tfc.layers.Parameter

Reparameterized Layer variable.

View aliases

Main aliases

tfc.Parameter

tfc.layers.Parameter(
 name=None
)

This object represents a parameter of a tf.keras.layer.Layer object which isn't directly stored in a tf.Variable, but can be represented as a function (of any number of tf.Variable attributes).

Attributes
`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.
`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

`get_config`

View source

@abc.abstractmethod
get_config()

Returns the configuration of the Parameter.

`get_weights`

View source

get_weights()

`set_weights`

View source

set_weights(
 weights
)

`with_name_scope`

@classmethod
with_name_scope(
 method
)

Decorator to automatically enter the module name scope.

classMyModule(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 '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

@abc.abstractmethod
__call__(
 compute_dtype=None
)

Computes and returns the parameter value as a tf.Tensor.

tfc.layers.Parameter Stay organized with collections Save and categorize content based on your preferences.

View aliases

Attributes

Methods

get_config

get_weights

set_weights

with_name_scope

__call__

tfc.layers.Parameter

`get_config`

`get_weights`

`set_weights`

`with_name_scope`

`call`