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

relu_layer_dense_batch.cpp

/* file: relu_layer_dense_batch.cpp */
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/*
! Content:
! C++ example of forward and backward rectified linear unit (relu) 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 relu layer results using default method */
relu::forward::Batch<> reluLayerForward;
/* Set input objects for the forward relu layer */
reluLayerForward.input.set(forward::data, tensorData);
/* Compute forward relu layer results */
reluLayerForward.compute();
/* Print the results of the forward relu layer */
relu::forward::ResultPtr forwardResult = reluLayerForward.getResult();
printTensor(forwardResult->get(forward::value), "Forward relu layer result (first 5 rows):", 5);
/* Get the size of forward relu 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 relu layer results using default method */
relu::backward::Batch<> reluLayerBackward;
/* Set input objects for the backward relu layer */
reluLayerBackward.input.set(backward::inputGradient, tensorDataBack);
reluLayerBackward.input.set(backward::inputFromForward, forwardResult->get(forward::resultForBackward));
/* Compute backward relu layer results */
reluLayerBackward.compute();
/* Print the results of the backward relu layer */
backward::ResultPtr backwardResult = reluLayerBackward.getResult();
printTensor(backwardResult->get(backward::gradient), "Backward relu layer result (first 5 rows):", 5);
return 0;
}

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