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.

building_function Returns True iff this graph represents a function.
collections Returns the names of the collections known to this graph.
finalized True if this graph has been finalized.
graph_def_versions The GraphDef version information of this graph.

For details on the meaning of each version, see GraphDef.

seed The graph-level random seed of this graph.
version Returns a version number that increases as ops are added to the graph.

Note that this is unrelated to the tf.Graph.graph_def_versions.



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Stores value in the collection with the given name.

Note that collections are not sets, so it is possible to add a value to a collection several times.

name The key for the collection. The GraphKeys class contains many standard names for collections.
value The value to add to the collection.


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Stores value in the collections given by names.

Note that collections are not sets, so it is possible to add a value to a collection several times. This function makes sure that duplicates in names are ignored, but it will not check for pre-existing membership of value in any of the collections in names.

names can be any iterable, but if names is a string, it is treated as a single collection name.

names The keys for the collections to add to. The GraphKeys class contains many standard names for collections.
value The value to add to the collections.


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Returns a context manager that makes this Graph the default graph.

This method should be used if you want to create multiple graphs in the same process. For convenience, a global default graph is provided, and all ops will be added to this graph if you do not create a new graph explicitly.

Use this method with the with keyword to specify that ops created within the scope of a block should be added to this graph. In this case, once the scope of the with is exited, the previous default graph is set again as default. There is a stack, so it's ok to have multiple nested levels of as_default calls.

The default graph is a property of the current thread. If you create a new thread, and wish to use the default graph in that thread, you must explicitly add a with g.as_default(): in that thread's function.

The following code examples are equivalent: