|---| | [ View source on GitHub](https://github.com/keras-team/keras/tree/v3.15.1/keras/src/constraints/constraints.py#L160-L215) | MinMaxNorm weight constraint. Inherits From: [`Constraint`](https://www.tensorflow.org/api_docs/python/tf/keras/constraints/Constraint) #### View aliases **Main aliases** [`tf.keras.constraints.min_max_norm`](https://www.tensorflow.org/api_docs/python/tf/keras/constraints/MinMaxNorm)
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.


| ## 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 ### `from_config` [View source](https://github.com/keras-team/keras/tree/v3.15.1/keras/src/constraints/constraints.py#L59-L77) @classmethod from_config( config ) Instantiates a weight constraint from a configuration dictionary. #### Example: constraint = UnitNorm() config = constraint.get_config() constraint = UnitNorm.from_config(config)


| Args || |---|---| | `config` | A Python dictionary, the output of `get_config()`. |



| Returns || |---|---| | A [`keras.constraints.Constraint`](https://www.tensorflow.org/api_docs/python/tf/keras/constraints/Constraint) instance. ||
### `get_config` [View source](https://github.com/keras-team/keras/tree/v3.15.1/keras/src/constraints/constraints.py#L209-L215) get_config() Returns a Python dict of the object config. A constraint config is a Python dictionary (JSON-serializable) that can be used to reinstantiate the same object.


| Returns || |---|---| | Python dict containing the configuration of the constraint object. ||
### `__call__` [View source](https://github.com/keras-team/keras/tree/v3.15.1/keras/src/constraints/constraints.py#L198-L207) __call__( w ) Applies the constraint to the input weight variable. By default, the inputs weight variable is not modified. Users should override this method to implement their own projection function.


| Args || |---|---| | `w` | Input weight variable. |



| Returns || |---|---| | Projected variable (by default, returns unmodified inputs). ||