tf.keras.constraints.MaxNorm

MaxNorm weight constraint.

Inherits From: Constraint

View aliases

Main aliases

tf.keras.constraints.max_norm

Compat aliases for migration

See Migration guide for more details.

`tf.compat.v1.keras.constraints.MaxNorm`, `tf.compat.v1.keras.constraints.max_norm`

tf.keras.constraints.MaxNorm(
    max_value=2, axis=0
)

Constrains the weights incident to each hidden unit to have a norm less than or equal to a desired value.

Also available via the shortcut function tf.keras.constraints.max_norm.

Args
max_value	the maximum norm value for the incoming weights.
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.

Returns
A tf.keras.constraints.Constraint instance.