guide.md

C++ API

Note: By default tensorflow.org shows docs for the most recent stable version. The instructions in this doc require building from source. You will probably want to build from the master version of tensorflow. You should, as a result, be sure you are following the master version of this doc, in case there have been any changes.

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TensorFlow's C++ API provides mechanisms for constructing and executing a data flow graph. The API is designed to be simple and concise: graph operations are clearly expressed using a "functional" construction style, including easy specification of names, device placement, etc., and the resulting graph can be efficiently run and the desired outputs fetched in a few lines of code. This guide explains the basic concepts and data structures needed to get started with TensorFlow graph construction and execution in C++.

The Basics

Let's start with a simple example that illustrates graph construction and execution using the C++ API.

// tensorflow/cc/example/example.cc

#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;
}

Place this example code in the file tensorflow/cc/example/example.cc inside a clone of the TensorFlow github repository. Also place a BUILD file in the same directory with the following contents:

load("//tensorflow:tensorflow.bzl", "tf_cc_binary")

tf_cc_binary(
    name = "example",
    srcs = ["example.cc"],
    deps = [
        "//tensorflow/cc:cc_ops",
        "//tensorflow/cc:client_session",
        "//tensorflow/core:tensorflow",
    ],
)

Use tf_cc_binary rather than Bazel's native cc_binary to link in necessary symbols from libtensorflow_framework.so. You should be able to build and run the example using the following command (be sure to run ./configure in your build sandbox first):

bazel run -c opt //tensorflow/cc/example:example

This example shows some of the important features of the C++ API such as the following:

• Constructing tensor constants from C++ nested initializer lists
• Constructing and naming of TensorFlow operations
• Specifying optional attributes to operation constructors
• Executing and fetching the tensor values from the TensorFlow session.

We will delve into the details of each below.

Graph Construction

Scope

tensorflow::Scope is the main data structure that holds the current state of graph construction. A Scope acts as a handle to the graph being constructed, as well as storing TensorFlow operation properties. The Scope object is the first argument to operation constructors, and operations that use a given Scope as their first argument inherit that Scope's properties, such as a common name prefix. Multiple Scopes can refer to the same graph, as explained further below.

Create a new Scope object by calling Scope::NewRootScope. This creates some resources such as a graph to which operations are added. It also creates a tensorflow::Status object which will be used to indicate errors encountered when constructing operations. The Scope class has value semantics, thus, a Scope object can be freely copied and passed around.

The Scope object returned by Scope::NewRootScope is referred to as the root scope. "Child" scopes can be constructed from the root scope by calling various member functions of the Scope class, thus forming a hierarchy of scopes. A child scope inherits all of the properties of the parent scope and typically has one property added or changed. For instance, NewSubScope(name) appends name to the prefix of names for operations created using the returned Scope object.

Here are some of the properties controlled by a Scope object:

• Operation names
• Set of control dependencies for an operation
• Device placement for an operation
• Kernel attribute for an operation

Please refer to tensorflow::Scope for the complete list of member functions that let you create child scopes with new properties.

Operation Constructors

You can create graph operations with operation constructors, one C++ class per TensorFlow operation. Unlike the Python API which uses snake-case to name the operation constructors, the C++ API uses camel-case to conform to C++ coding style. For instance, the MatMul operation has a C++ class with the same name.

Using this class-per-operation method, it is possible, though not recommended, to construct an operation as follows:

// Not recommended
MatMul m(scope, a, b);

Instead, we recommend the following "functional" style for constructing operations:

// Recommended
auto m = MatMul(scope, a, b);

The first parameter for all operation constructors is always a Scope object. Tensor inputs and mandatory attributes form the rest of the arguments.

