layer_variable | R Documentation |
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 |
What to compose the new
|
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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