This is a Tensorflow / Tensorlayer implementation of α-GAN for generating images to be used in EEG & fMRI deep image reconstruction.
α-GAN: Variational Approaches for Auto-Encoding Generative Adversarial Networks
Tensorflow - v1.8.0
Tensorlayer - v1.9.0
The training dataset must first be converted into a .tfrecord format.
This can be done by going to utils.py and modifying class_text_to_int(label) to contain the list of classes, and running convert_tfrecord(data_dir, save_dir, filename). An example is provided at the bottom of utils.py which you can run by executing utils.py.
(data_dir should contain all the folders with the dataset labels, and all the dataset images should be in their respective folder)
Before training the α-GAN, make sure the directory paths in config.py correspond to the dataset locations.
Execute the training by running the following command
python3 main.py
This will train the α-GAN and save the model in checkpoints_dir every epoch.
Generator testing is split into two parts: training set, and generation performance. These two are saved in save_gan_dir and save_test_gan_dir respectively.
This extracts the features from the given folder of images using the trained encoder, and stores them in encoded_feat.pkl.
python3 main.py --mode=encode
This reconstructs the folder of images from the encoding section by using the extracted features from encoded_feat.pkl to generate images.
python3 main.py --mode=gen
python3 main.py --mode=generate