tf.keras.constraints.MaxNorm

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MaxNorm weight constraint.

Inherits From: Constraint

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Main aliases

tf.keras.constraints.max_norm

Compat aliases for migration

See Migration guide for more details.

tf.compat.v1.keras.constraints.MaxNorm, tf.compat.v1.keras.constraints.max_norm

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.

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

Args
max_value	the maximum norm value for the incoming weights.
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).