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, ... )
object |
Model or layer object |
shape |
integer or integer vector specifying the shape of the output of this layer. |
dtype |
TensorFlow |
activation |
An activation function. See |
initializer |
Initializer for the |
regularizer |
Regularizer function applied to the |
constraint |
Constraint function applied to the |
... |
Additional keyword arguments passed to the |
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()
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