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

softmax_layer_dense_batch.cpp

/* file: softmax_layer_dense_batch.cpp */
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/*
! Content:
! C++ example of forward and backward softmax 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/layer.csv";
const size_t dimension = 1;
int main()
{
/* Read datasetFileName from a file and create a tensor to store input data */
TensorPtr tensorData = readTensorFromCSV(datasetName);
/* Create an algorithm to compute forward softmax layer results using default method */
softmax::forward::Batch<> softmaxLayerForward;
softmaxLayerForward.parameter.dimension = dimension;
/* Set input objects for the forward softmax layer */
softmaxLayerForward.input.set(forward::data, tensorData);
/* Compute forward softmax layer results */
softmaxLayerForward.compute();
/* Print the results of the forward softmax layer */
softmax::forward::ResultPtr forwardResult = softmaxLayerForward.getResult();
printTensor(forwardResult->get(forward::value), "Forward softmax layer result (first 5 rows):", 5);
/* Get the size of forward softmax layer output */
const Collection<size_t> &gDims = forwardResult->get(forward::value)->getDimensions();
TensorPtr tensorDataBack = TensorPtr(new HomogenTensor<>(gDims, Tensor::doAllocate, 0.01f));
/* Create an algorithm to compute backward softmax layer results using default method */
softmax::backward::Batch<> softmaxLayerBackward;
softmaxLayerBackward.parameter.dimension = dimension;
/* Set input objects for the backward softmax layer */
softmaxLayerBackward.input.set(backward::inputGradient, tensorDataBack);
softmaxLayerBackward.input.set(backward::inputFromForward, forwardResult->get(forward::resultForBackward));
/* Compute backward softmax layer results */
softmaxLayerBackward.compute();
/* Print the results of the backward softmax layer */
backward::ResultPtr backwardResult = softmaxLayerBackward.getResult();
printTensor(backwardResult->get(backward::gradient), "Backward softmax layer result (first 5 rows):", 5);
return 0;
}

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