tf.name_scope | TensorFlow v2.16.1 (original) (raw)
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__
__enter__() -> str
Start the scope block.
| Returns |
|---|
| The scope name. |
__exit__
__exit__(
type_arg: None, value_arg: None, traceback_arg: None
) -> bool
Raise any exception triggered within the runtime context.