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Java example source code file (BasePretrainNetwork.java)

This example Java source code file (BasePretrainNetwork.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

arraylist, baselayer, basepretrainnetwork, defaultgradient, gradient, indarray, list, override, pair, parameter, set, unable, use, util

The BasePretrainNetwork.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.layers;


import org.deeplearning4j.berkeley.Pair;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.deeplearning4j.nn.gradient.DefaultGradient;
import org.deeplearning4j.nn.gradient.Gradient;
import org.deeplearning4j.nn.params.PretrainParamInitializer;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.api.ops.LossFunction;
import org.nd4j.linalg.factory.Nd4j;
import org.nd4j.linalg.indexing.NDArrayIndex;
import org.nd4j.linalg.lossfunctions.LossCalculation;
import org.nd4j.linalg.lossfunctions.LossFunctions;

import java.util.ArrayList;
import java.util.List;
import java.util.Map;
import java.util.Set;


/**
 * Baseline class for any Neural Network used
 * as a layer in a deep network *
 * @author Adam Gibson
 *
 */
public abstract class BasePretrainNetwork<LayerConfT extends org.deeplearning4j.nn.conf.layers.BasePretrainNetwork>
        extends BaseLayer<LayerConfT> {

    public BasePretrainNetwork(NeuralNetConfiguration conf) {
        super(conf);
    }

    public BasePretrainNetwork(NeuralNetConfiguration conf, INDArray input) {
        super(conf, input);
    }


    /**
     * Corrupts the given input by doing a binomial sampling
     * given the corruption level
     * @param x the input to corrupt
     * @param corruptionLevel the corruption value
     * @return the binomial sampled corrupted input
     */
    public INDArray getCorruptedInput(INDArray x, double corruptionLevel) {
        INDArray corrupted = Nd4j.getDistributions().createBinomial(1,1 - corruptionLevel).sample(x.shape());
        corrupted.muli(x);
        return corrupted;
    }


    protected Gradient createGradient(INDArray wGradient,INDArray vBiasGradient,INDArray hBiasGradient) {
        Gradient ret = new DefaultGradient();
        ret.gradientForVariable().put(PretrainParamInitializer.VISIBLE_BIAS_KEY,vBiasGradient);
        ret.gradientForVariable().put(PretrainParamInitializer.BIAS_KEY,hBiasGradient);
        ret.gradientForVariable().put(PretrainParamInitializer.WEIGHT_KEY, wGradient);
        return ret;
    }

    @Override
    public int numParams(boolean backwards) {
        if(!backwards)
            return super.numParams(backwards);
        int ret = 0;
        for(String s : paramTable().keySet()) {
            if(!s.equals(PretrainParamInitializer.VISIBLE_BIAS_KEY)) {
                ret += getParam(s).length();
            }
        }

        return ret;
    }

    /**
     * Sample the hidden distribution given the visible
     * @param v the visible to sample from
     * @return the hidden mean and sample
     */
    public abstract Pair<INDArray,INDArray> sampleHiddenGivenVisible(INDArray v);

    /**
     * Sample the visible distribution given the hidden
     * @param h the hidden to sample from
     * @return the mean and sample
     */
    public abstract Pair<INDArray,INDArray> sampleVisibleGivenHidden(INDArray h);

    @Override
    protected void setScoreWithZ(INDArray z) {
        if (layerConf().getLossFunction() == LossFunctions.LossFunction.CUSTOM) {
            LossFunction create = Nd4j.getOpFactory().createLossFunction(layerConf().getCustomLossFunction(), input, z);
            create.exec();
            score = create.getFinalResult().doubleValue();
        }

        else {
            score = LossCalculation.builder()
                    .l1(calcL1()).l2(calcL2())
                    .labels(input).z(z).lossFunction(layerConf().getLossFunction())
                    .miniBatch(conf.isMiniBatch()).miniBatchSize(input.size(0))
                    .useRegularization(conf.isUseRegularization()).build().score();
        }
    }

    public INDArray paramsBackprop(){
        List<INDArray> list = new ArrayList<>(2);
        for(Map.Entry<String,INDArray> entry : params.entrySet()){
            if(!PretrainParamInitializer.VISIBLE_BIAS_KEY.equals(entry.getKey())) list.add(entry.getValue());
        }
        return Nd4j.toFlattened('f', list);
    }

    /**The number of parameters for the model, for backprop (i.e., excluding visible bias)
     * @return the number of parameters for the model (ex. visible bias)
     */
    public int numParamsBackprop() {
        int ret = 0;
        for(Map.Entry<String,INDArray> entry : params.entrySet()){
            if(PretrainParamInitializer.VISIBLE_BIAS_KEY.equals(entry.getKey())) continue;
            ret += entry.getValue().length();
        }
        return ret;
    }

    @Override
    public void setParams(INDArray params) {
        if(params == paramsFlattened) return;   //No op

        //SetParams has two different uses: during pretrain vs. backprop.
        //pretrain = 3 sets of params (inc. visible bias); backprop = 2

        List<String> parameterList = conf.variables();
        int lengthPretrain = 0;
        int lengthBackprop = 0;
        for(String s : parameterList) {
            int len = getParam(s).length();
            lengthPretrain += len;
            if(!PretrainParamInitializer.VISIBLE_BIAS_KEY.equals(s)) lengthBackprop += len;
        }

        boolean pretrain = params.length() == lengthPretrain;
        if( !pretrain && params.length() != lengthBackprop ) {
            throw new IllegalArgumentException("Unable to set parameters: must be of length " + lengthPretrain + " for pretrain, "
                + " or " + lengthBackprop + " for backprop. Is: " + params.length());
        }

        if(!pretrain){
            paramsFlattened.assign(params);
            return;
        }

        int idx = 0;
        Set<String> paramKeySet = this.params.keySet();
        for(String s : paramKeySet) {
            INDArray param = getParam(s);
            INDArray get = params.get(NDArrayIndex.point(0),NDArrayIndex.interval(idx, idx + param.length()));
            if(param.length() != get.length())
                throw new IllegalStateException("Parameter " + s + " should have been of length " + param.length() + " but was " + get.length());
            param.assign(get.reshape('f',param.shape()));  //Use assign due to backprop params being a view of a larger array
            idx += param.length();

        }

    }

}

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