tf.keras.constraints.MinMaxNorm

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MinMaxNorm weight constraint.

Inherits From: Constraint

tf.keras.constraints.MinMaxNorm(
    min_value=0.0, max_value=1.0, rate=1.0, axis=0
)

Constrains the weights incident to each hidden unit to have the norm between a lower bound and an upper bound.

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

min_value the minimum norm for the incoming weights. max_value the maximum norm for the incoming weights. rate rate for enforcing the constraint: weights will be rescaled to yield (1 - rate) * norm + rate * norm.clip(min_value, max_value). Effectively, this means that rate=1.0 stands for strict enforcement of the constraint, while rate<1.0 means that weights will be rescaled at each step to slowly move towards a value inside the desired interval. 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)

config A Python dictionary, the output of get_config.