Java* API Reference for Intel® Data Analytics Acceleration Library 2019 Update 4

LossLogisticEntrLayerDenseBatch.java

/* file: LossLogisticEntrLayerDenseBatch.java */
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/*
// Content:
// Java example of logistic cross-entropy layer in the batch processing mode
*/
package com.intel.daal.examples.neural_networks;
import com.intel.daal.algorithms.neural_networks.layers.logistic_cross.*;
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.loss.LossForwardInputId;
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 LossLogisticEntrLayerDenseBatch {
private static final String datasetFileName = "../data/batch/logistic_cross_entropy_layer.csv";
private static final String datasetGroundTruthFileName = "../data/batch/logistic_cross_entropy_layer_ground_truth.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);
Tensor groundTruth = Service.readTensorFromCSV(context, datasetGroundTruthFileName);
/* Create an algorithm to compute forward logistic cross-entropy layer results using default method */
LogisticCrossForwardBatch forwardLayer = new LogisticCrossForwardBatch(context, Float.class, LogisticCrossMethod.defaultDense);
/* Set input objects for the forward logistic cross-entropy layer */
forwardLayer.input.set(LossForwardInputId.data, data);
forwardLayer.input.set(LossForwardInputId.groundTruth, groundTruth);
/* Compute forward logistic cross-entropy layer results */
LogisticCrossForwardResult forwardResult = forwardLayer.compute();
/* Print the results of the forward logistic cross-entropy layer */
Service.printTensor("Forward logistic cross-entropy layer result (first 5 rows):", forwardResult.get(ForwardResultId.value), 5, 0);
Service.printTensor("Logistic cross-Entropy layer probabilities estimations (first 5 rows):", forwardResult.get(LogisticCrossLayerDataId.auxGroundTruth), 5, 0);
/* Create an algorithm to compute backward logistic cross-entropy layer results using default method */
LogisticCrossBackwardBatch backwardLayer = new LogisticCrossBackwardBatch(context, Float.class, LogisticCrossMethod.defaultDense);
/* Set input objects for the backward logistic cross-entropy layer */
backwardLayer.input.set(BackwardInputLayerDataId.inputFromForward, forwardResult.get(ForwardResultLayerDataId.resultForBackward));
/* Compute backward logistic cross-entropy layer results */
LogisticCrossBackwardResult backwardResult = backwardLayer.compute();
/* Print the results of the backward logistic cross-entropy layer */
Service.printTensor("Backward logistic cross-entropy layer result (first 5 rows):", backwardResult.get(BackwardResultId.gradient), 5, 0);
context.dispose();
}
}

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