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# tf.keras.constraints.MinMaxNorm

MinMaxNorm weight constraint.

Inherits From: `Constraint`

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

`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

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### `__call__`

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Call self as a function.

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