C++ API Reference for Intel® Data Analytics Acceleration Library 2018 Update 1

conv2d_layer_dense_batch.cpp

/* file: conv2d_layer_dense_batch.cpp */
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/*
! Content:
! C++ example of forward and backward two-dimensional convolution 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 name */
string datasetFileName = "../data/batch/layer.csv";
int main(int argc, char *argv[])
{
checkArguments(argc, argv, 1, &datasetFileName);
/* 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(16);
inDims.push_back(16);
TensorPtr tensorData = TensorPtr(new HomogenTensor<>(inDims, Tensor::doAllocate, 1.0f));
/* Create an algorithm to compute forward two-dimensional convolution layer results using default method */
convolution2d::forward::Batch<> convolution2dLayerForward;
convolution2dLayerForward.input.set(forward::data, tensorData);
/* Compute forward two-dimensional convolution layer results */
convolution2dLayerForward.compute();
/* Get the computed forward two-dimensional convolution layer results */
convolution2d::forward::ResultPtr forwardResult = convolution2dLayerForward.getResult();
printTensor(forwardResult->get(forward::value), "Two-dimensional convolution layer result (first 5 rows):", 5, 15);
printTensor(forwardResult->get(convolution2d::auxWeights), "Two-dimensional convolution layer weights (first 5 rows):", 5, 15);
const Collection<size_t> &gDims = forwardResult->get(forward::value)->getDimensions();
/* Create input gradient tensor for backward two-dimensional convolution layer */
TensorPtr tensorDataBack = TensorPtr(new HomogenTensor<>(gDims, Tensor::doAllocate, 0.01f));
/* Create an algorithm to compute backward two-dimensional convolution layer results using default method */
convolution2d::backward::Batch<> convolution2dLayerBackward;
convolution2dLayerBackward.input.set(backward::inputGradient, tensorDataBack);
convolution2dLayerBackward.input.set(backward::inputFromForward, forwardResult->get(forward::resultForBackward));
/* Compute backward two-dimensional convolution layer results */
convolution2dLayerBackward.compute();
/* Get the computed backward two-dimensional convolution layer results */
backward::ResultPtr backwardResult = convolution2dLayerBackward.getResult();
printTensor(backwardResult->get(backward::gradient),
"Two-dimensional convolution layer backpropagation gradient result (first 5 rows):", 5, 15);
printTensor(backwardResult->get(backward::weightDerivatives),
"Two-dimensional convolution layer backpropagation weightDerivative result (first 5 rows):", 5, 15);
printTensor(backwardResult->get(backward::biasDerivatives),
"Two-dimensional convolution layer backpropagation biasDerivative result (first 5 rows):", 5, 15);
return 0;
}

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