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

dropout_layer_dense_batch.cpp

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

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