Java* API Reference for Intel® Data Analytics Acceleration Library 2018 Update 3

DropoutLayerDenseBatch.java

/* file: DropoutLayerDenseBatch.java */
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/*
// Content:
// Java example of dropout layer in the batch processing mode
*/
package com.intel.daal.examples.neural_networks;
import com.intel.daal.algorithms.neural_networks.layers.dropout.*;
import com.intel.daal.algorithms.neural_networks.layers.ForwardResultId;
import com.intel.daal.algorithms.neural_networks.layers.ForwardResultLayerDataId;
import com.intel.daal.algorithms.neural_networks.layers.ForwardInputId;
import com.intel.daal.algorithms.neural_networks.layers.BackwardResultId;
import com.intel.daal.algorithms.neural_networks.layers.BackwardInputId;
import com.intel.daal.algorithms.neural_networks.layers.BackwardInputLayerDataId;
import com.intel.daal.data_management.data.Tensor;
import com.intel.daal.data_management.data.HomogenTensor;
import com.intel.daal.examples.utils.Service;
import com.intel.daal.services.DaalContext;
class DropoutLayerDenseBatch {
private static final String datasetFileName = "../data/batch/layer.csv";
private static DaalContext context = new DaalContext();
public static void main(String[] args) throws java.io.FileNotFoundException, java.io.IOException {
/* Read datasetFileName from a file and create a tensor to store forward input data */
Tensor data = Service.readTensorFromCSV(context, datasetFileName);
/* Create an algorithm to compute forward dropout layer results using default method */
DropoutForwardBatch forwardLayer = new DropoutForwardBatch(context, Float.class, DropoutMethod.defaultDense);
/* Set input objects for the forward dropout layer */
forwardLayer.input.set(ForwardInputId.data, data);
/* Compute forward dropout layer results */
DropoutForwardResult forwardResult = forwardLayer.compute();
/* Print the results of the forward dropout layer */
Service.printTensor("Forward dropout layer result (first 5 rows):", forwardResult.get(ForwardResultId.value), 5, 0);
Service.printTensor("Dropout layer retain mask (first 5 rows):", forwardResult.get(DropoutLayerDataId.auxRetainMask), 5, 0);
/* Create input gradient tensor for backward dropout layer */
long[] dims = forwardResult.get(ForwardResultId.value).getDimensions();
double[] inputGradientData = new double[(int)forwardResult.get(ForwardResultId.value).getSize()];
Tensor inputGradient = new HomogenTensor(context, dims, inputGradientData, 0.01);
/* Create an algorithm to compute backward dropout layer results using default method */
DropoutBackwardBatch backwardLayer = new DropoutBackwardBatch(context, Float.class, DropoutMethod.defaultDense);
/* Set input objects for the backward dropout layer */
backwardLayer.input.set(BackwardInputId.inputGradient, inputGradient);
backwardLayer.input.set(BackwardInputLayerDataId.inputFromForward, forwardResult.get(ForwardResultLayerDataId.resultForBackward));
/* Compute backward dropout layer results */
DropoutBackwardResult backwardResult = backwardLayer.compute();
/* Print the results of the backward dropout layer */
Service.printTensor("Backward dropout layer result (first 5 rows):", backwardResult.get(BackwardResultId.gradient), 5, 0);
context.dispose();
}
}

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