C++ API Reference for Intel® Data Analytics Acceleration Library 2019 Update 4

concat_layer_dense_batch.cpp

/* file: concat_layer_dense_batch.cpp */
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/*
! Content:
! C++ example of forward and backward concatenation (concat) 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";
const size_t concatDimension = 1;
const size_t nInputs = 3;
int main(int argc, char *argv[])
{
checkArguments(argc, argv, 1, &datasetName);
/* Retrieve the input data */
TensorPtr tensorData = readTensorFromCSV(datasetName);
LayerDataPtr tensorDataCollection = LayerDataPtr(new LayerData());
for(int i = 0; i < nInputs; i++)
{
(*tensorDataCollection)[i] = tensorData;
}
/* Create an algorithm to compute forward concatenation layer results using default method */
concat::forward::Batch<> concatLayerForward(concatDimension);
/* Set input objects for the forward concatenation layer */
concatLayerForward.input.set(forward::inputLayerData, tensorDataCollection);
/* Compute forward concatenation layer results */
concatLayerForward.compute();
/* Print the results of the forward concatenation layer */
concat::forward::ResultPtr forwardResult = concatLayerForward.getResult();
printTensor(forwardResult->get(forward::value), "Forward concatenation layer result value (first 5 rows):", 5);
/* Create an algorithm to compute backward concatenation layer results using default method */
concat::backward::Batch<> concatLayerBackward(concatDimension);
/* Set inputs for the backward concatenation layer */
concatLayerBackward.input.set(backward::inputGradient, forwardResult->get(forward::value));
concatLayerBackward.input.set(backward::inputFromForward, forwardResult->get(forward::resultForBackward));
printNumericTable(forwardResult->get(concat::auxInputDimensions), "auxInputDimensions ");
/* Compute backward concatenation layer results */
concatLayerBackward.compute();
/* Print the results of the backward concatenation layer */
concat::backward::ResultPtr backwardResult = concatLayerBackward.getResult();
for(size_t i = 0; i < tensorDataCollection->size(); i++)
{
printTensor(backwardResult->get(backward::resultLayerData, i), "Backward concatenation layer backward result (first 5 rows):", 5);
}
return 0;
}

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