tf.name_scope

A context manager for use when defining a Python op.

tf.name_scope(
    name
) -> None

Used in the notebooks

Used in the guide	Used in the tutorials
Migrating model checkpoints	Displaying text data in TensorBoard Graph-based Neural Structured Learning in TFX

This context manager pushes a name scope, which will make the name of all operations added within it have a prefix.

For example, to define a new Python op called my_op:

def my_op(a, b, c, name=None):
  with tf.name_scope("MyOp") as scope:
    a = tf.convert_to_tensor(a, name="a")
    b = tf.convert_to_tensor(b, name="b")
    c = tf.convert_to_tensor(c, name="c")
    # Define some computation that uses `a`, `b`, and `c`.
    return foo_op(..., name=scope)

When executed, the Tensors a, b, c, will have names MyOp/a, MyOp/b, and MyOp/c.

Inside a tf.function, if the scope name already exists, the name will be made unique by appending _n. For example, calling my_op the second time will generate MyOp_1/a, etc.

Args
name	The prefix to use on all names created within the name scope.

Raises
ValueError	If name is not a string.

Attributes
name

Methods

__enter__

View source

__enter__() -> str

Start the scope block.

Returns
The scope name.

__exit__

View source

__exit__(
    type_arg: None, value_arg: None, traceback_arg: None
) -> bool

Raise any exception triggered within the runtime context.