tf.keras.constraints.MaxNorm

MaxNorm weight constraint.

Inherits From: Constraint

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 keras.constraints.max_norm.

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

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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

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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).