MinMaxNorm weight constraint.
Inherits From: Constraint
tf.keras.constraints.MinMaxNorm(
min_value=0.0, max_value=1.0, rate=1.0, axis=0
)
Constrains the weights incident to each hidden unit
to have the norm between a lower bound and an upper bound.
Also available via the shortcut function
tf.keras.constraints.min_max_norm.
Args |
|---|
min_value
|
the minimum norm for the incoming weights.
|
max_value
|
the maximum norm for the incoming weights.
|
rate
|
rate for enforcing the constraint: weights will be
rescaled to yield
(1 - rate) * norm + rate * norm.clip(min_value, max_value).
Effectively, this means that rate=1.0 stands for strict
enforcement of the constraint, while rate<1.0 means that
weights will be rescaled at each step to slowly move
towards a value inside the desired interval.
|
axis
|
integer, axis along which to calculate weight norms.
For instance, in a Dense layer the weight matrix
has shape (input_dim, output_dim),
set axis to 0 to constrain each weight vector
of length (input_dim,).
In a Conv2D layer with data_format="channels_last",
the weight tensor has shape
(rows, cols, input_depth, output_depth),
set axis to [0, 1, 2]
to constrain the weights of each filter tensor of size
(rows, cols, input_depth).
|
Methods
from_config
View source
@classmethod
from_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.
|
View source on GitHub