Synthetic Data Generation

User friendly library to generate datasets using GANs and VAEs.

Paper supporting this library: Synthetic Data Generation To Better Perceive Classifier Predictions

Installation

Use git to install generate_datasets.

git clone https://github.com/Mr-Vicente/generate_datasets.git

Usage

GAN

from generate_datasets import data_access
from generate_datasets.WGAN_GP.wgan_gp_class_generic import WGAN_GP

EPOCHS = 500
OUTPUT_DIR = "imgs_gen"
params = {
    'lr': 0.0001,
    'beta1': 0,
    'batch_size': 64,
    'latent_dim': 128,
    'image_size': 152
}

class_names = ['Blond-yellow','Yellow','Orange','Orange-Brown','Blond',
                'Light-Brown','Brown','Black','Gray','White']


Number_Dataset_Classes = len(class_names)

gan = WGAN_GP(params)
gan.load_dataset(data_access.prepare_data('gan'),Number_Dataset_Classes)
gan.train_model(EPOCHS)
gan.generate_images(10,OUTPUT_DIR,class_names)

If no classifier is provided to GANs, the gan model will behave like a normal gan. The interest of one providing a classifier to our GAN model might be to better understand how it is operating, or even to generate images of a specific class.

VAE

from generate_datasets import data_access
from generate_datasets.VAE.generic_vannila_vae import VAE
from generate_datasets.Processing import process_cartoon

EPOCHS = 500
n_images = 10
OUTPUT_DIR = "imgs_gen"

params = {
    'lr': 0.0001,
    'beta1': 0,
    'batch_size': 64,
    'latent_dim': 128,
    'image_size': 152
}

class_names = ['Blond-yellow','Yellow','Orange','Orange-Brown','Blond',
                'Light-Brown','Brown','Black','Gray','White']

def load_cartoon_data():
    images, labels = process_cartoon.decode_data_cartoon()
    return images, labels

tf.keras.backend.clear_session()

model = VAE()
model.load_dataset(data_access.prepare_dataset('vae',load_cartoon_data(),image_size=(128,128)))
model.train_model(epochs)
model.generate_images(n_images,OUTPUT_DIR)

Library Info

This library has several generative models at your despose:

GAN (Generative Adversarial Network)
- Status: Working
- Paper: GAN
- Official Implementation: None
VAE (Variational Autoencoder)
- Status: Working
- Paper: VAE
- Official Implementation: None
WGAN (Wasserstein GAN)
- Status: Working
- Paper: WGAN
- Official Implementation: None
WGAN-GP (Wasserstein GAN with Gradient Penalty)
- Status: Working
- Paper: WGAN-GP
- Official Implementation: None, but check this one WGAN-GP Implementation
PGGAN (Progressive Growing GAN)
- Status: Not working
- Paper: PGGAN
- Official Implementation: PGGAN Implementation
IntroVAE (Introspective Variational Autoencoder)
- Status: Not giving proper results
- Paper: IntroVAE
- Official Implementation: None