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

elu_layer_dense_batch.cpp

/* file: elu_layer_dense_batch.cpp */
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/*
! Content:
! C++ example of forward and backward Exponential Linear Unit (ELU) 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";
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 ELU layer results using default method */
elu::forward::Batch<> eluLayerForward;
/* Set input objects for the forward ELU layer */
eluLayerForward.input.set(forward::data, tensorData);
/* Compute forward ELU layer results */
eluLayerForward.compute();
// /* Print the results of the forward ELU layer */
elu::forward::ResultPtr forwardResult = eluLayerForward.getResult();
printTensor(forwardResult->get(forward::value), "Forward ELU layer result (first 5 rows):", 5);
// /* Get the size of forward ELU layer output */
const Collection<size_t> &gDims = forwardResult->get(forward::value)->getDimensions();
TensorPtr tensorDataBack = TensorPtr(new HomogenTensor<>(gDims, Tensor::doAllocate, 1.0f));
// /* Create an algorithm to compute backward ELU layer results using default method */
elu::backward::Batch<> eluLayerBackward;
// /* Set input objects for the backward ELU layer */
eluLayerBackward.input.set(backward::inputGradient, tensorDataBack);
eluLayerBackward.input.set(backward::inputFromForward, forwardResult->get(forward::resultForBackward));
// /* Compute backward ELU layer results */
eluLayerBackward.compute();
// /* Print the results of the backward ELU layer */
backward::ResultPtr backwardResult = eluLayerBackward.getResult();
printTensor(backwardResult->get(backward::gradient), "Backward ELU layer result (first 5 rows):", 5);
return 0;
}

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