main.cpp

#include "tensorflow/core/public/session.h"
#include "tensorflow/core/graph/default_device.h"
using namespace tensorflow;

int main(int argc, char* argv[]) {

	std::string graph_definition = "mlp.pb";
	Session* session;
	GraphDef graph_def;
	SessionOptions opts;
	std::vector<Tensor> outputs; // Store outputs
	TF_CHECK_OK(ReadBinaryProto(Env::Default(), graph_definition, &graph_def));

	// Set GPU options
	graph::SetDefaultDevice("/gpu:0", &graph_def);
	opts.config.mutable_gpu_options()->set_per_process_gpu_memory_fraction(0.5);
	opts.config.mutable_gpu_options()->set_allow_growth(true);

	// create a new session
	TF_CHECK_OK(NewSession(opts, &session));

	// Load graph into session
	TF_CHECK_OK(session->Create(graph_def));

	// Initialize our variables
	TF_CHECK_OK(session->Run({}, {}, {"init_all_vars_op"}, nullptr));

	Tensor x(DT_FLOAT, TensorShape({100, 32}));
	Tensor y(DT_FLOAT, TensorShape({100, 8}));
	auto _XTensor = x.matrix<float>();
	auto _YTensor = y.matrix<float>();

	_XTensor.setRandom();
	_YTensor.setRandom();

	for (int i = 0; i < 10; ++i) {
		TF_CHECK_OK(session->Run({{"x", x}, {"y", y}}, {"cost"}, {}, &outputs));
		float cost = outputs[0].scalar<float>()(0);
		std::cout << "Cost: " <<  cost << std::endl;
		TF_CHECK_OK(session->Run({{"x", x}, {"y", y}}, {}, {"train"}, nullptr));
		outputs.clear();
	}


	session->Close();
	delete session;
	return 0;
}