|TensorFlow 1 version||View source on GitHub|
MaxNorm weight constraint.
See Migration guide for more details.
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.
m: the maximum norm for the incoming weights.
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.