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README.md

Training GAN

Usage

python main.py

optional arguments:

-h, --help            show this help message and exit
--run RUN             Which operation to run. [train|inference]
--nb_epoch NB_EPOCH   Number of epochs
--batch_size BATCH_SIZE
                      Number of samples per batch.
--nb_batch_per_epoch NB_BATCH_PER_EPOCH
                      Number of batches per epoch
--learning_rate LEARNING_RATE
                      Learning rate used for AdamOptimizer
--noise_dim NOISE_DIM
                      Noise dimension for GAN generation
--random_seed RANDOM_SEED
                      Seed used to initialize rng.
--use_XLA [USE_XLA]   Whether to use XLA compiler.
--nouse_XLA
--num_threads NUM_THREADS
                      Number of threads to fetch the data
--capacity_factor CAPACITY_FACTOR
                      Nuumber of batches to store in queue
--data_format DATA_FORMAT
                      Tensorflow image data format.
--celebA_path CELEBA_PATH
                      Path to celebA images
--channels CHANNELS   Number of channels
--central_fraction CENTRAL_FRACTION
                      Central crop as a fraction of total image
--img_size IMG_SIZE   Image size
--model_dir MODEL_DIR
                      Output folder where checkpoints are dumped.
--log_dir LOG_DIR     Logs for tensorboard.
--fig_dir FIG_DIR     Where to save figures.
--raw_dir RAW_DIR     Where raw data is saved
--data_dir DATA_DIR   Where processed data is saved

Example

python main.py --run train --use_XLA

N.B. At this stage, only data_format = NHWC has been tested. It is recommended to use the XLA option to improve runtime speed.

To Do:

Check validity of both data format and batch norm implementation