tfc.layers.GDNParameter

Nonnegative parameterization as needed for GDN parameters.

Inherits From: Parameter

View aliases

Main aliases

tfc.GDNParameter

tfc.layers.GDNParameter(
 initial_value,
 name=None,
 minimum=0.0,
 offset=(2 ** -18),
 shape=None,
 dtype=None
)

The variable is subjected to an invertible transformation that slows down the learning rate for small values.

Args
`initial_value`	`tf.Tensor` or `None`. The initial value of the kernel. If not provided, its `shape` must be given, and the initial value of the parameter will be undefined.
`name`	String. The name of the parameter.
`minimum`	Float. Lower bound for the parameter (defaults to zero).
`offset`	Float. Offset added to the reparameterization. The parameterization of beta/gamma as their square roots lets the training slow down when values are close to zero, which is desirable as small values in the denominator can lead to a situation where gradient noise on beta/gamma leads to extreme amounts of noise in the GDN activations. However, without the offset, we would get zero gradients if any elements of beta or gamma were exactly zero, and thus the training could get stuck. To prevent this, we add this small constant. The default value was empirically determined as a good starting point. Making it bigger potentially leads to more gradient noise on the activations, making it too small may lead to numerical precision issues.
`shape`	`tf.TensorShape` or compatible. Ignored unless `initial_value is None`.
`dtype`	`tf.dtypes.DType` or compatible. DType of this parameter. If not given, inferred from `initial_value`.

Attributes
`minimum`	Float. The `minimum` parameter provided on initialization.
`offset`	Float. The `offset` parameter provided on initialization.
`variable`	`tf.Variable`. The reparameterized variable.
`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

get_config() -> Dict[str, Any]

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

__call__(
 compute_dtype=None
) -> tf.Tensor

Computes and returns the non-negative value as a tf.Tensor.

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

View aliases

Args

Attributes

Methods

get_config

get_weights

set_weights

with_name_scope

__call__

tfc.layers.GDNParameter

`get_config`

`get_weights`

`set_weights`

`with_name_scope`

`call`