GANs are a very hot topic in the Deep Learning world! The goal of this implementation is to have a simple and fun demo, of a simple GAN that you can train in a few minutes but still getting some cool results!
Want to learn more about GANs ?
The code is basically the same available here by @wiseodd, that has a great repo with a lot of GAN implementations and a great blog post about this vanilla implementation.
I've changed this code mainly in order to make it simpler for beginners to get started with GANs and TensorFlow.
I've also implemented other features as:
- TensorBoard visualization for the discriminator and generator losses;
- Downloading the fashion mnist and classic mnist datasets automatically;
- Added comments and refactored code to make it simpler.
Looking for better generated samples? Here is a code for a DCGAN by @carpedm20. It will take a lot more time to train, but it will generate better results.
Run fashion MNIST:
python gan.pyrun classic MNIST:
python gan.py --mnist=mnistThere are some arguments you can play with, to check all of them run:
python gan.py -hYou'll see something like:
usage: gan.py [-h] [--output_path OUTPUT_PATH] [--input_path INPUT_PATH]
[--log_path LOG_PATH] [--mnist MNIST] [--z_dim Z_DIM]
[--batch_size BATCH_SIZE] [--train_steps TRAIN_STEPS]
optional arguments:
-h, --help show this help message and exit
--output_path OUTPUT_PATH
Output path for the generated images.
--input_path INPUT_PATH
Input path for the fashion mnist.If not available data
will be downloaded.
--log_path LOG_PATH Log path for tensorboard.
--mnist MNIST Choose to use "fashion" (fashion-mnist) or "mnist"
(classic mnist) dataset.
--z_dim Z_DIM Output path for the generated images.
--batch_size BATCH_SIZE
Batch size used for training.
--train_steps TRAIN_STEPS
Number of steps used for training.
# tensorboard_log is the default path to tensorboard logs
tensorboard --logdir=tensorboad_log
# a more general command is
tensorboard --logdir=<the value of --log_path>
