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

LCNLayerDenseBatch.java

/* file: LCNLayerDenseBatch.java */
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/*
// Content:
// Java example of local contrast normalization layer in the batch processing mode
*/
package com.intel.daal.examples.neural_networks;
import com.intel.daal.algorithms.neural_networks.layers.lcn.*;
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 LCNLayerDenseBatch {
private static DaalContext context = new DaalContext();
public static void main(String[] args) throws java.io.FileNotFoundException, java.io.IOException {
/* Create collection of dimension sizes of the input data tensor */
long[] dimensionSizes = new long[4];
dimensionSizes[0] = 2;
dimensionSizes[1] = 1;
dimensionSizes[2] = 3;
dimensionSizes[3] = 4;
/* Create input daat tensor */
double[] data = new double[24];
Tensor dataTensor = new HomogenTensor(context, dimensionSizes, data, 1.0);
/* Create an algorithm to compute forward local contrast normalization layer results using default method */
LcnForwardBatch lcnLayerForward = new LcnForwardBatch(context, Float.class, LcnMethod.defaultDense);
/* Set input objects for the forward local contrast normalization layer */
lcnLayerForward.input.set(ForwardInputId.data, dataTensor);
/* Compute forward local contrast normalization layer results */
LcnForwardResult forwardResult = lcnLayerForward.compute();
/* Print the results of the forward local contrast normalization layer */
Service.printTensor("Forward local contrast normalization layer result (first 5 rows):", forwardResult.get(ForwardResultId.value), 5, 0);
Service.printTensor("Centered data tensor (first 5 rows):", forwardResult.get(LcnLayerDataId.auxCenteredData), 5, 0);
Service.printTensor("Sigma tensor (first 5 rows):", forwardResult.get(LcnLayerDataId.auxSigma), 5, 0);
Service.printTensor("C tensor (first 5 rows):", forwardResult.get(LcnLayerDataId.auxC), 5, 0);
Service.printTensor("kernel:", lcnLayerForward.parameter.getKernel(), 5, 0);
Service.printNumericTable("getSumDimension:", lcnLayerForward.parameter.getSumDimension());
/* Create input gradient tensor for backward local contrast normalization layer */
double[] backData = new double[24];
Tensor tensorDataBack = new HomogenTensor(context, dimensionSizes, backData, 0.01);
/* Create an algorithm to compute backward local contrast normalization layer results using default method */
LcnBackwardBatch lcnLayerBackward = new LcnBackwardBatch(context, Float.class, LcnMethod.defaultDense);
/* Set input objects for the backward local contrast normalization layer */
lcnLayerBackward.input.set(BackwardInputId.inputGradient, tensorDataBack);
lcnLayerBackward.input.set(BackwardInputLayerDataId.inputFromForward,
forwardResult.get(ForwardResultLayerDataId.resultForBackward));
/* Compute backward local contrast normalization layer results */
LcnBackwardResult backwardResult = lcnLayerBackward.compute();
/* Get the computed backward local contrast normalization layer results */
Service.printTensor("Backward local contrast normalization layer result (first 5 rows):", backwardResult.get(BackwardResultId.gradient), 5, 0);
context.dispose();
}
}

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