The penalties are applied on a per-layer basis. The exact API will depend on the layer, but the layers Dense, Conv1D, Conv2D and Conv3D have a unified API.

These layers expose 2 keyword arguments:

• kernel_constraint for the main weights matrix
• bias_constraint for the bias.

Available constraints

MaxNorm weight constraint.

Arguments

• max_value: the maximum norm for the incoming weights.
• 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 , 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).

References

[source]

Constrains the weights to be non-negative.

Constrains the weights incident to each hidden unit to have unit norm.

Arguments

• 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 to [0, 1, 2] to constrain the weights of each filter tensor of size (rows, cols, input_depth).

MinMaxNorm weight constraint.

Arguments

• 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 (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.