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Java example source code file (IterativeReduceFlatMap.java)

This example Java source code file (IterativeReduceFlatMap.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, broadcast, exception, flatmapfunction, illegalstateexception, indarray, iterativereduceflatmap, layerfactory, logger, network, neuralnetconfiguration, outputlayer, override, training, util

The IterativeReduceFlatMap.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.spark.impl.layer;

import org.apache.spark.api.java.function.FlatMapFunction;
import org.apache.spark.broadcast.Broadcast;
import org.deeplearning4j.nn.api.Layer;
import org.deeplearning4j.nn.api.LayerFactory;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.deeplearning4j.nn.layers.OutputLayer;
import org.deeplearning4j.nn.layers.factory.LayerFactories;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.dataset.DataSet;
import org.nd4j.linalg.factory.Nd4j;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;

import java.util.ArrayList;
import java.util.Collections;
import java.util.Iterator;
import java.util.List;

/**
 * Iterative reduce with
 * flat map using map partitions
 *
 * @author Adam Gibson
 */
public class IterativeReduceFlatMap implements FlatMapFunction<Iterator {

    private String json;
    private Broadcast<INDArray> params;
    private static Logger log = LoggerFactory.getLogger(IterativeReduceFlatMap.class);

    /**
     * Pass in json configuration and baseline parameters
     * @param json json configuration for the network
     * @param params the parameters to use for the network
     */
    public IterativeReduceFlatMap(String json, Broadcast<INDArray> params) {
        this.json = json;
        this.params = params;
    }



    @Override
    public Iterable<INDArray> call(Iterator dataSetIterator) throws Exception {
        if(!dataSetIterator.hasNext()) {
            return Collections.singletonList(Nd4j.zeros(params.value().shape()));
        }

        List<DataSet> collect = new ArrayList<>();
        while(dataSetIterator.hasNext()) {
            collect.add(dataSetIterator.next());
        }

        DataSet data = DataSet.merge(collect,false);
        log.debug("Training on " + data.labelCounts());
        NeuralNetConfiguration conf = NeuralNetConfiguration.fromJson(json);
        LayerFactory layerFactory = LayerFactories.getFactory(conf.getLayer());
        int numParams = layerFactory.initializer().numParams(conf,true);
        INDArray thisParams = Nd4j.create(1, numParams);
        Layer network = layerFactory.create(conf, null, 0, thisParams, true);
        network.setBackpropGradientsViewArray(Nd4j.create(1,numParams));
        INDArray val = params.value().dup();
        if(val.length() != network.numParams())
            throw new IllegalStateException("Network did not have same number of parameters as the broadcasted set parameters");
        network.setParams(val);
       if(network instanceof OutputLayer) {
           OutputLayer o = (OutputLayer) network;
           o.fit(data);
       }
        else
            network.fit(data.getFeatureMatrix());

        return Collections.singletonList(network.params());

    }
}

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