tf.keras.constraints.UnitNorm
Constrains the weights incident to each hidden unit to have unit norm.
Inherits From: Constraint
tf.keras.constraints.UnitNorm(
axis=0
)
Also available via the shortcut function tf.keras.constraints.unit_norm
.
Args |
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 .
|
Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License, and code samples are licensed under the Apache 2.0 License. For details, see the Google Developers Site Policies. Java is a registered trademark of Oracle and/or its affiliates. Some content is licensed under the numpy license.
Last updated 2023-10-06 UTC.
[null,null,["Last updated 2023-10-06 UTC."],[],[]]