Keras GAN Library
Keras implementation of GANs
This library provides some simple infrastructure to define and train Generative Adversarial Networks in Keras. It can also be used from the command line.
The simplest way to install is:
pip install keragan
Some Images produced by keragan
|Minimalistic Landcape, 2020||Spring Dawn, 2020||Water Still 2, 2020|
Training GAN from the Command-Line
To start training, you can use the following command-line:
python -m keragan.trainer c:\dataset --size 1024 --model_path .\models --samples_path .\samples --latent_dim 100 --epochs 1000
You can find out more about other parameters by calling the program with
Important things to note:
--sizemust be power of 2, suitable values are 64, 128, 256, 512 and 1024. Higher resoltions are likely not to give good results.
- You can use
--lrto set learning rate, default value is 0.001. Smaller learning rates yield better results, but may significantly increase training time.
Once you have trained the model, you can use the generator model to produce new random images. To do that from a command line, you can use the following:
python -m keragan.generate --file ./models/gen_1100.hdf5 --out ./samples --n 100
--help option to find out more about different options.
The library is structured around few core classes:
GANis used to represent a GAN, with
discriminatorfields that define corresponding networks.
GANitself is abstract, and any subclass should define
create_discriminator()functions. This class is also responsible for loading/saving networks to disk, and it can also generate sample images using
DCGANis currently the only subclass, implementing Deep Convolutional GAN.
ImageDatasetis a class defining the process of loading initial dataset from disk, resizing it to specified size, filtering out bad images, etc.
GANTraineris responsible to training a GAN, i.e. running epoch loop and periodically storing samples and network weights to disk.
The actual training code looks like this:
gan = keragan.DCGAN(args) imsrc = keragan.ImageDataset(args) imsrc.load() if args.sample_images: imsrc.sample_images() train = keragan.GANTrainer(image_dataset=imsrc,gan=gan,args=args) def callbk(tr): if args.visual_inspection_interval and tr.gan.epoch % args.visual_inspection_interval == 0: res = tr.gan.sample_images(n=2) fig,ax = plt.subplots(1,len(res)) for i,v in enumerate(res): ax[i].imshow(v) plt.show() train.train(callbk)
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