Usage
new_layer_class(
classname,
initialize = NULL,
call = NULL,
build = NULL,
get_config = NULL,
...,
public = list(),
private = list(),
inherit = NULL,
parent_env = parent.frame()
)
Arguments
- classname
String, the name of the custom class. (Conventionally, CamelCase).
- initialize, call, build, get_config
Recommended methods to implement. See description and details sections.
- ..., public
Additional methods or public members of the custom class.
- private
Named list of R objects (typically, functions) to include in instance private environments.
private
methods will have all the same symbols in scope as public methods (See section "Symbols in Scope"). Each instance will have it's ownprivate
environment. Any objects inprivate
will be invisible from the Keras framework and the Python runtime.- inherit
What the custom class will subclass. By default, the base keras class.
- parent_env
The R environment that all class methods will have as a grandparent.
Value
A composing layer constructor, with similar behavior to other layer
functions like layer_dense()
. The first argument of the returned function
will be object
, enabling initialize()
ing and call()
the layer in one
step while composing the layer with the pipe, like
layer_foo <- Layer("Foo", ....)
output <- inputs |> layer_foo()
To only initialize()
a layer instance and not call()
it, pass a missing
or NULL
value to object
, or pass all arguments to initialize()
by name.
layer <- layer_dense(units = 2, activation = "relu")
layer <- layer_dense(NULL, 2, activation = "relu")
layer <- layer_dense(, 2, activation = "relu")
# then you can call() the layer in a separate step
outputs <- inputs |> layer()