tf.keras.constraints.Constraint

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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(tf.keras.constraints.Constraint):

 def __call__(self, w):
   return w * tf.cast(tf.math.greater_equal(w, 0.), w.dtype)

weight = tf.constant((-1.0, 1.0))
NonNegative()(weight)
<tf.Tensor: shape=(2,), dtype=float32, numpy=array([0.,  1.],
dtype=float32)>

tf.keras.layers.Dense(4, kernel_constraint=NonNegative())

Methods

from_config

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@classmethod
from_config(
    config
)

Instantiates a weight constraint from a configuration dictionary.

Example:

constraint = UnitNorm()
config = constraint.get_config()
constraint = UnitNorm.from_config(config)

get_config

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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.

__call__

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__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.