braindecode.modules.MLP#

class braindecode.modules.MLP(in_features: int, hidden_features=None, out_features=None, activation=<class 'torch.nn.modules.activation.GELU'>, drop=0.0, normalize=False)[source]#

Multilayer Perceptron (MLP) with GELU activation and optional dropout.

Also known as fully connected feedforward network, an MLP is a sequence of non-linear parametric functions

$$h_{i+1}=a_{i+1}(h_i{W}_{i+1}^T+b_{i+1}),$$

over feature vectors $h_i$, with the input and output feature vectors $x=h_0$ and $y=h_L$, respectively. The non-linear functions $a_i$ are called activation functions. The trainable parameters of an MLP are its weights and biases $phi=\{W_i,b_i|i=1,\dots ,L\}$.