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

lcn_layer_dense_batch.cpp

/* file: lcn_layer_dense_batch.cpp */
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/*
! Content:
! C++ example of forward and backward local contrast normalization 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;
int main()
{
/* Create collection of dimension sizes of the input data tensor */
Collection<size_t> inDims;
inDims.push_back(2);
inDims.push_back(1);
inDims.push_back(3);
inDims.push_back(4);
TensorPtr tensorData = TensorPtr(new HomogenTensor<>(inDims, Tensor::doAllocate, 1.0f));
/* Create an algorithm to compute forward local contrast normalization layer results using default method */
lcn::forward::Batch<> lcnLayerForward;
/* Set input objects for the forward local contrast normalization layer */
lcnLayerForward.input.set(forward::data, tensorData);
/* Compute forward local contrast normalization layer results */
lcnLayerForward.compute();
/* Print the results of the forward local contrast normalization layer */
lcn::forward::ResultPtr forwardResult = lcnLayerForward.getResult();
printTensor(forwardResult->get(forward::value), "Forward local contrast normalization layer result:");
printTensor(forwardResult->get(lcn::auxCenteredData), "Centered data tensor:");
printTensor(forwardResult->get(lcn::auxSigma), "Sigma tensor:");
printTensor(forwardResult->get(lcn::auxC), "C tensor:");
printTensor(forwardResult->get(lcn::auxInvMax), "Inverted max(sigma, C):");
/* Create input gradient tensor for backward local contrast normalization layer */
TensorPtr tensorDataBack = TensorPtr(new HomogenTensor<>(inDims, Tensor::doAllocate, 0.01f));
/* Create an algorithm to compute backward local contrast normalization layer results using default method */
lcn::backward::Batch<> lcnLayerBackward;
lcnLayerBackward.input.set(backward::inputGradient, tensorDataBack);
lcnLayerBackward.input.set(backward::inputFromForward, forwardResult->get(forward::resultForBackward));
/* Compute backward local contrast normalization layer results */
lcnLayerBackward.compute();
/* Get the computed backward local contrast normalization layer results */
backward::ResultPtr backwardResult = lcnLayerBackward.getResult();
printTensor(backwardResult->get(backward::gradient), "Local contrast normalization layer backpropagation gradient result:");
return 0;
}

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