C++ API Reference for Intel® Data Analytics Acceleration Library 2018 Update 2

batch_norm_layer_dense_batch.cpp

/* file: batch_norm_layer_dense_batch.cpp */
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/*
! Content:
! C++ example of forward and backward batch normalization 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";
const size_t dimension = 1;
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);
printTensor(data, "Forward batch normalization layer input (first 5 rows):", 5);
/* Get collection of dimension sizes of the input data tensor */
const Collection<size_t> &dataDims = data->getDimensions();
size_t dimensionSize = dataDims[dimension];
/* Create a collection of dimension sizes of input weights, biases, population mean and variance tensors */
Collection<size_t> dimensionSizes;
dimensionSizes.push_back(dimensionSize);
/* Create input weights, biases, population mean and population variance tensors */
TensorPtr weights(new HomogenTensor<>(dimensionSizes, Tensor::doAllocate, 1.0f));
TensorPtr biases (new HomogenTensor<>(dimensionSizes, Tensor::doAllocate, 2.0f));
TensorPtr populationMean (new HomogenTensor<>(dimensionSizes, Tensor::doAllocate, 0.0f));
TensorPtr populationVariance(new HomogenTensor<>(dimensionSizes, Tensor::doAllocate, 0.0f));
/* Create an algorithm to compute forward batch normalization layer results using default method */
batch_normalization::forward::Batch<> forwardLayer;
forwardLayer.parameter.dimension = dimension;
forwardLayer.input.set(forward::data, data);
forwardLayer.input.set(forward::weights, weights);
forwardLayer.input.set(forward::biases, biases);
forwardLayer.input.set(batch_normalization::forward::populationMean, populationMean);
forwardLayer.input.set(batch_normalization::forward::populationVariance, populationVariance);
/* Compute forward batch normalization layer results */
forwardLayer.compute();
/* Get the computed forward batch normalization layer results */
batch_normalization::forward::ResultPtr forwardResult = forwardLayer.getResult();
printTensor(forwardResult->get(forward::value), "Forward batch normalization layer result (first 5 rows):", 5);
printTensor(forwardResult->get(batch_normalization::auxMean), "Mini-batch mean (first 5 values):", 5);
printTensor(forwardResult->get(batch_normalization::auxStandardDeviation), "Mini-batch standard deviation (first 5 values):", 5);
printTensor(forwardResult->get(batch_normalization::auxPopulationMean), "Population mean (first 5 values):", 5);
printTensor(forwardResult->get(batch_normalization::auxPopulationVariance), "Population variance (first 5 values):", 5);
/* Create input gradient tensor for backward batch normalization layer */
TensorPtr inputGradientTensor = TensorPtr(new HomogenTensor<>(dataDims, Tensor::doAllocate, 10.0f));
/* Create an algorithm to compute backward batch normalization layer results using default method */
batch_normalization::backward::Batch<> backwardLayer;
backwardLayer.parameter.dimension = dimension;
backwardLayer.input.set(backward::inputGradient, inputGradientTensor);
backwardLayer.input.set(backward::inputFromForward, forwardResult->get(forward::resultForBackward));
/* Compute backward batch normalization layer results */
backwardLayer.compute();
/* Get the computed backward batch normalization layer results */
backward::ResultPtr backwardResult = backwardLayer.getResult();
printTensor(backwardResult->get(backward::gradient), "Backward batch normalization layer result (first 5 rows):", 5);
printTensor(backwardResult->get(backward::weightDerivatives), "Weight derivatives (first 5 values):", 5);
printTensor(backwardResult->get(backward::biasDerivatives), "Bias derivatives (first 5 values):", 5);
return 0;
}

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