TensorFlow 1 version
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View source on GitHub
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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 | |
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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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