Table 2 CNN layers and hyperparameters
Layer | Hyperparameters |
|---|---|
Conv2D | 32 filters, 3 × 3 filter size, ReLU activation, same padding, followed by batch normalization |
MaxPool2D | 3 × 3 pool size to reduce image spatial dimensions quickly from 96 × 96 to 32 × 32 |
Dropout (Core Layer) | 0.25 Neurons |
Conv2D | 64 filters, 3 × 3 filter size, ReLU activation, same padding |
Conv2D | 64 filters, 3 × 3 filter size, ReLU activation, following the same padding, batch normalization is performed |
MaxPool2D | 2 × 2 pool size |
Dropout (Core Layer) | 0.25 Neurons |
Conv2D | 128 filters, 3 × 3 filter, ReLU activation, following the same padding, batch normalization is performed |
Conv2D | 128 filters, 3 × 3 filter size, ReLU activation, same padding followed by batch normalization |
MaxPool2D | 2 × 2 pool size |
Dropout (Core Layer) | 0.25 Neurons |
Flatten (Core Layer) | – |
Dense | 1024 Units, ReLU sctivation, and batch normalization |
Dropout (Core Layer) | 0.5 Neurons |
Dense | 7 Units, softmax activation |
