Table 5 Network architecture of Batch-Normalized Maxout Network in Network [25]
From: Effective hyperparameter optimization using Nelder-Mead method in deep learning
Conv 1 | Kernel size: 5, stride: 1, pad: 2, BN |
MMLP 1-1 | Kernel size: 1, stride: 1, pad: 0, k = 5, BN |
MMLP 1-2 | Kernel size: 1, stride: 1, pad: 0, k = 5, BN |
Pool 1 (AVE pooling) | Kernel size: 3, stride: 2, pad: 0, dropout |
Conv 2 | Kernel size: 5, stride: 1, pad: 2, BN |
MMLP 2-1 | Kernel size: 1, stride: 1, pad: 0, k = 5, BN |
MMLP 2-2 | Kernel size: 1, stride: 1, pad: 0, k = 5, BN |
Pool 2 (AVE pooling) | Kernel size: 3, stride: 2, pad: 0, dropout |
Conv 3 | Kernel size: 3, stride: 1, pad: 1, BN |
MMLP 3-1 | Kernel size: 1, stride: 1, pad: 0, k = 5, BN |
MMLP 3-2 | Kernel size: 1, stride: 1, pad: 0, k = 5, BN |
Pool 3 (AVE pooling) | Â |