ScanGAN

Convenient GAN implementation in Keras

This is a WIP

Basic Usage

from GAN import GAN

# 128x128, 3 channels

shape = (128, 128, 3)
model = GAN(shape)

model.train(x, y, epochs=100)

pred = model.generate(x[0])

Settings

You may construct the model with some custom hyperparameters by passing in a settings object at construction.

from keras.optimizers import Adagrad

shape = (64, 64, 1) # 64x64 grayscal images

my_settings = {
	'input_mask':True,
	'g_ksize': 3,
	'd_optimizer': Adagrad()
}

model = GAN(shape, settings=my_settings)

Key	Type	Default Value	Description
input_mask	boolean	False	If set to True, the model with replace the output of the generator with the input where the input != 0. The masked output is used when computing loss
d_loss_target	0.3	float between 0 .. 1	Each epoch, the discriminator will be trained if its loss is less than the target, the generator will be trained otherwise
g_optimizer	str, keras Optimizer, or lambda	Adam(1e-3)	The optimizer that will be used for the Generator.
g_ksize	int	5	Convolution kernel size for the generator (g_ksize * g_ksize)
g_depth	int	64	Output depth of each hidden layer of the generator
g_activation	str or lambda	lambda: LeakyReLU()	Activation function to use for the generator
g_regularizer	str of lambda	None	Regularization to use for the generator
d_optimizer	str, keras Optimizer, or lambda	SGD()	The optimizer that will be used for the Discriminator.
d_ksize	int	5	Convolution kernel size for the discriminator (d_ksize * d_ksize)
d_depth	int	32	Output depth of each hidden layer of the discriminator
d_activation	str or lambda	lambda: LeakyReLU()	Activation function to use for the discriminator
d_output_activation	str or lambda	'sigmoid'	Activation function to use for output layer the discriminator
d_regularizer	str of lambda	None	Regularization to use for the discriminator

• lambda settings values should always return a new instance

Sample Output (inpainting)

• Training set was ~400 128x128 RGB images of landscapes
• Default settings
• epochs = 100

Input / Output / Original