 View source on GitHub |
Base class for weight constraints.
A Constraint instance works like a stateless function. Users who subclass this 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:
class NonNegative(keras.constraints.Constraint):def __call__(self, w):return w * ops.cast(ops.greater_equal(w, 0.), dtype=w.dtype)
weight = ops.convert_to_tensor((-1.0, 1.0))NonNegative()(weight)[0., 1.]
Usage in a layer:
keras.layers.Dense(4, kernel_constraint=NonNegative())Methods
from_config
@classmethodfrom_config( config )
Instantiates a weight constraint from a configuration dictionary.
Example:
constraint = UnitNorm() config = constraint.get_config() constraint = UnitNorm.from_config(config) | Args | |
|---|---|
config | A Python dictionary, the output of get_config(). |
| Returns | |
|---|---|
A keras.constraints.Constraint instance. |
get_config
get_config() Returns a Python dict of the object config.
A constraint config is a Python dictionary (JSON-serializable) that can be used to reinstantiate the same object.
| Returns | |
|---|---|
| Python dict containing the configuration of the constraint object. |
__call__
__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). |