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

LRNLayerDenseBatch.java

/* file: LRNLayerDenseBatch.java */
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/*
// Content:
// Java example of local response normalization layer in the batch processing mode
*/
package com.intel.daal.examples.neural_networks;
import com.intel.daal.algorithms.neural_networks.layers.lrn.*;
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 LRNLayerDenseBatch {
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);
Service.printTensor("LRN layer input (first 5 rows):",data, 5, 0);
/* Create an algorithm to compute forward local response normalization layer results using default method */
LrnForwardBatch lrnLayerForward = new LrnForwardBatch(context, Float.class, LrnMethod.defaultDense);
/* Set input objects for the forward local response normalization layer */
lrnLayerForward.input.set(ForwardInputId.data, data);
/* Compute forward local response normalization layer results */
LrnForwardResult forwardResult = lrnLayerForward.compute();
/* Print the results of the forward local response normalization layer */
Service.printTensor("LRN layer result (first 5 rows):",
forwardResult.get(ForwardResultId.value), 5, 0);
//Service.printTensor("LRN layer auxSmBeta (first 5 rows):",
// forwardResult.get(LrnLayerDataId.auxSmBeta), 5, 0);
/* Get the size of forward local response normalization 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 local response normalization layer results using default method */
LrnBackwardBatch lrnLayerBackward = new LrnBackwardBatch(context, Float.class, LrnMethod.defaultDense);
/* Set input objects for the backward local response normalization layer */
lrnLayerBackward.input.set(BackwardInputId.inputGradient, tensorDataBack);
lrnLayerBackward.input.set(BackwardInputLayerDataId.inputFromForward, forwardResult.get(ForwardResultLayerDataId.resultForBackward));
/* Compute backward local response normalization layer results */
LrnBackwardResult backwardResult = lrnLayerBackward.compute();
/* Print the results of the backward local response normalization layer */
Service.printTensor("LRN layer backpropagation result (first 5 rows):",
backwardResult.get(BackwardResultId.gradient), 5, 0);
context.dispose();
}
}

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