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)A Constraint instance, a callable that can be passed to layer
constructors or used directly by calling it with tensors.
the minimum norm for the incoming weights.
the maximum norm for the incoming weights.
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.
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).
Other constraints:
Constraint()
constraint_maxnorm()
constraint_nonneg()
constraint_unitnorm()