nd4j.linalg.factory.Nd4j;
import org.nd4j.linalg.indexing.NDArrayIndex;

import java.util.LinkedHashMap;
import java.util.Map;

/**
 * Initialize convolution params.
 *
 * @author Adam Gibson
 */
public class ConvolutionParamInitializer implements ParamInitializer {

    public final static String WEIGHT_KEY = DefaultParamInitializer.WEIGHT_KEY;
    public final static String BIAS_KEY = DefaultParamInitializer.BIAS_KEY;

    @Override
    public int numParams(NeuralNetConfiguration conf, boolean backprop) {
        org.deeplearning4j.nn.conf.layers.ConvolutionLayer layerConf =
                (org.deeplearning4j.nn.conf.layers.ConvolutionLayer) conf.getLayer();

        int[] kernel = layerConf.getKernelSize();
        int nIn = layerConf.getNIn();
        int nOut = layerConf.getNOut();
        return nIn * nOut * kernel[0] * kernel[1] + nOut;
    }

    @Override
    public void init(Map<String, INDArray> params, NeuralNetConfiguration conf, INDArray paramsView, boolean initializeParams) {
        if (((org.deeplearning4j.nn.conf.layers.ConvolutionLayer) conf.getLayer()).getKernelSize().length != 2)
            throw new IllegalArgumentException("Filter size must be == 2");

        org.deeplearning4j.nn.conf.layers.ConvolutionLayer layerConf =
                (org.deeplearning4j.nn.conf.layers.ConvolutionLayer) conf.getLayer();

        int[] kernel = layerConf.getKernelSize();
        int nIn = layerConf.getNIn();
        int nOut = layerConf.getNOut();

        INDArray biasView = paramsView.get(NDArrayIndex.point(0), NDArrayIndex.interval(0, nOut));
        INDArray weightView = paramsView.get(NDArrayIndex.point(0), NDArrayIndex.interval(nOut, numParams(conf,true)));

        params.put(BIAS_KEY, createBias(conf, biasView, initializeParams));
        params.put(WEIGHT_KEY, createWeightMatrix(conf, weightView, initializeParams));
        conf.addVariable(WEIGHT_KEY);
        conf.addVariable(BIAS_KEY);

    }

    @Override
    public Map<String, INDArray> getGradientsFromFlattened(NeuralNetConfiguration conf, INDArray gradientView) {

        org.deeplearning4j.nn.conf.layers.ConvolutionLayer layerConf =
                (org.deeplearning4j.nn.conf.layers.ConvolutionLayer) conf.getLayer();

        int[] kernel = layerConf.getKernelSize();
        int nIn = layerConf.getNIn();
        int nOut = layerConf.getNOut();

        INDArray biasGradientView = gradientView.get(NDArrayIndex.point(0), NDArrayIndex.interval(0, nOut));
        INDArray weightGradientView = gradientView.get(NDArrayIndex.point(0), NDArrayIndex.interval(nOut, numParams(conf,true)))
                .reshape('c',nOut, nIn, kernel[0], kernel[1]);

        Map<String,INDArray> out = new LinkedHashMap<>();
        out.put(BIAS_KEY, biasGradientView);
        out.put(WEIGHT_KEY, weightGradientView);
        return out;
    }

    //1 bias per feature map
    protected INDArray createBias(NeuralNetConfiguration conf, INDArray biasView, boolean initializeParams) {
        //the bias is a 1D tensor -- one bias per output feature map
        org.deeplearning4j.nn.conf.layers.ConvolutionLayer layerConf =
                (org.deeplearning4j.nn.conf.layers.ConvolutionLayer) conf.getLayer();
        if(initializeParams) biasView.assign(layerConf.getBiasInit());
        return biasView;
    }


    protected INDArray createWeightMatrix(NeuralNetConfiguration conf, INDArray weightView, boolean initializeParams) {
        /*
         Create a 4d weight matrix of:
           (number of kernels, num input channels, kernel height, kernel width)
         Note c order is used specifically for the CNN weights, as opposed to f order elsewhere
         Inputs to the convolution layer are:
         (batch size, num input feature maps, image height, image width)
         */
        org.deeplearning4j.nn.conf.layers.ConvolutionLayer layerConf =
                (org.deeplearning4j.nn.conf.layers.ConvolutionLayer) conf.getLayer();
        if(initializeParams) {
            Distribution dist = Distributions.createDistribution(conf.getLayer().getDist());
            int[] kernel = layerConf.getKernelSize();
            return WeightInitUtil.initWeights(new int[]{layerConf.getNOut(), layerConf.getNIn(), kernel[0], kernel[1]},
                    layerConf.getWeightInit(), dist, 'c', weightView);
        } else {
            int[] kernel = layerConf.getKernelSize();
            return WeightInitUtil.reshapeWeights(new int[]{layerConf.getNOut(), layerConf.getNIn(), kernel[0], kernel[1]}, weightView, 'c');
        }
    }
}