C++ API Reference for Intel® Data Analytics Acceleration Library 2018 Update 3

eltwise_sum_layer_dense_batch.cpp

/* file: eltwise_sum_layer_dense_batch.cpp */
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/*
! Content:
! C++ example of forward and backward element-wise sum 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";
/* Number of input tensors */
const size_t nInputs = 3;
int main()
{
/* Create an algorithm to compute forward element-wise sum layer results using default method */
eltwise_sum::forward::Batch<> eltwiseSumLayerForward;
/* Read datasetFileName from a file and create a tensor to store input data */
for (size_t i = 0; i < nInputs; i++)
{
TensorPtr tensorData = readTensorFromCSV(datasetName);
/* Set input objects for the forward element-wise sum layer */
eltwiseSumLayerForward.input.set(forward::inputLayerData, tensorData, i);
}
/* Compute forward element-wise sum layer results */
eltwiseSumLayerForward.compute();
/* Print the results of the forward element-wise sum layer */
eltwise_sum::forward::ResultPtr forwardResult = eltwiseSumLayerForward.getResult();
printTensor(forwardResult->get(forward::value), "Forward element-wise sum layer result (first 5 rows):", 5);
printNumericTable(forwardResult->get(eltwise_sum::auxNumberOfCoefficients),
"Forward element-wise sum layer number of inputs (number of coefficients)", 1);
/* Create an algorithm to compute backward element-wise sum layer results using default method */
eltwise_sum::backward::Batch<> eltwiseSumLayerBackward;
/* Set input objects for the backward element-wise sum layer */
eltwiseSumLayerBackward.input.set(backward::inputGradient, readTensorFromCSV(datasetName));
eltwiseSumLayerBackward.input.set(backward::inputFromForward, forwardResult->get(forward::resultForBackward));
/* Compute backward element-wise sum layer results */
eltwiseSumLayerBackward.compute();
/* Print the results of the backward element-wise sum layer */
eltwise_sum::backward::ResultPtr backwardResult = eltwiseSumLayerBackward.getResult();
for (size_t i = 0; i < nInputs; i++)
{
printTensor(backwardResult->get(backward::resultLayerData, i),
"Backward element-wise sum layer backward result (first 5 rows):", 5);
}
return 0;
}

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