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Java example source code file (TestSparkLayer.java)
The TestSparkLayer.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.JavaRDD; import org.deeplearning4j.datasets.iterator.impl.IrisDataSetIterator; import org.deeplearning4j.eval.Evaluation; import org.deeplearning4j.nn.api.OptimizationAlgorithm; import org.deeplearning4j.nn.conf.NeuralNetConfiguration; import org.deeplearning4j.nn.layers.OutputLayer; import org.deeplearning4j.nn.weights.WeightInit; import org.deeplearning4j.spark.BaseSparkTest; import org.junit.Test; import org.nd4j.linalg.dataset.DataSet; import org.nd4j.linalg.lossfunctions.LossFunctions; import java.util.List; /** * Created by agibsonccc on 1/18/15. */ public class TestSparkLayer extends BaseSparkTest { @Test public void testIris2() throws Exception { NeuralNetConfiguration conf = new NeuralNetConfiguration.Builder() .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT) .iterations(10) .learningRate(1e-1) .layer(new org.deeplearning4j.nn.conf.layers.OutputLayer.Builder(LossFunctions.LossFunction.MCXENT) .nIn(4).nOut(3) .weightInit(WeightInit.XAVIER) .activation("softmax").build()) .build(); System.out.println("Initializing network"); SparkDl4jLayer master = new SparkDl4jLayer(sc,conf); DataSet d = new IrisDataSetIterator(150,150).next(); d.normalizeZeroMeanZeroUnitVariance(); d.shuffle(); List<DataSet> next = d.asList(); JavaRDD<DataSet> data = sc.parallelize(next); OutputLayer network2 =(OutputLayer) master.fitDataSet(data); Evaluation evaluation = new Evaluation(); evaluation.eval(d.getLabels(), network2.output(d.getFeatureMatrix())); System.out.println(evaluation.stats()); } } Other Java examples (source code examples)Here is a short list of links related to this Java TestSparkLayer.java source code file: |
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