tfp.experimental.nn.util.CallOnce

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 self.name_scope.name which includes parent module names.
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.

Methods

is_unset

View source

is_unset()

Returns True if there is no memoized value and False otherwise.

reset

View source

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.Variables and tf.Tensors 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 &#x27;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__

View source

__call__(
    *args, **kwargs
)

Return the memoized value.