Java* API Reference for Intel® Data Analytics Acceleration Library 2018 Update 1

FullyconLayerDenseBatch.java

/* file: FullyconLayerDenseBatch.java */
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/*
// Content:
// Java example of fully-connected layer in the batch processing mode
*/
package com.intel.daal.examples.neural_networks;
import com.intel.daal.algorithms.neural_networks.layers.fullyconnected.*;
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 FullyconLayerDenseBatch {
private static final String datasetFileName = "../data/batch/layer.csv";
private static DaalContext context = new DaalContext();
private static final long m = 5;
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 fully-connected layer results using default method */
FullyConnectedForwardBatch forwardLayer = new FullyConnectedForwardBatch(context, Float.class, FullyConnectedMethod.defaultDense, m);
/* Set input objects for the forward fully-connected layer */
forwardLayer.input.set(ForwardInputId.data, data);
/* Compute forward fully-connected layer results */
FullyConnectedForwardResult forwardResult = forwardLayer.compute();
/* Print the results of the forward fully-connected layer */
Service.printTensor("Forward fully-connected layer result (first 5 rows):", forwardResult.get(ForwardResultId.value), 5, 0);
/* Create input gradient tensor for backward fully-connected 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 fully-connected layer results using default method */
FullyConnectedBackwardBatch backwardLayer = new FullyConnectedBackwardBatch(context, Float.class, FullyConnectedMethod.defaultDense, m);
/* Set input objects for the backward fully-connected layer */
backwardLayer.input.set(BackwardInputId.inputGradient, inputGradient);
backwardLayer.input.set(BackwardInputLayerDataId.inputFromForward, forwardResult.get(ForwardResultLayerDataId.resultForBackward));
/* Compute backward fully-connected layer results */
FullyConnectedBackwardResult backwardResult = backwardLayer.compute();
/* Print the results of the backward fully-connected layer */
Service.printTensor("Backward fully-connected gradient result (first 5 rows):",
backwardResult.get(BackwardResultId.gradient), 5, 0);
Service.printTensor("Backward fully-connected weightDerivatives result (first 5 rows):",
backwardResult.get(BackwardResultId.weightDerivatives), 5, 0);
Service.printTensor("Backward fully-connected biasDerivatives result (first 5 rows):",
backwardResult.get(BackwardResultId.biasDerivatives), 5, 0);
context.dispose();
}
}

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