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 = 0)The minimum norm for the incoming weights.
The maximum norm for the incoming weights.
The rate for enforcing the constraint: weights will be rescaled to yield (1 - rate) * norm + rate * norm.clip(low, high). 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.
The 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 convolution 2D layer with dim_ordering="tf", the weight tensor has
shape rows, cols, input_depth, output_depth, set axis to c(0, 1, 2)
to constrain the weights of each filter tensor of size rows, cols, input_depth.
Other constraints: constraint_maxnorm,
constraint_nonneg,
constraint_unitnorm