description: Function object which memoizes the result of create_value_fn().
View source on GitHub |
Function object which memoizes the result of create_value_fn()
.
tfp.experimental.nn.util.CallOnce(
create_value_fn
)
This object is used to memoize the computation of some function. Upon first
call, the user provided create_value_fn
is called and with the args/kwargs
provided to this object's __call__
. On subsequent calls the previous result
is returned and regardless of the args/kwargs provided to this object's
__call__
. To trigger a new evaluation, invoke this.reset()
and to
identify if a new evaluation will execute (on-demand) invoke
this.is_unset()
. For an example application of this object, see
help(tfp.experimental.nn.util.RandomVariable)
and/or
help(tfp.util.DeferredTensor)
.
Args | |
---|---|
create_value_fn
|
Python callable which takes any input args/kwargs and
returns a value to memoize. (The value is not presumed to be of any
particular type.)
|
Attributes | |
---|---|
create_value_fn
|
|
name
|
Returns the name of this module as passed or determined in the ctor.
NOTE: This is not the same as the |
name_scope
|
Returns a tf.name_scope instance for this class.
|
non_trainable_variables
|
Sequence of non-trainable variables owned by this module and its submodules.
Note: this method uses reflection to find variables on the current instance and submodules. For performance reasons you may wish to cache the result of calling this method if you don't expect the return value to change. |
submodules
|
Sequence of all sub-modules.
Submodules are modules which are properties of this module, or found as properties of modules which are properties of this module (and so on). >>> a = tf.Module()
>>> b = tf.Module()
>>> c = tf.Module()
>>> a.b = b
>>> b.c = c
>>> list(a.submodules) == [b, c]
True
>>> list(b.submodules) == [c]
True
>>> list(c.submodules) == []
True
|
trainable_variables
|
Sequence of trainable variables owned by this module and its submodules.
Note: this method uses reflection to find variables on the current instance and submodules. For performance reasons you may wish to cache the result of calling this method if you don't expect the return value to change. |
value
|
|
variables
|
Sequence of variables owned by this module and its submodules.
Note: this method uses reflection to find variables on the current instance and submodules. For performance reasons you may wish to cache the result of calling this method if you don't expect the return value to change. |
is_unset
is_unset()
Returns True
if there is no memoized value and False
otherwise.
reset
reset()
Removes memoized value which triggers re-eval on subsequent reads.
with_name_scope
@classmethod
with_name_scope( method )
Decorator to automatically enter the module name scope.
>>> class MyModule(tf.Module):
... @tf.Module.with_name_scope
... def __call__(self, x):
... if not hasattr(self, 'w'):
... self.w = tf.Variable(tf.random.normal([x.shape[1], 3]))
... return tf.matmul(x, self.w)
Using the above module would produce tf.Variable
s and tf.Tensor
s whose
names included the module name:
>>> mod = MyModule()
>>> mod(tf.ones([1, 2]))
<tf.Tensor: shape=(1, 3), dtype=float32, numpy=..., dtype=float32)>
>>> mod.w
<tf.Variable 'my_module/Variable:0' shape=(2, 3) dtype=float32,
numpy=..., dtype=float32)>
Args | |
---|---|
method
|
The method to wrap. |
Returns | |
---|---|
The original method wrapped such that it enters the module's name scope. |
__call__
__call__(
*args, **kwargs
)
Return the memoized value.