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
)

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

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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)

Args
config A Python dictionary, the output of get_config().

Returns
A keras.constraints.Constraint instance.

get_config

View source

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.

Returns
Python dict containing the configuration of the constraint 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.

Args
w Input weight variable.

Returns
Projected variable (by default, returns unmodified inputs).