tf.keras.constraints.MaxNorm | TensorFlow v2.0.0 (original) (raw)

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

SeeMigration 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.

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).

Arguments

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).

Methods

`get_config`

View source

get_config()

`call`

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__call__(
    w
)

Call self as a function.

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Last updated 2020-10-01 UTC.