| ID . | Type . | Size . | n . | Activation . | Param count . |
|---|---|---|---|---|---|
| (1) . | (2) . | (3) . | (4) . | (5) . | (6) . |
| 1 | Input | 64 |$\times$| 64 | – | – | – |
| 2 | Convolutional | 11 |$\times$| 11 | 64 | ReLU | 23 296 |
| 3 | Max pooling | 2 |$\times$| 2 | – | – | – |
| 4 | Convolutional | 7 |$\times$| 7 | 128 | ReLU | 401 536 |
| 5 | Dropout (0.2) | – | – | – | – |
| 6 | Convolutional | 5 |$\times$| 5 | 128 | ReLU | 409 728 |
| 7 | Max pooling | 2 |$\times$| 2 | – | – | – |
| 8 | Convolutional | 5 |$\times$| 5 | 256 | ReLU | 819 456 |
| 9 | Dropout (0.2) | – | – | – | – |
| 10 | Convolutional | 3 |$\times$| 3 | 256 | ReLU | 590 080 |
| 11 | Max pooling | 2 |$\times$| 2 | – | – | – |
| 12 | Fully connected | 1024 | – | ReLU | 2360 320 |
| 13 | Dropout (0.2) | – | – | – | – |
| 14 | fully connected | 1024 | – | ReLU | 1049 600 |
| 15 | Dropout (0.2) | – | – | – | – |
| 16 | Fully connected | 512 | – | ReLU | 524 800 |
| 17 | Dropout (0.2) | – | – | – | – |
| 18 | Fully connected | 512 | – | ReLU | 262 656 |
| 19 | Fully connected | 1 | – | Sigmoid | 513 |
| Total params | 6441 985 | ||||
| Trainable params | 6441 985 | ||||
| Nontrainable params | 0 | ||||
| ID . | Type . | Size . | n . | Activation . | Param count . |
|---|---|---|---|---|---|
| (1) . | (2) . | (3) . | (4) . | (5) . | (6) . |
| 1 | Input | 64 |$\times$| 64 | – | – | – |
| 2 | Convolutional | 11 |$\times$| 11 | 64 | ReLU | 23 296 |
| 3 | Max pooling | 2 |$\times$| 2 | – | – | – |
| 4 | Convolutional | 7 |$\times$| 7 | 128 | ReLU | 401 536 |
| 5 | Dropout (0.2) | – | – | – | – |
| 6 | Convolutional | 5 |$\times$| 5 | 128 | ReLU | 409 728 |
| 7 | Max pooling | 2 |$\times$| 2 | – | – | – |
| 8 | Convolutional | 5 |$\times$| 5 | 256 | ReLU | 819 456 |
| 9 | Dropout (0.2) | – | – | – | – |
| 10 | Convolutional | 3 |$\times$| 3 | 256 | ReLU | 590 080 |
| 11 | Max pooling | 2 |$\times$| 2 | – | – | – |
| 12 | Fully connected | 1024 | – | ReLU | 2360 320 |
| 13 | Dropout (0.2) | – | – | – | – |
| 14 | fully connected | 1024 | – | ReLU | 1049 600 |
| 15 | Dropout (0.2) | – | – | – | – |
| 16 | Fully connected | 512 | – | ReLU | 524 800 |
| 17 | Dropout (0.2) | – | – | – | – |
| 18 | Fully connected | 512 | – | ReLU | 262 656 |
| 19 | Fully connected | 1 | – | Sigmoid | 513 |
| Total params | 6441 985 | ||||
| Trainable params | 6441 985 | ||||
| Nontrainable params | 0 | ||||
Notes. (1) ID of the layers. (2) Type of the layers. (3) Size of the data or the filters. (4) Number of the filters. (5) Activation function adopted in the layers. (6) Trainable parameters.
| ID . | Type . | Size . | n . | Activation . | Param count . |
|---|---|---|---|---|---|
| (1) . | (2) . | (3) . | (4) . | (5) . | (6) . |
| 1 | Input | 64 |$\times$| 64 | – | – | – |
| 2 | Convolutional | 11 |$\times$| 11 | 64 | ReLU | 23 296 |
| 3 | Max pooling | 2 |$\times$| 2 | – | – | – |
| 4 | Convolutional | 7 |$\times$| 7 | 128 | ReLU | 401 536 |
| 5 | Dropout (0.2) | – | – | – | – |
| 6 | Convolutional | 5 |$\times$| 5 | 128 | ReLU | 409 728 |
| 7 | Max pooling | 2 |$\times$| 2 | – | – | – |
| 8 | Convolutional | 5 |$\times$| 5 | 256 | ReLU | 819 456 |
| 9 | Dropout (0.2) | – | – | – | – |
| 10 | Convolutional | 3 |$\times$| 3 | 256 | ReLU | 590 080 |
| 11 | Max pooling | 2 |$\times$| 2 | – | – | – |
| 12 | Fully connected | 1024 | – | ReLU | 2360 320 |
| 13 | Dropout (0.2) | – | – | – | – |
| 14 | fully connected | 1024 | – | ReLU | 1049 600 |
| 15 | Dropout (0.2) | – | – | – | – |
| 16 | Fully connected | 512 | – | ReLU | 524 800 |
| 17 | Dropout (0.2) | – | – | – | – |
| 18 | Fully connected | 512 | – | ReLU | 262 656 |
| 19 | Fully connected | 1 | – | Sigmoid | 513 |
| Total params | 6441 985 | ||||
| Trainable params | 6441 985 | ||||
| Nontrainable params | 0 | ||||
| ID . | Type . | Size . | n . | Activation . | Param count . |
|---|---|---|---|---|---|
| (1) . | (2) . | (3) . | (4) . | (5) . | (6) . |
| 1 | Input | 64 |$\times$| 64 | – | – | – |
| 2 | Convolutional | 11 |$\times$| 11 | 64 | ReLU | 23 296 |
| 3 | Max pooling | 2 |$\times$| 2 | – | – | – |
| 4 | Convolutional | 7 |$\times$| 7 | 128 | ReLU | 401 536 |
| 5 | Dropout (0.2) | – | – | – | – |
| 6 | Convolutional | 5 |$\times$| 5 | 128 | ReLU | 409 728 |
| 7 | Max pooling | 2 |$\times$| 2 | – | – | – |
| 8 | Convolutional | 5 |$\times$| 5 | 256 | ReLU | 819 456 |
| 9 | Dropout (0.2) | – | – | – | – |
| 10 | Convolutional | 3 |$\times$| 3 | 256 | ReLU | 590 080 |
| 11 | Max pooling | 2 |$\times$| 2 | – | – | – |
| 12 | Fully connected | 1024 | – | ReLU | 2360 320 |
| 13 | Dropout (0.2) | – | – | – | – |
| 14 | fully connected | 1024 | – | ReLU | 1049 600 |
| 15 | Dropout (0.2) | – | – | – | – |
| 16 | Fully connected | 512 | – | ReLU | 524 800 |
| 17 | Dropout (0.2) | – | – | – | – |
| 18 | Fully connected | 512 | – | ReLU | 262 656 |
| 19 | Fully connected | 1 | – | Sigmoid | 513 |
| Total params | 6441 985 | ||||
| Trainable params | 6441 985 | ||||
| Nontrainable params | 0 | ||||
Notes. (1) ID of the layers. (2) Type of the layers. (3) Size of the data or the filters. (4) Number of the filters. (5) Activation function adopted in the layers. (6) Trainable parameters.
This PDF is available to Subscribers Only
View Article Abstract & Purchase OptionsFor full access to this pdf, sign in to an existing account, or purchase an annual subscription.
