MinMaxNorm weight constraint.

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

Usage

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.