def run_ccdml():
test_database = EuroSatDatabase()
ccdml = CombinedCrossDomainMetaLearning(
database=test_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=15000,
meta_learning_rate=0.001,
report_validation_frequency=1000,
log_train_images_after_iteration=1000,
num_tasks_val=100,
clip_gradients=True,
experiment_name='cdml',
val_seed=42,
val_test_batch_norm_momentum=0.0,
)
ccdml.train(iterations=60000)
acdml.evaluate(iterations=100, num_tasks=1000, seed=14)
def run_transfer_meta_learning():
mini_imagenet_database = MiniImagenetDatabase()
euro_sat_database = EuroSatDatabase()
tml = TransferMetaLearningVGG16(
database=mini_imagenet_database,
val_database=euro_sat_database,
target_database=euro_sat_database,
network_cls=None,
n=5,
k=1,
k_val_ml=5,
k_val_val=15,
k_val_test=15,
k_test=1,
meta_batch_size=4,
num_steps_ml=1,
lr_inner_ml=0.001,
num_steps_validation=5,
save_after_iterations=1500,
meta_learning_rate=0.0001,
report_validation_frequency=250,
log_train_images_after_iteration=1000,
number_of_tasks_val=100,
number_of_tasks_test=100,
clip_gradients=True,
experiment_name='transfer_meta_learning_mini_imagenet_euro_sat',
val_seed=42,
val_test_batch_norm_momentum=0.0,
random_layer_initialization_seed=42,
num_trainable_layers=3,
)
tml.train(iterations=6000)
tml.evaluate(50, seed=42, use_val_batch_statistics=True)
tml.evaluate(50, seed=42, use_val_batch_statistics=False)
def run_acdml():
acdml = AttentionCrossDomainMetaLearning(
database=None,
val_database=ISICDatabase(),
test_database=EuroSatDatabase(),
network_cls=get_assembled_model,
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=15000,
meta_learning_rate=0.001,
report_validation_frequency=1000,
log_train_images_after_iteration=1000,
num_tasks_val=100,
clip_gradients=True,
experiment_name='acdml_11',
val_seed=42,
val_test_batch_norm_momentum=0.0,
)
acdml.train(iterations=60000)
acdml.evaluate(iterations=100, num_tasks=1000, seed=14)
def run_transfer_learning():
euro_sat_database = EuroSatDatabase()
transfer_learning = TransferLearningVGG16(
database=euro_sat_database,
n=5,
k_val_test=15,
k_test=5,
lr_inner_ml=0.01,
number_of_tasks_test=100,
val_test_batch_norm_momentum=0.0,
random_layer_initialization_seed=42,
num_trainable_layers=0,
)
transfer_learning.evaluate(10, seed=42, use_val_batch_statistics=True)
def setUp(self):
def parse_function(example_address):
return example_address
self.parse_function = parse_function
self.omniglot_database = OmniglotDatabase(random_seed=-1,
num_train_classes=1200,
num_val_classes=100)
self.mini_imagenet_database = MiniImagenetDatabase()
self.celeba_database = CelebADatabase()
self.lfw_database = LFWDatabase()
self.euro_sat_database = EuroSatDatabase()
self.isic_database = ISICDatabase()
self.chest_x_ray_database = ChestXRay8Database()
def run_domain_attention():
train_domain_databases = [
MiniImagenetDatabase(),
OmniglotDatabase(random_seed=47,
num_train_classes=1200,
num_val_classes=100),
DTDDatabase(),
VGGFlowerDatabase()
]
meta_train_domain_databases = [
AirplaneDatabase(),
FungiDatabase(),
CUBDatabase(),
]
test_database = EuroSatDatabase()
ewda = ElementWiseDomainAttention(
train_databases=train_domain_databases,
meta_train_databases=meta_train_domain_databases,
database=test_database,
test_database=test_database,
network_cls=None,
image_shape=(84, 84, 3),
n=5,
k_ml=1,
k_val_ml=5,
k_val=1,
k_val_val=15,
k_test=5,
k_val_test=15,
meta_batch_size=4,
num_steps_ml=5,
lr_inner_ml=0.05,
num_steps_validation=5,
save_after_iterations=15000,
meta_learning_rate=0.001,
report_validation_frequency=1000,
log_train_images_after_iteration=1000,
num_tasks_val=100,
clip_gradients=True,
experiment_name='element_wise_domain_attention',
val_seed=42,
val_test_batch_norm_momentum=0.0,
)
ewda.train(iterations=60000)
ewda.evaluate(iterations=50, num_tasks=1000, seed=14)
if __name__ == '__main__':
tf.config.set_visible_devices([], 'GPU')
root_folder_to_save = os.path.expanduser('~/datasets_visualization/')
if not os.path.exists(root_folder_to_save):
os.mkdir(root_folder_to_save)
databases = (
MiniImagenetDatabase(),
OmniglotDatabase(random_seed=42,
num_train_classes=1200,
num_val_classes=100),
AirplaneDatabase(),
CUBDatabase(),
DTDDatabase(),
FungiDatabase(),
VGGFlowerDatabase(),
# TrafficSignDatabase(),
MSCOCODatabase(),
PlantDiseaseDatabase(),
EuroSatDatabase(),
ISICDatabase(),
ChestXRay8Database(),
)
# for database in databases:
# visualize_database(database, root_folder_to_save)
visualize_all_domains_together(databases, root_folder_to_save)