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Java example source code file (BatchNormalizationParamInitializer.java)

This example Java source code file (BatchNormalizationParamInitializer.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

batchnormalization, batchnormalizationparaminitializer, beta, gamma, indarray, linkedhashmap, map, neuralnetconfiguration, override, paraminitializer, string, util

The BatchNormalizationParamInitializer.java Java example source code

package org.deeplearning4j.nn.params;

import org.deeplearning4j.nn.api.ParamInitializer;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.deeplearning4j.nn.conf.layers.BatchNormalization;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.indexing.NDArrayIndex;

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

 * Batch normalization variable init

public class BatchNormalizationParamInitializer implements ParamInitializer {
    public final static String GAMMA = "gamma";
    public final static String BETA = "beta";

    public int numParams(NeuralNetConfiguration conf, boolean backprop){
        BatchNormalization layer = (BatchNormalization) conf.getLayer();
        return 2*layer.getNOut();

    public void init(Map<String, INDArray> params, NeuralNetConfiguration conf, INDArray paramView, boolean initializeParams) {
        // gamma & beta per activation for DNN and per per feature matrix for CNN layers
        // TODO setup for CNN & RNN
        BatchNormalization layer = (BatchNormalization) conf.getLayer();
        int nOut = layer.getNOut();

        INDArray gammaView = paramView.get(NDArrayIndex.point(0), NDArrayIndex.interval(0,nOut));
        INDArray betaView = paramView.get(NDArrayIndex.point(0), NDArrayIndex.interval(nOut,2*nOut));

        params.put(GAMMA,createGamma(conf, gammaView, initializeParams));
        params.put(BETA, createBeta(conf, betaView, initializeParams));

    public Map<String, INDArray> getGradientsFromFlattened(NeuralNetConfiguration conf, INDArray gradientView) {
        BatchNormalization layer = (BatchNormalization) conf.getLayer();
        int nOut = layer.getNOut();

        INDArray gammaView = gradientView.get(NDArrayIndex.point(0), NDArrayIndex.interval(0,nOut));
        INDArray betaView = gradientView.get(NDArrayIndex.point(0), NDArrayIndex.interval(nOut,2*nOut));

        Map<String,INDArray> out = new LinkedHashMap<>();
        out.put(GAMMA, gammaView);
        out.put(BETA, betaView);

        return out;

    protected INDArray createBeta(NeuralNetConfiguration conf, INDArray betaView, boolean initializeParams) {
        BatchNormalization layer = (BatchNormalization) conf.getLayer();
        if(initializeParams) betaView.assign(layer.getBeta());
        return betaView;

    protected INDArray createGamma(NeuralNetConfiguration conf, INDArray gammaView, boolean initializeParams) {
        BatchNormalization layer = (BatchNormalization) conf.getLayer();
        if(initializeParams) gammaView.assign(layer.getGamma());
        return gammaView;

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