ECON-T autoencoder model
Following files illustrates prototypes of different autoencoder architectures
auto.ipynb - 1D deep NN autoencoder demo
auto_CNN.ipynb - 2D CNN autoencoder demo
Auto_qCNN.ipynb - 2D quantized CNN autoencoder, trained with qKeras demo
qkeras instructions: https://github.com/google/qkeras
Scripts to explore hyperparameters choices:
models.py - constructs and compile simple model architectures
denseCNN.py - model class for constructing conv2D-dense architectures
train.py - train(or load weights) and evaluate models
## edit parameters setting inside train.py
## train with 1 epoch to make sure model parameters are OK, output to a trainning folder
python train.py -i ~/eos/ecoder/pgun_pid1_pt200_200PU.csv -o ./qjet_200PU/ --epoch 1
## train the weights with max 150 epoch
python train.py -i ~/eos/ecoder/pgun_pid1_pt200_200PU.csv -o ./qjet_200PU/ --epoch 150