Table 2.
The internal architecture of the proposed Deep-NSR model with its relevant hyperparameters. Here, ReLU is used after each convolution layer and BN is used after each ReLU and dropout; fully connected and softmax layers are not shown.
| Layer name | Output size | Kernel size | # Filters | Stride |
|---|---|---|---|---|
| Conv2d-1 | 62 × 62 | 5 × 5 | 32 | 2 |
| MaxPool2d-4 | 30 × 30 | 3 × 3 | 1 | 2 |
| Conv2d-5 | 28 × 28 | 3 × 3 | 64 | 1 |
| Conv2d-8 | 26 × 26 | 3 × 3 | 128 | 1 |
| Conv2d-11 | 24 × 24 | 3 × 3 | 256 | 1 |
| AvgPool2d-14 | 11 × 11 | 3 × 3 | 1 | 2 |
| Conv2d-15 | 9 × 9 | 3 × 3 | 8 | 1 |
| AdaptiveAvgPool2d-18 | 1 × 1 | 9 × 9 | 1 | — |