Figure 1.
Figure 2.
Figure 3.
General structure of the TIM network, each row is a layer_ Total number of trainable parameters is 19,762_
| Layer type | Number of neurons | Number of parameters | Activation |
|---|---|---|---|
| InputLayer | 4 | 0 | None |
| Dense | 64 | 320 | tanh |
| Dense | 64 | 4160 | relu |
| Dense | 64 | 4160 | relu |
| Dense | 64 | 4160 | relu |
| Dense | 32 | 2080 | relu |
| Dense | 32 | 1056 | relu |
| Dense | 32 | 1056 | relu |
| Dense | 32 | 1056 | relu |
| Dense | 16 | 528 | relu |
| Dense | 16 | 272 | relu |
| Dense | 16 | 272 | relu |
| Dense | 16 | 272 | relu |
| Dense | 8 | 136 | relu |
| Dense | 8 | 72 | relu |
| Dense | 8 | 72 | relu |
| Dense | 8 | 72 | relu |
| Dense | 2 | 18 | sigmoid |