Variable Layer

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).

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

Arguments

object	Model or layer object
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.

Value

a Keras 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()