tfprobability (version 0.15.1)

layer_variable: Variable Layer

Description

Simply returns a (trainable) variable, regardless of input. This layer implements the mathematical function f(x) = c where c is a constant, i.e., unchanged for all x. Like other Keras layers, the constant is trainable. This layer can also be interpretted as the special case of layer_dense() when the kernel is forced to be the zero matrix (tf$zeros).

Usage

layer_variable(
  object,
  shape,
  dtype = NULL,
  activation = NULL,
  initializer = "zeros",
  regularizer = NULL,
  constraint = NULL,
  ...
)

Value

a Keras layer

Arguments

object

What to compose the new Layer instance with. Typically a Sequential model or a Tensor (e.g., as returned by layer_input()). The return value depends on object. If object is:

• missing or NULL, the Layer instance is returned.

• a Sequential model, the model with an additional layer is returned.

• a Tensor, the output tensor from layer_instance(object) is returned.

shape

integer or integer vector specifying the shape of the output of this layer.

dtype

TensorFlow dtype of the variable created by this layer.

activation

An activation function. See keras::layer_dense. Default: NULL.

initializer

Initializer for the constant vector.

regularizer

Regularizer function applied to the constant vector.

constraint

Constraint function applied to the constant vector.

...

Additional keyword arguments passed to the keras::layer_dense constructed by this layer.

See Also

Other layers: layer_autoregressive(), layer_conv_1d_flipout(), layer_conv_1d_reparameterization(), layer_conv_2d_flipout(), layer_conv_2d_reparameterization(), layer_conv_3d_flipout(), layer_conv_3d_reparameterization(), layer_dense_flipout(), layer_dense_local_reparameterization(), layer_dense_reparameterization(), layer_dense_variational()