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 integer or integer vector specifying the shape of the output of this layer. TensorFlow dtype of the variable created by this layer. An activation function. See keras::layer_dense. Default: NULL. Initializer for the constant vector. Regularizer function applied to the constant vector. Constraint function applied to the constant vector. Additional keyword arguments passed to the keras::layer_dense constructed by this layer.

## Value

a Keras layer

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()