mini_imagenet_database = MiniImagenetDatabase()
shape = (84, 84, 3)
latent_dim = 512
mini_imagenet_generator = get_generator(latent_dim)
mini_imagenet_discriminator = get_discriminator()
mini_imagenet_parser = MiniImagenetParser(shape=shape)
experiment_name = prefix+str(labeled_percentage)
# for the SSGAN we need to feed the labels, L, when initializing
gan = GAN(
'mini_imagenet',
image_shape=shape,
latent_dim=latent_dim,
database=mini_imagenet_database,
parser=mini_imagenet_parser,
generator=mini_imagenet_generator,
discriminator=mini_imagenet_discriminator,
visualization_freq=1,
d_learning_rate=0.0003,
g_learning_rate=0.0003,
)
gan.perform_training(epochs=GAN_EPOCHS, checkpoint_freq=50)
gan.load_latest_checkpoint()
print("training GAN is done")
time.sleep(1)
# Split labeled and not labeled
train_folders = mini_imagenet_database.train_folders
keys = list(train_folders.keys())
labeled_keys = np.random.choice(keys, int(len(train_folders.keys())*labeled_percentage), replace=False)
if __name__ == '__main__':
omniglot_database = OmniglotDatabase(random_seed=47,
num_train_classes=1200,
num_val_classes=100)
shape = (28, 28, 1)
latent_dim = 128
omniglot_generator = get_generator(latent_dim)
omniglot_discriminator = get_discriminator()
omniglot_parser = OmniglotParser(shape=shape)
gan = GAN(
'omniglot',
image_shape=shape,
latent_dim=latent_dim,
database=omniglot_database,
parser=omniglot_parser,
generator=omniglot_generator,
discriminator=omniglot_discriminator,
visualization_freq=50,
d_learning_rate=0.0003,
g_learning_rate=0.0003,
)
gan.perform_training(epochs=500, checkpoint_freq=50)
gan.load_latest_checkpoint()
maml_gan = MAMLGAN(gan=gan,
latent_dim=latent_dim,
generated_image_shape=shape,
database=omniglot_database,
network_cls=SimpleModel,
n=5,
k=1,