tf.keras.constraints.MaxNorm

MaxNorm weight constraint.

Inherits From: Constraint

View aliases

Main aliases

tf.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)`.

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.