# vae.perform_training(epochs=20, checkpoint_freq=100)
vae.load_latest_checkpoint()
# vae.visualize_meta_learning_task()
maml_vae = MAML_VAE(vae=vae,
latent_algorithm='p1',
database=mini_imagenet_database,
network_cls=MiniImagenetModel,
n=5,
k_ml=1,
k_val_ml=5,
k_val=1,
k_val_val=15,
k_test=1,
k_val_test=15,
meta_batch_size=4,
num_steps_ml=5,
lr_inner_ml=0.05,
num_steps_validation=5,
save_after_iterations=1000,
meta_learning_rate=0.001,
report_validation_frequency=200,
log_train_images_after_iteration=200,
num_tasks_val=100,
clip_gradients=True,
experiment_name='mini_imagenet_crop_random_uniform',
val_seed=42,
val_test_batch_norm_momentum=0.0)
maml_vae.visualize_meta_learning_task(shape, num_tasks_to_visualize=2)
maml_vae.train(iterations=8000)
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,
k_val_ml=5,
k_val_val=15,
k_val_test=15,
k_test=5,
meta_batch_size=4,
num_steps_ml=5,
lr_inner_ml=0.4,
num_steps_validation=5,
save_after_iterations=1000,
meta_learning_rate=0.001,
report_validation_frequency=200,
log_train_images_after_iteration=200,
number_of_tasks_val=100,
number_of_tasks_test=1000,
clip_gradients=False,
experiment_name='voxceleb_std_1.0',
val_seed=42,
val_test_batch_norm_momentum=0.0
)
maml_vae.visualize_meta_learning_task(shape, num_tasks_to_visualize=2)
vae.perform_training(epochs=500, 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=20,
k_ml=1,
k_val_ml=1,
k_val=1,
k_val_val=1,
k_test=1,
k_val_test=1,
meta_batch_size=4,
num_steps_ml=5,
lr_inner_ml=0.4,
num_steps_validation=5,
save_after_iterations=1000,
meta_learning_rate=0.001,
report_validation_frequency=200,
log_train_images_after_iteration=200,
num_tasks_val=100,
clip_gradients=False,
experiment_name='omniglot_vae_0.5_shift',
val_seed=42,
val_test_batch_norm_momentum=0.0)
# maml_vae.visualize_meta_learning_task(shape, num_tasks_to_visualize=2)
maml_vae.train(iterations=1000)