Usage of constraints

Functions from the constraints module allow setting constraints (eg. non-negativity) on network parameters during optimization.

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.

from keras.constraints import max_norm
model.add(Dense(64, kernel_constraint=max_norm(2.)))

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Available constraints

[source]

MaxNorm

keras.constraints.MaxNorm(max_value=2, axis=0)

MaxNorm weight constraint.

Constrains the weights incident to each hidden unit to have a norm less than or equal to a desired value.

Arguments

• m: 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 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).

References

• Dropout: A Simple Way to Prevent Neural Networks from Overfitting

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[source]

NonNeg

keras.constraints.NonNeg()

Constrains the weights to be non-negative.

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[source]

UnitNorm

keras.constraints.UnitNorm(axis=0)

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

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[source]

MinMaxNorm

keras.constraints.MinMaxNorm(min_value=0.0, max_value=1.0, rate=1.0, axis=0)

MinMaxNorm weight constraint.

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

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. 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).

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