A simple convolutional auto-encoder implemented using Keras and Tensorflow
Usage: "python main.py <input_dir> [option 1] [option 2]..."
Run "python main.py --help" for a detailed help message:
usage: main.py [-h] [--compression_level COMPRESSION_LEVEL] [--n_epochs N_EPOCHS] [--batch_size BATCH_SIZE] [--train_ratio TRAIN_RATIO] [--mode {visualize,train,encode,decode}] input_folder
Input, network configuration and other settings of the auto-encoder
positional arguments: input_folder Input folder for the network, should contain color images of size 64x64
optional arguments: -h, --help show this help message and exit --compression_level COMPRESSION_LEVEL How much to compress the image (a fraction of the original size, eg. 0.05) --n_epochs N_EPOCHS The number of epochs in the training stage of the network --batch_size BATCH_SIZE The batch size in the training stage of the network --train_ratio TRAIN_RATIO The train ratio out of the entire data set --mode {visualize,train,encode,decode} Mode. Can be 'visualize', 'train', 'encode' or 'decode'. 'visualize' is used to assess the performance of the network, 'train' to train the network, and 'encode' and 'decode' are used to encode the images in the input folder and decode them, respectively