tf.keras.constraints.MaxNorm
MaxNorm weight constraint.
Inherits From: Constraint
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.
| Arguments | |
|---|---|
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). |
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Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/versions/r2.4/api_docs/python/tf/keras/constraints/MaxNorm