The main aim of this project is to speed up a process of building deep learning pipelines that are based on Generative Adversarial Networks and simplify prototyping of various generator/discriminator models. This library provides several GAN trainers that can be used as off-the-shelf features such us:
- Vanilla GAN
- Conditional GAN
- Cycle GAN
- Wasserstein GAN
- Progressive GAN (WIP)
| Vanilla GAN (Gaussian function) | Vanilla GAN (sigmoid function) |
|---|---|
 |
 |
| Vanilla GAN (MNIST) | Conditional GAN (MNIST) |
|---|---|
 |
 |
| Vanilla GAN (FASHION_MNIST) | Conditional GAN (FASHION_MNIST) |
|---|---|
 |
 |
| Vanilla GAN (CIFAR10) | Conditional GAN (CIFAR10) |
|---|---|
 |
 |
| Cycle GAN (SUMMER2WINTER) | Cycle GAN (WINTER2SUMMER) |
|---|---|
 |
 |
pip install gans2[tensorflow_gpu]pip install gans2[tensorflow]import tensorflow as tf
from easydict import EasyDict as edict
from gans.datasets import mnist
from gans.models.discriminators import discriminator
from gans.models.generators.latent_to_image import latent_to_image
from gans.trainers import optimizers
from gans.trainers import vanilla_gan_trainer
model_parameters = edict({
'img_height': 28,
'img_width': 28,
'num_channels': 1,
'batch_size': 16,
'num_epochs': 10,
'buffer_size': 1000,
'latent_size': 100,
'learning_rate_generator': 0.0001,
'learning_rate_discriminator': 0.0001,
'save_images_every_n_steps': 10
})
dataset = mnist.MnistDataset(model_parameters)
generator = latent_to_image.LatentToImageGenerator(model_parameters)
discriminator = discriminator.Discriminator(model_parameters)
generator_optimizer = optimizers.Adam(
learning_rate=model_parameters.learning_rate_generator,
beta_1=0.5,
)
discriminator_optimizer = optimizers.Adam(
learning_rate=model_parameters.learning_rate_discriminator,
beta_1=0.5,
)
gan_trainer = vanilla_gan_trainer.VanillaGANTrainer(
batch_size=model_parameters.batch_size,
generator=generator,
discriminator=discriminator,
training_name='VANILLA_GAN_MNIST',
generator_optimizer=generator_optimizer,
discriminator_optimizer=discriminator_optimizer,
latent_size=model_parameters.latent_size,
continue_training=False,
save_images_every_n_steps=model_parameters.save_images_every_n_steps,
visualization_type='image',
)
gan_trainer.train(
dataset=dataset,
num_epochs=model_parameters.num_epochs,
)import tensorflow as tf
from easydict import EasyDict as edict
from tensorflow.python import keras
from tensorflow.python.keras import layers
from gans.datasets import mnist
from gans.models import sequential
from gans.trainers import optimizers
from gans.trainers import vanilla_gan_trainer
model_parameters = edict({
'img_height': 28,
'img_width': 28,
'num_channels': 1,
'batch_size': 16,
'num_epochs': 10,
'buffer_size': 1000,
'latent_size': 100,
'learning_rate_generator': 0.0001,
'learning_rate_discriminator': 0.0001,
'save_images_every_n_steps': 10
})
dataset = mnist.MnistDataset(model_parameters)
generator = sequential.SequentialModel(
layers=[
keras.Input(shape=[model_parameters.latent_size]),
layers.Dense(units=7 * 7 * 256, use_bias=False),
layers.BatchNormalization(),
layers.LeakyReLU(),
layers.Reshape((7, 7, 256)),
layers.Conv2DTranspose(128, (5, 5), strides=(1, 1), padding='same', use_bias=False),
layers.BatchNormalization(),
layers.LeakyReLU(),
layers.Conv2DTranspose(64, (5, 5), strides=(2, 2), padding='same', use_bias=False),
layers.BatchNormalization(),
layers.LeakyReLU(),
layers.Conv2DTranspose(1, (5, 5), strides=(2, 2), padding='same', use_bias=False, activation='tanh')
]
)
discriminator = sequential.SequentialModel(
[
keras.Input(
shape=[
model_parameters.img_height,
model_parameters.img_width,
model_parameters.num_channels,
]),
layers.Conv2D(filters=64, kernel_size=(5, 5), strides=(2, 2), padding='same'),
layers.LeakyReLU(),
layers.Dropout(0.3),
layers.Conv2D(filters=128, kernel_size=(5, 5), strides=(2, 2), padding='same'),
layers.LeakyReLU(),
layers.Dropout(rate=0.3),
layers.Flatten(),
layers.Dense(units=1),
]
)
generator_optimizer = optimizers.Adam(
learning_rate=model_parameters.learning_rate_generator,
beta_1=0.5,
)
discriminator_optimizer = optimizers.Adam(
learning_rate=model_parameters.learning_rate_discriminator,
beta_1=0.5,
)
gan_trainer = vanilla_gan_trainer.VanillaGANTrainer(
batch_size=model_parameters.batch_size,
generator=generator,
discriminator=discriminator,
training_name='VANILLA_GAN_MNIST_CUSTOM_MODELS',
generator_optimizer=generator_optimizer,
discriminator_optimizer=discriminator_optimizer,
latent_size=model_parameters.latent_size,
continue_training=False,
save_images_every_n_steps=model_parameters.save_images_every_n_steps,
visualization_type='image',
)
gan_trainer.train(
dataset=dataset,
num_epochs=model_parameters.num_epochs,
)Vanilla GAN for Gaussian function modeling
Vanilla GAN for sigmoid function modeling
Conditional GAN for MNIST digit generation
Cycle GAN for summer2winter/winter2summer style transfer
Wasserstein GAN for MNIST digit generatio
In order to visualize a training process (loss values, generated outputs) run the following command in the project directory:
tensorboard --logdir outputsTo follow the training process go to the default browser and type the following address http://your-workstation-name:6006/
The below picture presents the TensorBoard view lunched for two experiments: VANILLA_MNIST and VANILLA_FASION_MNIST.
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