model robustness testing

This commit is contained in:
Joseph Hopfmüller
2025-01-10 23:40:54 +01:00
parent 3af73343c1
commit f38d0ca3bb
13 changed files with 1558 additions and 334 deletions

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# models
## no polarisation flipping
```py
config_path="data/20241229-163*-128-16384-50000-*.ini"
model=".models/best_20241230_011907.tar"
```
```py
config_path="data/20241229-163*-128-16384-80000-*.ini"
model=".models/best_20241230_103752.tar"
```
```py
config_path="data/20241229-163*-128-16384-100000-*.ini"
model=".models/best_20241230_164534.tar"
```
## with polarisation flipping
polarisation flipping: signal is randomly rotated by 180°. polarization rotation can be detected by adding a tone on one of the polarisations, but only to mod 180° with a direct detection setup. the randomly flipped signal should allow the network to hopefully learn to compensate for dispersion, pmd independently from the polarization rot. the training data includes the flipped signal as well, but no indication if the polarisation is flipped.
```py
config_path="data/20241229-163*-128-16384-50000-*.ini"
model=".models/best_20241231_000328.tar"
```
```py
config_path="data/20241229-163*-128-16384-80000-*.ini"
model=".models/best_20241231_163614.tar"
```
```py
config_path="data/20241229-163*-128-16384-100000-*.ini"
model=".models/best_20241231_170532.tar"
```