tf.keras.constraints.MinMaxNorm
MinMaxNorm weight constraint.
Inherits From: Constraint
tf.keras.constraints.MinMaxNorm(
min_value=0.0, max_value=1.0, rate=1.0, axis=0
)
Constrains the weights incident to each hidden unit to have the norm between a lower bound and an upper bound.
Also available via the shortcut function tf.keras.constraints.min_max_norm.
| Arguments | |
|---|---|
min_value |
the minimum norm for the incoming weights. |
max_value |
the maximum norm for the incoming weights. |
rate |
rate for enforcing the constraint: weights will be rescaled to yield (1 - rate) * norm + rate * norm.clip(min_value, max_value). Effectively, this means that rate=1.0 stands for strict enforcement of the constraint, while rate<1.0 means that weights will be rescaled at each step to slowly move towards a value inside the desired interval. |
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/MinMaxNorm