C++ API Reference for Intel® Data Analytics Acceleration Library 2018 Update 1

loss_softmax_entr_layer_dense_batch.cpp

/* file: loss_softmax_entr_layer_dense_batch.cpp */
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/*
! Content:
! C++ example of forward and backward softmax 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;
static const size_t nDim = 3;
static const size_t dims[] = {3, 2, 4};
static const size_t gtDims[] = {3, 1, 4};
static float gTArray[3][1][4] = {{{1, 0, 0, 1}},
{{0, 0, 1, 1}},
{{1, 0, 0, 1}}};
static float dataArray[3][2][4] = {{{ 1, 2, 3, 4},
{ 5, 6, 7, 8}},
{{9, 10, 11, 12},
{13, 14, 15, 16}},
{{17, 18, 19, 20},
{21, 22, 23, 24}}};
int main()
{
TensorPtr tensorData(new HomogenTensor<>(nDim, dims, (float *)dataArray));
TensorPtr groundTruth(new HomogenTensor<>(nDim, gtDims, (float *)gTArray));
printTensor(tensorData, "Forward softmax cross-entropy layer input data:");
printTensor(groundTruth, "Forward softmax cross-entropy layerr input ground truth:");
/* Create an algorithm to compute forward softmax cross-entropy layer results using default method */
loss::softmax_cross::forward::Batch<> softmaxCrossEntropyLayerForward;
/* Set input objects for the forward softmax cross-entropy layer */
softmaxCrossEntropyLayerForward.input.set(forward::data, tensorData);
softmaxCrossEntropyLayerForward.input.set(loss::forward::groundTruth, groundTruth);
/* Compute forward softmax cross-entropy layer results */
softmaxCrossEntropyLayerForward.compute();
/* Print the results of the forward softmax cross-entropy layer */
loss::softmax_cross::forward::ResultPtr forwardResult = softmaxCrossEntropyLayerForward.getResult();
printTensor(forwardResult->get(forward::value), "Forward softmax cross-entropy layer result (first 5 rows):", 5);
printTensor(forwardResult->get(loss::softmax_cross::auxProbabilities), "Softmax Cross-Entropy layer probabilities estimations (first 5 rows):", 5);
printTensor(forwardResult->get(loss::softmax_cross::auxGroundTruth), "Softmax Cross-Entropy layer ground truth (first 5 rows):", 5);
/* Create an algorithm to compute backward softmax cross-entropy layer results using default method */
loss::softmax_cross::backward::Batch<> softmaxCrossEntropyLayerBackward;
/* Set input objects for the backward softmax cross-entropy layer */
softmaxCrossEntropyLayerBackward.input.set(backward::inputFromForward, forwardResult->get(forward::resultForBackward));
/* Compute backward softmax cross-entropy layer results */
softmaxCrossEntropyLayerBackward.compute();
/* Print the results of the backward softmax cross-entropy layer */
backward::ResultPtr backwardResult = softmaxCrossEntropyLayerBackward.getResult();
printTensor(backwardResult->get(backward::gradient), "Backward softmax cross-entropy layer result (first 5 rows):", 5);
return 0;
}

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