MinMaxNorm
Inherits From: Constraint
tf.keras.constraints.MinMaxNorm
tf.keras.constraints.min_max_norm
Defined in tensorflow/python/keras/_impl/keras/constraints.py
.
MinMaxNorm weight constraint.
Constrains the weights incident to each hidden unit to have the norm between a lower bound and an upper bound.
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)
.__init__
__init__( min_value=0.0, max_value=1.0, rate=1.0, axis=0 )
Initialize self. See help(type(self)) for accurate signature.
__call__
__call__(w)
Call self as a function.
get_config
get_config()
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Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/api_docs/python/tf/keras/constraints/MinMaxNorm