A neural network model that can approximate any non-linear function by using the random search algorithm for the optimization of the loss function.
If you are coming from the article on Medium, keep in mind that the code is still partially incomplete and, most importantly, not fully documented, but you can figure things out.
| experiments | target_function | S | networks | epochs | layers_sizes | STEP_SIZE | raw mean loss | mean loss % |
|---|---|---|---|---|---|---|---|---|
| 5 | sin(x) | 300 | 16 | 25_000 | 32; 3 | 1e-4 | 0.0022145095302255496 | 0.221% |
| 5 | sin(x) | 300 | 16 | 25_000 | 8; 10 | 1e-4 | 0.5271807633104308 | 52.718% |
| 5 | sin(x) | 300 | 16 | 25_000 | 16; 5 | 1e-5 | 0.5271807632693248 | 52.718% |
| 5 | sin(x) | 300 | 16 | 25_000 | 16; 4 | 1e-4 | 0.06721093900301409 | 6.721% |
| 5 | sin(x) | 300 | 16 | 25_000 | 16; 3 | 1e-5 | 0.52699025347444 | 52.699% |
| 5 | sin(x) | 300 | 128 | 25_000 | 32; 3 | 1e-4 | 0.00012897642800016362 | 0.013% |
| 5 | sin(x) | 300 | 32 | 25_000 | 32; 4 | 1e-4 | 0.0009437770169172481 | 0.094% |
| 5 | x^3 - 2x^2 - 6x | 300 | 32 | 25_000 | 32; 4 | 1e-4 | 1390.5025496471226 | 139050.255% |
| 5 | sin(x) | 300 | 128 | 25_000 | 8; 6 | 1e-7 | 0.5271807632690662 | 52.718% |
| 5 | cos(x) | 300 | 32 | 25_000 | 32; 3 | 1e-4 | 0.006531106146449706 | 0.653% |