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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.

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)` . |

`get_config`

get_config()

`__call__`

__call__( w )

Call self as a function.

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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/r1.15/api_docs/python/tf/keras/constraints/MinMaxNorm