omniglot_database = OmniglotDatabase(random_seed=47,
num_train_classes=1200,
num_val_classes=100)
shape = (28, 28, 1)
latent_dim = 20
omniglot_encoder = get_encoder(latent_dim)
omniglot_decoder = get_decoder(latent_dim)
omniglot_parser = OmniglotParser(shape=shape)
vae = VAE(
'omniglot',
image_shape=shape,
latent_dim=latent_dim,
database=omniglot_database,
parser=omniglot_parser,
encoder=omniglot_encoder,
decoder=omniglot_decoder,
visualization_freq=5,
learning_rate=0.001,
)
vae.perform_training(epochs=1000, checkpoint_freq=100)
vae.load_latest_checkpoint()
# vae.visualize_meta_learning_task()
maml_vae = MAML_VAE(vae=vae,
database=omniglot_database,
latent_algorithm='p1',
network_cls=SimpleModel,
n=5,
k=1,
# import tensorflow as tf
# tf.config.experimental_run_functions_eagerly(True)
voxceleb_database = VoxCelebDatabase()
shape = (16000, 1)
latent_dim = 20
voxceleb_encoder = get_encoder(latent_dim)
voxceleb_decoder = get_decoder(latent_dim)
voxceleb_parser = VoxCelebParser(shape=shape)
vae = VAE(
'voxceleb',
image_shape=shape,
latent_dim=latent_dim,
database=voxceleb_database,
parser=voxceleb_parser,
encoder=voxceleb_encoder,
decoder=voxceleb_decoder,
visualization_freq=1,
learning_rate=0.001,
)
vae.perform_training(epochs=1000, checkpoint_freq=100, vis_callback_cls=AudioCallback)
vae.load_latest_checkpoint()
# vae.visualize_meta_learning_task()
maml_vae = MAML_VAE(
vae=vae,
database=voxceleb_database,
network_cls=SimpleModel,
n=5,
k=1,
# import tensorflow as tf
# tf.config.experimental_run_functions_eagerly(True)
celebalot_database = CelebADatabase()
shape = (84, 84, 3)
latent_dim = 500
celebalot_encoder = get_encoder(latent_dim)
celebalot_decoder = get_decoder(latent_dim)
celebalot_parser = CelebAParser(shape=shape)
vae = VAE(
'celeba',
image_shape=shape,
latent_dim=latent_dim,
database=celebalot_database,
parser=celebalot_parser,
encoder=celebalot_encoder,
decoder=celebalot_decoder,
visualization_freq=1,
learning_rate=0.001,
)
# vae.perform_training(epochs=20, checkpoint_freq=100)
vae.load_latest_checkpoint()
# vae.visualize_meta_learning_task()
maml_vae = MAMLVAECelebA(vae=vae,
latent_algorithm='p3',
database=celebalot_database,
network_cls=MiniImagenetModel,
n=2,
k=1,