Model
ClassThis is for advanced use cases where you need to subclass the base Model
type, e.g., you want to override the train_step()
method.
If you just want to create or define a keras model, prefer keras_model()
or keras_model_sequential()
.
If you just want to encapsulate some custom logic and state, and don't need
to customize training behavior (besides calling self$add_loss()
in the
call()
method), prefer Layer()
.
Model(
classname,
initialize = NULL,
call = NULL,
train_step = NULL,
predict_step = NULL,
test_step = NULL,
compute_loss = NULL,
compute_metrics = NULL,
...,
public = list(),
private = list(),
inherit = NULL,
parent_env = parent.frame()
)
A model constructor function, which you can call to create an instance of the new model type.
String, the name of the custom class. (Conventionally, CamelCase).
Optional methods that can be overridden.
Additional methods or public members of the custom class.
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 own private
environment. Any objects
in private
will be invisible from the Keras framework and the Python
runtime.
What the custom class will subclass. By default, the base keras class.
The R environment that all class methods will have as a grandparent.
All R function custom methods (public and private) will have the following symbols in scope:
self
: The custom class instance.
super
: The custom class superclass.
private
: An R environment specific to the class instance.
Any objects assigned here are invisible to the Keras framework.
__class__
and as.symbol(classname)
: the custom class type object.
active_property()
(e.g., for a metrics
property implemented as a
function).