https://github.com/fastmachinelearning/keras-training
jet classification and regression training in keras
https://github.com/fastmachinelearning/keras-training
Last synced: 3 months ago
JSON representation
jet classification and regression training in keras
- Host: GitHub
- URL: https://github.com/fastmachinelearning/keras-training
- Owner: fastmachinelearning
- License: gpl-3.0
- Created: 2017-11-15T23:04:49.000Z (over 7 years ago)
- Default Branch: master
- Last Pushed: 2022-06-23T03:20:12.000Z (about 3 years ago)
- Last Synced: 2025-03-27T15:52:15.871Z (3 months ago)
- Language: Python
- Size: 1.37 MB
- Stars: 9
- Watchers: 9
- Forks: 15
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# keras-training
## Installation
Install `miniconda2` by sourcing `install_miniconda.sh` in your home directory. Log out and log back in after this.
```bash
cp install_miniconda.sh ~/
cd ~
source install_miniconda.sh
```
Install the rest of the dependencies:
```bash
cd ~/keras-training
source install.sh
```
Each time you log in set things up:
```bash
source setup.sh
```
## Conversion of data
All of the data ntuple files are available here: https://cernbox.cern.ch/index.php/s/AgzB93y3ac0yuId
To add the truth values and flatten the trees (you can skip this step)
```bash
cd ~/keras-training/convert
python addTruth.py -t t_allpar \
../data/processed-pythia82-lhc13-*-pt1-50k-r1_h022_e0175_t220_nonu.root
```
To `hadd` these files and convert from `TTree` to `numpy array` with
random shuffling (you can skip this step)
```bash
hadd -f \
../data/processed-pythia82-lhc13-all-pt1-50k-r1_h022_e0175_t220_nonu_truth.root \
../data/processed-pythia82-lhc13-*-pt1-50k-r1_h022_e0175_t220_nonu_truth.root
python convert.py -t t_allpar_new \
../data/processed-pythia82-lhc13-all-pt1-50k-r1_h022_e0175_t220_nonu_truth.root
```
You can also copy this file directly from: https://cernbox.cern.ch/index.php/s/jvFd5MoWhGs1l5v
## Training and evaluation
To run a simple training with L1 regularization (lamba = 1e-4):
```bash
cd ~/keras-training/train
python train.py -t t_allpar_new \
-i ../data/processed-pythia82-lhc13-all-pt1-50k-r1_h022_e0175_t220_nonu_truth.z \
-c train_config_threelayer.yml \
-o train_simple_l10p0001/
```
and evaluate the training:
```bash
python eval.py -t t_allpar_new \
-i ../data/processed-pythia82-lhc13-all-pt1-50k-r1_h022_e0175_t220_nonu_truth.z \
-m train_simple/KERAS_check_best_model.h5 \
-c train_config_threelayer.yml \
-o eval_simple_l10p0001/
```
## Pruning and retraining
To prune the trained model by removing weights below a certain
percentile (relative weight percentile < 32.7%):
```bash
mkdir -p prune_simple_l10p0001_33perc
python prune.py -m train_simple_l10p0001/KERAS_check_best_model.h5 \
-o prune_simple_l10p0001_33perc/trained_model_33perc.h5 \
--relative-weight-percentile 32.7
```
and evaluate the pruned model:
```bash
python eval.py -t t_allpar_new \
-i ../data/processed-pythia82-lhc13-all-pt1-50k-r1_h022_e0175_t220_nonu_truth.z \
-m prune_simple_l10p0001_33perc/pruned_model.h5 \
-c train_config_threelayer.yml \
-o eval_simple_l10p0001_33perc/
```
To retrain the pruned model (keeping the pruned weights fixed to 0)
with L1 regularization (lamba = 1e-4):
```bash
python retrain.py -t t_allpar_new \
-i ../data/processed-pythia82-lhc13-all-pt1-50k-r1_h022_e0175_t220_nonu_truth.z \
-o retrain_simple_l10p0001_33perc \
-m prune_simple_l10p0001_33perc/pruned_model.h5 \
-c train_config_threelayer.yml \
-d prune_simple_l10p0001_33perc/pruned_model_drop_weights.h5
```
and prune again (this time 48 percent of the weights):
```bash
mkdir -p prune_simple_l10p0001_48perc
python prune.py -m retrain_simple_l10p0001_33perc/KERAS_check_best_model.h5 \
--relative-weight-percentile 47.5 \
-o prune_simple_l10p0001_48perc/pruned_model.h5
```
and evalute the pruned model (2nd iteration):
```bash
python eval.py -t t_allpar_new \
-i ../data/processed-pythia82-lhc13-all-pt1-50k-r1_h022_e0175_t220_nonu_truth.z \
-m prune_simple_l10p0001_48perc/pruned_model.h5 \
-c train_config_threelayer.yml \
-o eval_simple_l10p0001_48perc/
```
This procedure can be repeated as done in [train/train_prune_eval_retrain.sh](train/train_prune_eval_retrain.sh).