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

initializers_dense_batch.cpp

/* file: initializers_dense_batch.cpp */
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/*
! Content:
! C++ example of initializers
!******************************************************************************/
#include "daal.h"
#include "service.h"
using namespace daal;
using namespace daal::algorithms;
using namespace daal::algorithms::neural_networks;
using namespace daal::algorithms::neural_networks::layers;
using namespace daal::data_management;
using namespace daal::services;
int main(int argc, char *argv[])
{
/* 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));
/* Fill tensor data using truncated gaussian initializer */
/* Create an algorithm to initialize data using default method */
initializers::truncated_gaussian::Batch<> truncatedGaussInitializer(0.0, 1.0);
/* Set input object and parameters for the truncated gaussian initializer */
truncatedGaussInitializer.input.set(initializers::data, tensorData);
/* Compute truncated gaussian initializer */
truncatedGaussInitializer.compute();
/* Print the results of the truncated gaussian initializer */
printTensor(tensorData, "Data with truncated gaussian distribution:");
/* Fill tensor data using gaussian initializer */
/* Create an algorithm to initialize data using default method */
initializers::gaussian::Batch<> gaussInitializer(1.0, 0.5);
/* Set input object for the gaussian initializer */
gaussInitializer.input.set(initializers::data, tensorData);
/* Compute gaussian initializer */
gaussInitializer.compute();
/* Print the results of the gaussian initializer */
printTensor(tensorData, "Data with gaussian distribution:");
/* Fill tensor data using uniform initializer */
/* Create an algorithm to initialize data using default method */
initializers::uniform::Batch<> uniformInitializer(-5.0, 5.0);
/* Set input object and parameters for the uniform initializer */
uniformInitializer.input.set(initializers::data, tensorData);
/* Compute uniform initializer */
uniformInitializer.compute();
/* Print the results of the uniform initializer */
printTensor(tensorData, "Data with uniform distribution:");
/* Fill layer weights using xavier initializer */
/* Create an algorithm to compute forward fully-connected layer results using default method */
fullyconnected::forward::Batch<> fullyconnectedLayerForward(5);
/* Set input objects and parameter for the forward fully-connected layer */
fullyconnectedLayerForward.input.set(forward::data, tensorData);
fullyconnectedLayerForward.parameter.weightsInitializer.reset(new initializers::xavier::Batch<>());
/* Compute forward fully-connected layer results */
fullyconnectedLayerForward.compute();
/* Print the results of the xavier initializer */
printTensor(fullyconnectedLayerForward.input.get(layers::forward::weights), "Weights filled by xavier initializer:");
return 0;
}

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