Table 4.
Optimal hyper-parameters of the proposed CNN models
| Value | ||||
|---|---|---|---|---|
| Hyper-parameters | Model 1 (Gender) | Model 2 (Age) | Model 3 (Both) | |
| 1 | Number of Convoluiton layer | 7 | 6 | 2 |
| 2 | Number of Maxpooling layer | 7 | 6 | 2 |
| 3 | Number of FC layers | 2 | 2 | 2 |
| 4 | Number of filters [Conv1, Pool1, Conv2, Pool2, Conv3, Pool3, Conv4, Pool4, Conv 5, Pool 5, Conv 6, Pool6, Conv7, Pool7…] | [48, 48, 24, 24, 24, 24, 24, 24, 16 16,16, 16, 16, 16] | [48, 48, 32, 32, 24, 24, 24, 24, 16, 16, 16, 16] | [48, 48, 24, 24] |
| 5 | Filter sizes [Conv1, Pool1, Conv2, Pool2, Conv3, Pool3, Conv4, Pool4, Conv 5, Pool 5, Conv 6, Pool6, Conv7, Pool7 …] | [3, 4, 3, 3, 5, 4, 5, 5, 3, 3, 3, 3, 5, 4] | [4, 3, 4, 3, 5, 4, 3, 5, 4, 4, 4, 4] | [2, 4, 5, 4] |
| 6 | Padding [Conv1, Pool1, Conv2, Pool2, Conv3, Pool3, Conv4, Pool4, Conv 5, Pool 5, Conv 6, Pool6, Conv7, Pool7 …] | [0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1] | [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0] | [0, same, 0, 0] |
| 7 | Stride [Conv1, Pool1, Conv2, Pool2, Conv3, Pool3, Conv4, Pool4, Conv 5, Pool 5, Conv 6, Pool6, Conv7, Pool7 …] | [1, 1, 2, 1, 1, 1, 1, 1, 2, 2, 2, 1, 2, 1] | [1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2] | [2, 2, 2, 2] |
| 8 | L2 regularization | 0.0001 | 0.0001 | 0.0001 |
| 9 | Momentum | 0.9000 | 0.9000 | 0.9000 |
| 10 | Mini-batch size | 32 | 32 | 32 |
| 11 | Learning rate | 0.0001 | 0.0001 | 0.0002 |
| 12 | Activation function | ReLu | ReLu | ReLu |