R: Define a custom 'Constraint' class

Constraint {keras3}

R Documentation

Define a custom `Constraint` class

Description

Base class for weight constraints.

A Constraint() instance works like a stateless function. Users who subclass the Constraint class should override the call() method, which takes a single weight parameter and return a projected version of that parameter (e.g. normalized or clipped). Constraints can be used with various Keras layers via the kernel_constraint or bias_constraint arguments.

Here's a simple example of a non-negative weight constraint:

constraint_nonnegative <- Constraint("NonNegative",
  call = function(w) {
    w * op_cast(w >= 0, dtype = w$dtype)
  }
)
weight <- op_convert_to_tensor(c(-1, 1))
constraint_nonnegative()(weight)

## tf.Tensor([-0.  1.], shape=(2), dtype=float32)

Usage in a layer:

layer_dense(units = 4, kernel_constraint = constraint_nonnegative())

## <Dense name=dense, built=False>
##  signature: (*args, **kwargs)

Usage

Constraint(
  classname,
  call = 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).
`call`	\(w) Applies the constraint to the input weight variable. By default, the inputs weight variable is not modified. Users should override this method to implement their own projection function. Args: `w`: Input weight variable. Returns: Projected variable (by default, returns unmodified inputs).
`get_config`	\() Function that returns a named list of the object config. A constraint config is a named list (JSON-serializable) that can be used to reinstantiate the same object (via `⁠do.call(<constraint_class>, <config>)⁠`).
`...`, `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 own `private` environment. Any objects in `private` 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 function that returns Constraint instances, similar to the builtin constraint functions like constraint_maxnorm().

Symbols in scope

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.