Table 1.
Analysis of the CA model
| Layer (type) | Output size | Parameters |
|---|---|---|
| input_1 (Input Layer) | (100, 100, 1) | 0 |
| conv2d_1 (Conv2D) | (100, 100, 32) | 320 |
| max_pooling2d_1 (MaxPooling2) | (50, 50, 32) | 0 |
| conv2d_2 (Conv2D) | (50, 50, 16) | 4624 |
| max_pooling2d_2 (MaxPooling2) | (25, 25, 16) | 0 |
| conv2d_3 (Conv2D) | (25, 25, 8) | 1160 |
| max_pooling2d_3 (MaxPooling2) | (13, 13, 8) | 0 |
| conv2d_4 (Conv2D) | (13, 13, 8) | 584 |
| up_sampling2d_1 (UpSampling2) | (26, 26, 8) | 0 |
| conv2d_5 (Conv2D) | (26, 26, 16) | 1168 |
| up_sampling2d_2 (UpSampling2) | (52, 52, 16) | 0 |
| conv2d_6 (Conv2D) | (50, 50, 32) | 4640 |
| up_sampling2d_3 (UpSampling2) | (100, 100, 32) | 0 |
| conv2d_7 (Conv2D) | (100, 100, 1) | 289 |
| Total learnable parameters: 12,785 | ||