#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";
const size_t dimension = 1;
int main()
{
TensorPtr tensorData = readTensorFromCSV(datasetName);
softmax::forward::Batch<> softmaxLayerForward;
softmaxLayerForward.parameter.dimension = dimension;
softmaxLayerForward.input.set(forward::data, tensorData);
softmaxLayerForward.compute();
softmax::forward::ResultPtr forwardResult = softmaxLayerForward.getResult();
printTensor(forwardResult->get(forward::value), "Forward softmax layer result (first 5 rows):", 5);
const Collection<size_t> &gDims = forwardResult->get(forward::value)->getDimensions();
TensorPtr tensorDataBack = TensorPtr(new HomogenTensor<>(gDims, Tensor::doAllocate, 0.01f));
softmax::backward::Batch<> softmaxLayerBackward;
softmaxLayerBackward.parameter.dimension = dimension;
softmaxLayerBackward.input.set(backward::inputGradient, tensorDataBack);
softmaxLayerBackward.input.set(backward::inputFromForward, forwardResult->get(forward::resultForBackward));
softmaxLayerBackward.compute();
backward::ResultPtr backwardResult = softmaxLayerBackward.getResult();
printTensor(backwardResult->get(backward::gradient), "Backward softmax layer result (first 5 rows):", 5);
return 0;
}