tf.keras.constraints.MinMaxNorm

MinMaxNorm weight constraint.

Inherits From: Constraint

Main aliases

tf.keras.constraints.min_max_norm

Compat aliases for migration

See Migration guide for more details.

`tf.compat.v1.keras.constraints.MinMaxNorm`, `tf.compat.v1.keras.constraints.min_max_norm`

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.

Args
`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)`.

Methods

@classmethod
from_config(
    config
)

Instantiates a weight constraint from a configuration dictionary.

constraint = UnitNorm()
config = constraint.get_config()
constraint = UnitNorm.from_config(config)

Args
`config`	A Python dictionary, the output of `get_config`.

Returns
A `tf.keras.constraints.Constraint` instance.