braindecode.modules.MaxNormLinear#

class braindecode.modules.MaxNormLinear(in_features, out_features, bias=True, max_norm_val=2, eps=1e-05, **kwargs)[source]#

Linear layer with MaxNorm constraining on weights.

Equivalent of Keras tf.keras.Dense(…, kernel_constraint=max_norm()) [1] and [2]. Implemented as advised in [3].

Parameters:

• in_features (int) – Size of each input sample.

• out_features (int) – Size of each output sample.

• bias (bool, optional) – If set to False, the layer will not learn an additive bias. Default: True.

References

[1]

https://keras.io/api/layers/core_layers/dense/#dense-class

[2]

https://www.tensorflow.org/api_docs/python/tf/keras/constraints/ MaxNorm

[3]

https://discuss.pytorch.org/t/how-to-correctly-implement-in-place- max-norm-constraint/96769