Table 4 Detailed configuration of MNet.
From: Automatic diagnosis of neurological diseases using MEG signals with a deep neural network
Layer | Ksize | Stride | # of filters | Data shape |
|---|---|---|---|---|
Input | (1, 160, 800) | |||
Conv1 | (160, 64) | (1, 2) | 32 | (32, 1, 369) |
Conv2 | (1, 16) | (1, 2) | 64 | (64, 1, 177) |
Pool2 | (1, 2) | (1, 2) | (64, 1, 89) | |
Swap axes | (1, 64, 89) | |||
Conv3 | (8, 8) | (1, 1) | 32 | (32, 57, 82) |
Conv4 | (8, 8) | (1, 1) | 32 | (32, 50, 75) |
Pool4 | (5, 3) | (5, 3) | (32, 10, 25) | |
Conv5 | (1, 4) | (1, 1) | 64 | (64, 10, 22) |
Conv6 | (1, 4) | (1, 1) | 64 | (64, 10, 19) |
Pool6 | (1, 2) | (1, 2) | (64, 10, 10) | |
Conv7 | (1, 2) | (1, 1) | 128 | (128, 10, 9) |
Conv8 | (1, 2) | (1, 1) | 128 | (128, 10, 8) |
Pool8 | (1, 2) | (1, 2) | (128, 10, 4) | |
Conv9 | (1, 2) | (1, 1) | 256 | (256, 10, 3) |
Conv10 | (1, 2) | (1, 1) | 256 | (256, 10, 2) |
Pool10 | (1, 2) | (1, 2) | (256, 10, 1) | |
Fc11 | — | — | 1,024 | (1,024) |
Fc12 | — | — | 1,024 | (1,024) |
Input | (1, 160, 800) | |||
RPS | (1, 160, 6) | |||
Concat | (1,984)* | |||
Fc13 | — | — | # of classes | (# of classes) |
