Missed TensorFlow World? Check out the recap. Learn more

tf.keras.constraints.MaxNorm

TensorFlow 2.0 version View source on GitHub

Class MaxNorm

MaxNorm weight constraint.

Inherits From: Constraint

Aliases:

  • Class tf.compat.v1.keras.constraints.MaxNorm
  • Class tf.compat.v1.keras.constraints.max_norm
  • Class tf.compat.v2.keras.constraints.MaxNorm
  • Class tf.compat.v2.keras.constraints.max_norm
  • Class tf.keras.constraints.max_norm

Constrains the weights incident to each hidden unit to have a norm less than or equal to a desired value.

Arguments:

  • m: the maximum norm 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).

__init__

View source

__init__(
    max_value=2,
    axis=0
)

Methods

__call__

View source

__call__(w)

get_config

View source

get_config()