R: MinMaxNorm weight constraint.

constraint_minmaxnorm {keras3}

R Documentation

MinMaxNorm weight constraint.

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

constraint_minmaxnorm(min_value = 0, max_value = 1, rate = 1, axis = 1L)

`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)⁠`.

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