C++ API Reference for Intel® Data Analytics Acceleration Library 2019 Update 5

loss_logistic_entr_layer_dense_batch.cpp

/* file: loss_logistic_entr_layer_dense_batch.cpp */
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/*
! Content:
! C++ example of forward and backward logistic cross-entropy layer usage
!
!******************************************************************************/
#include "daal.h"
#include "service.h"
using namespace std;
using namespace daal;
using namespace daal::algorithms;
using namespace daal::algorithms::neural_networks::layers;
using namespace daal::data_management;
using namespace daal::services;
/* Input data set parameters */
string datasetName = "../data/batch/logistic_cross_entropy_layer.csv";
string datasetGroundTruthName = "../data/batch/logistic_cross_entropy_layer_ground_truth.csv";
int main()
{
/* Read datasetFileName from a file and create a tensor to store input data */
TensorPtr tensorData = readTensorFromCSV(datasetName);
TensorPtr groundTruth = readTensorFromCSV(datasetGroundTruthName);
/* Create an algorithm to compute forward logistic cross-entropy layer results using default method */
loss::logistic_cross::forward::Batch<> logisticCrossEntropyLayerForward;
/* Set input objects for the forward logistic cross-entropy layer */
logisticCrossEntropyLayerForward.input.set(forward::data, tensorData);
logisticCrossEntropyLayerForward.input.set(loss::forward::groundTruth, groundTruth);
/* Compute forward logistic cross-entropy layer results */
logisticCrossEntropyLayerForward.compute();
/* Print the results of the forward logistic cross-entropy layer */
loss::logistic_cross::forward::ResultPtr forwardResult = logisticCrossEntropyLayerForward.getResult();
printTensor(forwardResult->get(forward::value), "Forward logistic cross-entropy layer result (first 5 rows):", 5);
printTensor(forwardResult->get(loss::logistic_cross::auxGroundTruth), "Logistic Cross-Entropy layer ground truth (first 5 rows):", 5);
/* Create an algorithm to compute backward logistic cross-entropy layer results using default method */
loss::logistic_cross::backward::Batch<> logisticCrossEntropyLayerBackward;
/* Set input objects for the backward logistic cross-entropy layer */
logisticCrossEntropyLayerBackward.input.set(backward::inputFromForward, forwardResult->get(forward::resultForBackward));
/* Compute backward logistic cross-entropy layer results */
logisticCrossEntropyLayerBackward.compute();
/* Print the results of the backward logistic cross-entropy layer */
backward::ResultPtr backwardResult = logisticCrossEntropyLayerBackward.getResult();
printTensor(backwardResult->get(backward::gradient), "Backward logistic cross-entropy layer result (first 5 rows):", 5);
return 0;
}

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