tf.Graph

A TensorFlow computation, represented as a dataflow graph.

Used in the notebooks

Used in the guide Used in the tutorials

Graphs are used by tf.functions to represent the function's computations. Each graph contains a set of tf.Operation objects, which represent units of computation; and tf.Tensor objects, which represent the units of data that flow between operations.

Using graphs directly (deprecated)

A tf.Graph can be constructed and used directly without a tf.function, as was required in TensorFlow 1, but this is deprecated and it is recommended to use a tf.function instead. If a graph is directly used, other deprecated TensorFlow 1 classes are also required to execute the graph, such as a tf.compat.v1.Session.

A default graph can be registered with the tf.Graph.as_default context manager. Then, operations will be added to the graph instead of being executed eagerly. For example:

g = tf.Graph()
with g.as_default():
  # Define operations and tensors in `g`.
  c = tf.constant(30.0)
  assert c.graph is g

tf.compat.v1.get_default_graph() can be used to obtain the default graph.

Important note: This class is not thread-safe for graph construction. All operations should be created from a single thread, or external synchronization must be provided. Unless otherwise specified, all methods are not thread-safe.

A Graph instance supports an arbitrary number of "collections" that are identified by name. For convenience when building a large graph, collections can store groups of related objects: for example, the tf.Variable uses a collection (named tf.GraphKeys.GLOBAL_VARIABLES) for all variables that are created during the construction of a graph. The caller may define additional collections by specifying a new name.