tfc.layers.RDFTParameter

RDFT reparameterization of a convolution kernel.

Inherits From: Parameter

tfc.layers.RDFTParameter( initial_value, name=None, shape=None, dtype=None )

This uses the real-input discrete Fourier transform (RDFT) of a kernel as its parameterization. The inverse RDFT is applied to the variable to produce the kernel.

(see https://en.wikipedia.org/wiki/Discrete_Fourier_transform)

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 kernel.
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.

shape tf.TensorShape. The shape of the convolution kernel.
real tf.Variable. The real part of the RDFT of the kernel.
imag tf.Variable. The imaginary part of the RDFT of the kernel.
dtype

name Returns the name of this module as passed or determined in the ctor.
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.
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.
variables Sequence of variables owned by this module and its submodules.

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.

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 '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 convolution kernel as a tf.Tensor.