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package org.deeplearning4j.instrumentation.gradient;

import org.deeplearning4j.datasets.iterator.DataSetIterator;
import org.deeplearning4j.datasets.iterator.impl.RawMnistDataSetIterator;
import org.deeplearning4j.dbn.DBN;
import org.deeplearning4j.iterativereduce.actor.multilayer.ActorNetworkRunner;
import org.deeplearning4j.scaleout.conf.Conf;

public class ParallelGradientStatistics {

	/**
	 * @param args
	 */
	public static void main(String[] args) throws Exception {
		//batches of 10, 60000 examples total
				DataSetIterator iter = new RawMnistDataSetIterator(80,60000);
				
				
				Conf c = new Conf();
				c.setFinetuneEpochs(10000);
				c.setFinetuneLearningRate(0.01);
				c.setLayerSizes(new int[]{500,400,250});
				c.setnIn(784);
				c.setUseAdaGrad(false);
				//c.setRenderWeightEpochs(1000);
				c.setnOut(10);
				c.setSplit(10);
				c.setMultiLayerClazz(DBN.class);
				c.setUseRegularization(false);
				c.setDeepLearningParams(new Object[]{1,0.01,1000});
				c.setRenderWeightEpochs(1000);
				ActorNetworkRunner runner = new ActorNetworkRunner("master",iter);
				runner.setup(c);
				runner.train();
	}

}