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tf.keras.constraints.MinMaxNorm

Class `MinMaxNorm`

MinMaxNorm weight constraint.

Inherits From: `Constraint`

Aliases:

• Class `tf.compat.v1.keras.constraints.MinMaxNorm`
• Class `tf.compat.v1.keras.constraints.min_max_norm`
• Class `tf.compat.v2.keras.constraints.MinMaxNorm`
• Class `tf.compat.v2.keras.constraints.min_max_norm`
• Class `tf.keras.constraints.min_max_norm`

Constrains the weights incident to each hidden unit to have the norm between a lower bound and an upper bound.

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)`.

`__init__`

View source

``````__init__(
min_value=0.0,
max_value=1.0,
rate=1.0,
axis=0
)
``````

Methods

`__call__`

View source

``````__call__(w)
``````

`get_config`

View source

``````get_config()
``````