For optional arguments, constructors have an optional parameter that allows optional attributes. For operations with optional arguments, the constructor's last optional parameter is a struct type called [operation]:Attrs that contains data members for each optional attribute. You can construct such Attrs in multiple ways:

• You can specify a single optional attribute by constructing an Attrs object using the static functions provided in the C++ class for the operation. For example:

auto m = MatMul(scope, a, b, MatMul::TransposeA(true));

• You can specify multiple optional attributes by chaining together functions available in the Attrs struct. For example:

auto m = MatMul(scope, a, b, MatMul::TransposeA(true).TransposeB(true));

// Or, alternatively
auto m = MatMul(scope, a, b, MatMul::Attrs().TransposeA(true).TransposeB(true));

The arguments and return values of operations are handled in different ways depending on their type:

• For operations that return single tensors, the object returned by the operation object can be passed directly to other operation constructors. For example:

auto m = MatMul(scope, x, W);
auto sum = Add(scope, m, bias);

• For operations producing multiple outputs, the object returned by the operation constructor has a member for each of the outputs. The names of those members are identical to the names present in the OpDef for the operation. For example:

auto u = Unique(scope, a);
// u.y has the unique values and u.idx has the unique indices
auto m = Add(scope, u.y, b);

• Operations producing a list-typed output return an object that can be indexed using the [] operator. That object can also be directly passed to other constructors that expect list-typed inputs. For example:

auto s = Split(scope, 0, a, 2);
// Access elements of the returned list.
auto b = Add(scope, s[0], s[1]);
// Pass the list as a whole to other constructors.
auto c = Concat(scope, s, 0);

Constants

You may pass many different types of C++ values directly to tensor constants. You may explicitly create a tensor constant by calling the tensorflow::ops::Const function from various kinds of C++ values. For example:

• Scalars

auto f = Const(scope, 42.0f);
auto s = Const(scope, "hello world!");

• Nested initializer lists

// 2x2 matrix
auto c1 = Const(scope, { {1, 2}, {2, 4} });
// 1x3x1 tensor
auto c2 = Const(scope, { { {1}, {2}, {3} } });
// 1x2x0 tensor
auto c3 = ops::Const(scope, { { {}, {} } });

• Shapes explicitly specified

// 2x2 matrix with all elements = 10
auto c1 = Const(scope, 10, /* shape */ {2, 2});
// 1x3x2x1 tensor
auto c2 = Const(scope, {1, 2, 3, 4, 5, 6}, /* shape */ {1, 3, 2, 1});

You may directly pass constants to other operation constructors, either by explicitly constructing one using the Const function, or implicitly as any of the above types of C++ values. For example:

// [1 1] * [41; 1]
auto x = MatMul(scope, { {1, 1} }, { {41}, {1} });
// [1 2 3 4] + 10
auto y = Add(scope, {1, 2, 3, 4}, 10);

Graph Execution

When executing a graph, you will need a session. The C++ API provides a tensorflow::ClientSession class that will execute ops created by the operation constructors. TensorFlow will automatically determine which parts of the graph need to be executed, and what values need feeding. For example:

Scope root = Scope::NewRootScope();
auto c = Const(root, { {1, 1} });
auto m = MatMul(root, c, { {42}, {1} });

ClientSession session(root);
std::vector<Tensor> outputs;
session.Run({m}, &outputs);
// outputs[0] == {42}

Similarly, the object returned by the operation constructor can be used as the argument to specify a value being fed when executing the graph. Furthermore, the value to feed can be specified with the different kinds of C++ values used to specify tensor constants. For example:

Scope root = Scope::NewRootScope();
auto a = Placeholder(root, DT_INT32);
// [3 3; 3 3]
auto b = Const(root, 3, {2, 2});
auto c = Add(root, a, b);
ClientSession session(root);
std::vector<Tensor> outputs;

// Feed a <- [1 2; 3 4]
session.Run({ {a, { {1, 2}, {3, 4} } } }, {c}, &outputs);
// outputs[0] == [4 5; 6 7]

Please see the tensorflow::Tensor documentation for more information on how to use the execution output.