# DL-Project

Predicting eye gaze with DL
    Projects that we try: CNN, InceptionTime, EEGNet, DeepEye



## Leonhard Settings

Please use the command

`module load gcc/6.3.0 python_gpu/3.8.5 hdf5/1.10.1`

before training the model. Please make sure that the Tensorflow version should be 2.x.

## model configuration
Please configure the config.py file correctly before running the main.py file  
config['data_dir']    : indicates the directory where you stored the data  
config['model']       : indicates the model you want to use, choose between 'cnn', 'eegnet', 'inception', 'xception' or'deepeye'  
config['downsampled'] : True if you want to use 125 data points per second instead of 500. Default is False  
config['split']       : True if you want to run a clustered version of the model, please keep it to False as the clustered version is inneficient  


## deepEye3 tuning

nb_filter: [32, 64]

depth: [9, 12, 20]

kernel_size:[40, 20]

residual_jump: [3, 4]

Large depth causes overfitting, same for the number of filters. Kernel size seems to have tiny affect on validation. Residual jump for 4 (i.e. `depth % (res_jump) == (res_jump - 1)`) is not so good in our task, but I think it would be useful for future tasks.

The best setting is **nb_filter == 32, depth == 9, kernel_size == 40, res_jump == 3**