A context manager for use when defining a Python op.
tf.name_scope(
name, default_name=None, values=None
)
This context manager validates that the given values
are from the
same graph, makes that graph the default graph, and pushes a
name scope in that graph (see
tf.Graph.name_scope
for more details on that).
For example, to define a new Python op called my_op
:
def my_op(a, b, c, name=None):
with tf.name_scope(name, "MyOp", [a, b, c]) 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)
Args |
name
|
The name argument that is passed to the op function.
|
default_name
|
The default name to use if the name argument is None .
|
values
|
The list of Tensor arguments that are passed to the op function.
|
Raises |
TypeError
|
if default_name is passed in but not a string.
|
Methods
__enter__
View source
__enter__()
Start the scope block.
Raises |
ValueError
|
if neither name nor default_name is provided
but values are.
|
__exit__
View source
__exit__(
type_arg, value_arg, traceback_arg
)