#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;
string datasetName = "../data/batch/layer.csv";
int main()
{
TensorPtr tensorData = readTensorFromCSV(datasetName);
lrn::forward::Batch<> forwardLRNlayer;
forwardLRNlayer.input.set(forward::data, tensorData);
forwardLRNlayer.compute();
lrn::forward::ResultPtr forwardResult = forwardLRNlayer.getResult();
printTensor(tensorData, "LRN layer input (first 5 rows):", 5);
printTensor(forwardResult->get(forward::value), "LRN layer result (first 5 rows):", 5);
printTensor(forwardResult->get(lrn::auxSmBeta), "LRN layer auxSmBeta (first 5 rows):", 5);
const Collection<size_t> &gDims = forwardResult->get(forward::value)->getDimensions();
TensorPtr tensorDataBack = TensorPtr(new HomogenTensor<>(gDims, Tensor::doAllocate, 0.01f));
lrn::backward::Batch<> backwardLRNlayer;
backwardLRNlayer.input.set(backward::inputGradient, tensorDataBack);
backwardLRNlayer.input.set(backward::inputFromForward, forwardResult->get(forward::resultForBackward));
backwardLRNlayer.compute();
backward::ResultPtr backwardResult = backwardLRNlayer.getResult();
printTensor(backwardResult->get(backward::gradient), "LRN layer backpropagation result (first 5 rows):", 5);
return 0;
}