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

ave_pool2d_layer_dense_batch.cpp

/* file: ave_pool2d_layer_dense_batch.cpp */
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/*
! Content:
! C++ example of neural network forward and backward two-dimensional average pooling layers 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);
/* Read datasetFileName from a file and create a tensor to store input data */
TensorPtr data = readTensorFromCSV(datasetFileName);
size_t nDim = data->getNumberOfDimensions();
printTensor(data, "Forward two-dimensional average pooling layer input (first 10 rows):", 10);
/* Create an algorithm to compute forward two-dimensional maximum pooling layer results using default method */
average_pooling2d::forward::Batch<> forwardLayer(nDim);
forwardLayer.input.set(forward::data, data);
/* Compute forward two-dimensional average pooling layer results */
forwardLayer.compute();
/* Get the computed forward two-dimensional average pooling layer results */
average_pooling2d::forward::ResultPtr forwardResult = forwardLayer.getResult();
printTensor(forwardResult->get(forward::value), "Forward two-dimensional average pooling layer result (first 5 rows):", 5);
printNumericTable(forwardResult->get(average_pooling2d::auxInputDimensions), "Forward two-dimensional average pooling layer input dimensions:");
/* Create an algorithm to compute backward two-dimensional average pooling layer results using default method */
average_pooling2d::backward::Batch<> backwardLayer(nDim);
backwardLayer.input.set(backward::inputGradient, forwardResult->get(forward::value));
backwardLayer.input.set(backward::inputFromForward, forwardResult->get(forward::resultForBackward));
/* Compute backward two-dimensional average pooling layer results */
backwardLayer.compute();
/* Get the computed backward two-dimensional average pooling layer results */
backward::ResultPtr backwardResult = backwardLayer.getResult();
printTensor(backwardResult->get(backward::gradient),
"Backward two-dimensional average pooling layer result (first 10 rows):", 10);
return 0;
}

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