matmul.cpp

// matmul.cpp

#include <vector>
#include <eigen/Dense>

#include "matmul.h"
#include "tensorflow/core/public/session.h"
#include "tensorflow/cc/ops/standard_ops.h"

using namespace tensorflow;

// Build a computation graph that takes a tensor of shape [?, 2] and
// multiplies it by a hard-coded matrix.
GraphDef CreateGraphDef()
{
  Scope root = Scope::NewRootScope();

  auto X = ops::Placeholder(root.WithOpName("x"), DT_FLOAT, 
                            ops::Placeholder::Shape({ -1, 2 }));
  auto A = ops::Const(root, { { 3.f, 2.f },{ -1.f, 0.f } });

  auto Y = ops::MatMul(root.WithOpName("y"), A, X, 
                       ops::MatMul::TransposeB(true));

  GraphDef def;
  TF_CHECK_OK(root.ToGraphDef(&def));

  return def;
}

int main()
{
  GraphDef graph_def = CreateGraphDef();

  // Start up the session
  SessionOptions options;
  std::unique_ptr<Session> session(NewSession(options));
  TF_CHECK_OK(session->Create(graph_def));

  // Define some data.  This needs to be converted to an Eigen Tensor to be
  // fed into the placeholder.  Note that this will be broken up into two
  // separate vectors of length 2: [1, 2] and [3, 4], which will separately
  // be multiplied by the matrix.
  std::vector<float> data = { 1, 2, 3, 4 };
  auto mapped_X_ = Eigen::TensorMap<Eigen::Tensor<float, 2, Eigen::RowMajor>>
                     (&data[0], 2, 2);
  auto eigen_X_ = Eigen::Tensor<float, 2, Eigen::RowMajor>(mapped_X_);

  Tensor X_(DT_FLOAT, TensorShape({ 2, 2 }));
  X_.tensor<float, 2>() = eigen_X_;

  std::vector<Tensor> outputs;
  TF_CHECK_OK(session->Run({ { "x", X_ } }, { "y" }, {}, &outputs));

  // Get the result and print it out
  Tensor Y_ = outputs[0];
  std::cout << Y_.tensor<float, 2>() << std::endl;
  
  session->Close();
}