def run(self,
train_set,
valid_set=None,
test_set=None,
train_size=None,
controllers=None):
"""
Run until the end.
"""
if isinstance(train_set, Dataset):
dataset = train_set
train_set = dataset.train_set()
valid_set = dataset.valid_set()
test_set = dataset.test_set()
train_size = dataset.train_size()
timer = Timer()
for _ in self.train(train_set,
valid_set=valid_set,
test_set=test_set,
train_size=train_size):
if controllers:
ending = False
for controller in controllers:
if hasattr(controller, 'invoke') and controller.invoke():
ending = True
if ending:
break
timer.report()
return
def run(self, train_set, valid_set=None, test_set=None, train_size=None, controllers=None):
"""
Run until the end.
"""
if isinstance(train_set, Dataset):
dataset = train_set
train_set = dataset.train_set()
valid_set = dataset.valid_set()
test_set = dataset.test_set()
train_size = dataset.train_size()
timer = Timer()
for _ in self.train(train_set, valid_set=valid_set, test_set=test_set, train_size=train_size):
if controllers:
ending = False
for controller in controllers:
if hasattr(controller, 'invoke') and controller.invoke():
ending = True
if ending:
break
timer.report()
return
ap.add_argument("--random_glimpse", default=False)
args = ap.parse_args()
mnist = MiniBatches((MnistDataset()), batch_size=1)
model_path = args.model
network = get_network(model_path, std=args.variance,
disable_reinforce=args.disable_reinforce, random_glimpse=args.random_glimpse)
trainer_conf = TrainerConfig()
trainer_conf.learning_rate = args.learning_rate
trainer_conf.weight_l2 = 0.0001
trainer_conf.hidden_l2 = 0.0001
trainer_conf.method = args.method
trainer_conf.disable_reinforce=args.disable_reinforce
trainer_conf.disable_backprop=args.disable_backprop
trainer = AttentionTrainer(network, network.layers[0], config=trainer_conf)
trainer_conf.report()
timer = Timer()
for _ in list(trainer.train(mnist.train_set(), mnist.valid_set(), mnist.test_set())):
pass
timer.end()
network.save_params(model_path)
timer.report()
