Table 2.
Layers property of the CNN architecture. The network consists of twenty-five layers. There are eight layers with learnable weights: five convolutional layers, and three fully connected layers
| No. | Layer name | Description |
|---|---|---|
| 1 | Image input | 227 × 227 × 3 true color images with zerocenter standardization |
| 2 | 1st-level convolution | 96 channels, 11 × 11 × 3 convolutions |
| 3 | ReLU | Rectified linear units |
| 4 | Cross channel standardization | Cross channel standardization with 5 channels per element |
| 5 | Max pooling | 3 × 3 max pooling |
| 6 | 2nd-level convolution | 256 channels, 5 × 5 × 48 convolutions |
| 7 | ReLU | Rectified linear units |
| 8 | Cross channel standardization | Cross channel standardization with 5 channels per element |
| 9 | Max pooling | 3 × 3 max pooling |
| 10 | 3rd-level convolution | 384 channels, 3 × 3 × 256 convolutions |
| 11 | ReLU | Rectified linear units |
| 12 | 4th-level convolution | 384 channels, 3 × 3 × 192 convolutions |
| 13 | ReLU | Rectified linear units |
| 14 | 5th-level convolution | 256 channels, 3 × 3 × 192 convolutions |
| 15 | ReLU | Rectified linear units |
| 16 | Max pooling | 3 × 3 max pooling |
| 17 | 6th-level fully connected layer | 4096 fully connected layer |
| 18 | ReLU | Rectified linear units |
| 19 | Dropout | 50% of dropout |
| 20 | 7th-level fully connected layer | 4096 fully connected layer |
| 21 | ReLU | Rectified linear units |
| 22 | Dropout | 50% of dropout |
| 23 | 8th-level fully connected layer | 4 fully connected layer |
| 24 | Softmax | Softmax |
| 25 | Comprehension output | Crossentropyex with Androsace umbellata (Lour.) Merr., Bidens pilosa L., Trifolium repens L. and Fragaria × ananassa |