tf.keras.constraints.MinMaxNorm | TensorFlow v2.0.0 (original) (raw)

tf.keras.constraints.MinMaxNorm

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MinMaxNorm weight constraint.

Inherits From: Constraint

Main aliases

tf.keras.constraints.min_max_norm

Compat aliases for migration

SeeMigration guide for more details.

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.

Arguments
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).

get_config()

__call__(
    w
)

Call self as a function.

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Last updated 2020-10-01 UTC.