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 op_clip?

`(1 - rate) * norm + rate * op_clip(norm, 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)`

.

## Value

A `Constraint`

instance, a callable that can be passed to layer
constructors or used directly by calling it with tensors.

## See also

Other constraints: `Constraint()`

`constraint_maxnorm()`

`constraint_nonneg()`

`constraint_unitnorm()`