Ytukyg

From TensorFlow documentation, the following can be done to build graph using inherent OP

#include "tensorflow/cc/client/client_session.h"

#include "tensorflow/cc/ops/standard_ops.h"

#include "tensorflow/core/framework/tensor.h"



int main() {

  using namespace tensorflow;

  using namespace tensorflow::ops;

  Scope root = Scope::NewRootScope();

  // Matrix A = [3 2; -1 0]

  auto A = Const(root, { {3.f, 2.f}, {-1.f, 0.f} });

  // Vector b = [3 5]

  auto b = Const(root, { {3.f, 5.f} });

  // v = Ab^T

  auto v = MatMul(root.WithOpName("v"), A, b, MatMul::TransposeB(true));

  std::vector<Tensor> outputs;

  ClientSession session(root);

  // Run and fetch v

  TF_CHECK_OK(session.Run({v}, &outputs));

  // Expect outputs[0] == [19; -3]

  LOG(INFO) << outputs[0].matrix<float>();

  return 0;

}

It seems that MatMul class is auto generated as there is no tensorflow/cc/ops/math_ops.h in the github source code.
How to do the same thing for custom op such as ZeroOut OP from here

Take ZeroOut from here as example, you have to do the following

class ZeroOut {

 public:

  ZeroOut(const ::tensorflow::Scope& scope, ::tensorflow::Input x);

  operator ::tensorflow::Output() const { return y; }

  operator ::tensorflow::Input() const { return y; }

  ::tensorflow::Node* node() const { return y.node(); }



  ::tensorflow::Output y;

};



ZeroOut::ZeroOut(const ::tensorflow::Scope& scope, ::tensorflow::Input x) {

  if (!scope.ok()) return;

  auto _x = ::tensorflow::ops::AsNodeOut(scope, x);

  if (!scope.ok()) return;

  ::tensorflow::Node* ret;

  const auto unique_name = scope.GetUniqueNameForOp("ZeroOut");

  auto builder = ::tensorflow::NodeBuilder(unique_name, "ZeroOut")

                     .Input(_x)

  ;

  scope.UpdateBuilder(&builder);

  scope.UpdateStatus(builder.Finalize(scope.graph(), &ret));

  if (!scope.ok()) return;

  scope.UpdateStatus(scope.DoShapeInference(ret));

  this->y = Output(ret, 0);

}

Then you can use it to build graph

Scope root = Scope::NewRootScope();

// Matrix A = [3 2; -1 0]

auto A = Const(root, { {3, 2}, {-1, 0} });

auto v = ZeroOut(root.WithOpName("v"), A);

std::vector<Tensor> outputs;

ClientSession session(root);

// Run and fetch v

TF_CHECK_OK(session.Run({v}, &outputs));

LOG(INFO) << outputs[0].matrix<int>();

Note: For TensorFlow inherent OP, code like ZeroOut class is autogenerated by bazel rule. We can imitate those codes(e.g. tensorflow/cc/ops/math_ops.h) to hand write our own classes if we only have a few custom OPs.