Min max norm constraint
This card is a wrapper of this Keras class.
Note: the backend for building and training neural networks is based on Keras. The documentation of this card is a variant of the documentation of its corresponding class.
Inputs
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Min value — Float
The minimum norm for the incoming weights.
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Max value — Float
The maximum norm for the incoming weights.
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Rate — Float
Rate for enforcing the constraint: weights will be rescaled to yield
(1 - rate) * norm + rate * norm.clip(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. -
Axes — List of Integer
Axis along which to calculate weight norms. For instance, in a Dense layer the weight matrix has shape (
input_dim,output_dim), set Axes to “0” to constrain each weight vector of length (input_dim,). In a Convolution 2D layer with Data format = “Channels last”, the weight tensor has shape (rows,cols,input_depth,output_depth), set Axes to “0, 1, 2” to constrain the weights of each filter tensor of size (rows,cols,input_depth).
Outputs
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Constraint instance — NeuralNetworkConstraint
Instance of the constraint.