|TensorFlow 1 version||View source on GitHub|
MinMaxNorm weight constraint.
See Migration 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.
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
Denselayer the weight matrix has shape
(input_dim, output_dim), set
0to constrain each weight vector of length
(input_dim,). In a
data_format="channels_last", the weight tensor has shape
(rows, cols, input_depth, output_depth), set
[0, 1, 2]to constrain the weights of each filter tensor of size
(rows, cols, input_depth).
__call__( w )
Call self as a function.