Java* API Reference for Intel® Data Analytics Acceleration Library 2019 Update 5

PReLULayerDenseBatch.java

/* file: PReLULayerDenseBatch.java */
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/*
// Content:
// Java example of prelu layer in the batch processing mode
*/
package com.intel.daal.examples.neural_networks;
import com.intel.daal.algorithms.neural_networks.layers.prelu.*;
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 PReLULayerDenseBatch {
private static final String datasetFileName = "../data/batch/layer.csv";
private static final String weightsFileName = "../data/batch/layer.csv";
/* Prelu layer parameters */
private static final long dataDimension = 0; /* Starting data dimension index to apply weight */
private static final long weightsDimension = 2; /* Number of weight dimensions */
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);
Tensor weights = Service.readTensorFromCSV(context, weightsFileName);
/* Create an algorithm to compute forward prelu layer results using default method */
PreluForwardBatch preluLayerForward = new PreluForwardBatch(context, Float.class, PreluMethod.defaultDense);
/* Set algorithm parameters */
preluLayerForward.parameter.setDataDimension(dataDimension);
preluLayerForward.parameter.setWeightsDimension(weightsDimension);
preluLayerForward.parameter.setWeightsAndBiasesInitializationFlag(true);
/* Set input objects for the forward prelu layer */
preluLayerForward.input.set(ForwardInputId.data, data);
preluLayerForward.input.set(ForwardInputId.weights, weights);
/* Compute forward prelu layer results */
PreluForwardResult forwardResult = preluLayerForward.compute();
/* Print the results of the forward prelu layer */
Service.printTensor("Forward prelu layer result (first 5 rows):", forwardResult.get(ForwardResultId.value), 5, 0);
/* Get the size of forward prelu layer output */
int nSize = (int)forwardResult.get(ForwardResultId.value).getSize();
long[] dims = forwardResult.get(ForwardResultId.value).getDimensions();
/* Create a tensor with backward input data */
double[] backData = new double[nSize];
Tensor tensorDataBack = new HomogenTensor(context, dims, backData, 0.01);
/* Create an algorithm to compute backward prelu layer results using default method */
PreluBackwardBatch preluLayerBackward = new PreluBackwardBatch(context, Float.class, PreluMethod.defaultDense);
/* Set algorithm parameters */
preluLayerBackward.parameter.setDataDimension(dataDimension);
preluLayerBackward.parameter.setWeightsDimension(weightsDimension);
/* Set input objects for the backward prelu layer */
preluLayerBackward.input.set(BackwardInputId.inputGradient, tensorDataBack);
preluLayerBackward.input.set(BackwardInputLayerDataId.inputFromForward, forwardResult.get(ForwardResultLayerDataId.resultForBackward));
/* Compute backward prelu layer results */
PreluBackwardResult backwardResult = preluLayerBackward.compute();
/* Print the results of the backward prelu layer */
Service.printTensor("Backward prelu layer result (first 5 rows):", backwardResult.get(BackwardResultId.gradient), 5, 0);
Service.printTensor("Weights derivative (first 5 rows):", backwardResult.get(BackwardResultId.weightDerivatives), 5, 0);
context.dispose();
}
}

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