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

abs_layer_dense_batch.cpp

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

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