MatthiasQuintern/teng-ml
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

updated readme

Browse Source
This commit is contained in:
matthias@arch 2023-08-30 17:43:59 +02:00
parent 570d6dbc25
commit 3d823a19c7
1 changed files with 9 additions and 5 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

12
readme.md
View File

@ -1,6 +1,11 @@
# Machine learning for material recognition with a TENG # Machine learning for material recognition with a triboelectric nanogenerator (TENG)
(Bi)LSTM for name classification. This project was written for my bachelor's thesis.
More information on the project are [on my website](https://quintern.xyz/en/teng.html).
It was written to classify TENG voltage output from pressing it against different materials.
Contents:
- Data preparation/plotting/loading utilites
- (Bi)LSTM + fully connected + softmax model for name classifiying TENG output
- Progress tracking utilities to easily find the best parameters
## Model training ## Model training
Adjust the parameters in `main.py` and run it. Adjust the parameters in `main.py` and run it.
@ -10,4 +15,3 @@ of the `<model_dir>` that was set in `main.py`.
## Model evaluation ## Model evaluation
Run `find_best_model.py <model_dir>` with the `<model_dir>` specified in `main.py` during training. Run `find_best_model.py <model_dir>` with the `<model_dir>` specified in `main.py` during training.