def get_default_callbacks(self):
return [
('epoch_timer', EpochTimer()),
('train_loss',
EpochScoring(
train_loss_score,
name='train_loss',
on_train=True,
)),
('valid_loss', EpochScoring(
valid_loss_score,
name='valid_loss',
)),
('valid_acc',
EpochScoring(
'accuracy',
name='valid_acc',
lower_is_better=False,
)),
('print_log', PrintLog(sink=logger_info,
keys_ignored=[
'batches',
])),
('auc', EpochScoring(scoring='roc_auc', lower_is_better=False))
]
def _default_callbacks(self):
return [
('epoch_timer', EpochTimer()),
('critic_loss',
PassthroughScoring(
name='critic_loss',
lower_is_better=True,
on_train=True,
)),
('train_distance',
PassthroughScoring(
name='train_distance',
lower_is_better=True,
on_train=True,
)),
('valid_distance',
PassthroughScoring(
name='valid_distance',
lower_is_better=True,
)),
('inter_distance',
PassthroughScoring(
name='inter_distance',
lower_is_better=True,
)),
('print_log', PrintLog()),
]
def _default_callbacks(self):
return [
('epoch_timer', EpochTimer()),
('train_loss',
PassthroughScoring(
name='train_loss',
on_train=True,
)),
('valid_loss', PassthroughScoring(name='valid_loss', )),
# add train accuracy because by default, there is no valid split
('train_acc',
EpochScoring(
'accuracy',
name='train_acc',
lower_is_better=False,
on_train=True,
)),
('valid_acc',
EpochScoring(
'accuracy',
name='valid_acc',
lower_is_better=False,
)),
('print_log', PrintLog()),
]
def _default_callbacks(self):
return [
('epoch_timer', EpochTimer()),
('train_loss', PassthroughScoring(
name='train_loss',
on_train=True,
)),
('discriminator_loss', PassthroughScoring(
name='discriminator_loss',
on_train=True
)),
('generator_loss', PassthroughScoring(
name='generator_loss',
on_train=True
)),
('adversarial_loss', PassthroughScoring(
name='adversarial_loss',
on_train=True
)),
('contextual_loss', PassthroughScoring(
name='contextual_loss',
on_train=True
)),
('encoder_loss', PassthroughScoring(
name='encoder_loss',
on_train=True
)),
('print_log', PrintLog()),
]
def test_setting_callback_default_possible(self, net_cls, module_cls):
from skorch.callbacks import EpochTimer, PrintLog
net = net_cls(module_cls)
net.initialize()
assert isinstance(dict(net.callbacks_)['print_log'], PrintLog)
net.set_params(callbacks__print_log=EpochTimer())
assert isinstance(dict(net.callbacks_)['print_log'], EpochTimer)
def test_setting_callback_possible(self, net_cls, module_cls):
from skorch.callbacks import EpochTimer, PrintLog
net = net_cls(module_cls, callbacks=[('mycb', PrintLog())])
net.initialize()
assert isinstance(dict(net.callbacks_)['mycb'], PrintLog)
net.set_params(callbacks__mycb=EpochTimer())
assert isinstance(dict(net.callbacks_)['mycb'], EpochTimer)
def get_default_callbacks(self):
return [
('epoch_timer', EpochTimer()),
('train_loss', Scoring(
'train_loss',
train_loss_score,
on_train=True)),
('valid_loss', Scoring('valid_loss', valid_loss_score)),
('valid_acc', Scoring(
name='valid_acc',
scoring='accuracy_score',
lower_is_better=False,
on_train=False,
pred_extractor=accuracy_pred_extractor,
)),
('print_log', PrintLog()),
]
def _default_callbacks(self):
return [
('epoch_timer', EpochTimer()),
('train_loss',
BatchScoring(
train_loss_score,
name='train_loss',
on_train=True,
target_extractor=noop,
)),
('valid_loss',
BatchScoring(
valid_loss_score,
name='valid_loss',
target_extractor=noop,
)),
('print_log', PrintLog()),
]
def callbacks_monkey_patch(self):
return [
('epoch_timer', EpochTimer()),
('train_loss', BatchScoring(
train_loss_score,
name='train_loss',
on_train=True,
target_extractor= \
lambda x: self.encoder.transform(np.array(x).reshape(-1, 1)),
)),
('valid_loss', BatchScoring(
valid_loss_score,
name='valid_loss',
target_extractor= \
lambda x: self.encoder.transform(np.array(x).reshape(-1, 1)),
)),
('print_log', PrintLog()),
]
def get_default_callbacks(self):
return [
('epoch_timer', EpochTimer()),
('train_loss', EpochScoring(
train_loss_score,
name='train_loss',
on_train=True,
)),
('valid_loss', EpochScoring(
valid_loss_score,
name='valid_loss',
)),
('valid_acc', EpochScoring(
'accuracy',
name='valid_acc',
lower_is_better=False,
)),
('print_log', PrintLog()),
]
def _default_callbacks(self):
default_cb_list = [
('epoch_timer', EpochTimer()),
('train_loss',
BatchScoring(train_loss_score,
name='train_loss',
on_train=True,
target_extractor=noop)),
('valid_loss',
BatchScoring(valid_loss_score,
name='valid_loss',
target_extractor=noop)),
('valid_acc',
EpochScoring(
'accuracy',
name='valid_acc',
lower_is_better=False,
)),
# ('checkpoint', Checkpoint(
# dirname=self.model_path)),
# ('end_checkpoint', TrainEndCheckpoint(
# dirname=self.model_path)),
('report', ReportLog()),
('progressbar', ProgressBar())
]
# if 'stop_patience' in self.hyperparamters.keys() and \
# self.hyperparamters['stop_patience']:
# earlystop_cb = ('earlystop', EarlyStopping(
# patience=self.patience,
# threshold=1e-4))
# default_cb_list.append(earlystop_cb)
#
# if 'lr_step' in self.hyperparamters.keys() and \
# self.hyperparamters['lr_step']:
# lr_callback = ('lr_schedule', DecayLR(
# self.hyperparamters['lr'],
# self.hyperparamters['lr_step'],
# gamma=0.5))
# default_cb_list.append(lr_callback)
return default_cb_list
def _default_callbacks(self):
return [
("epoch_timer", EpochTimer()),
(
"train_loss",
BatchScoring(
train_loss_score,
name="train_loss",
on_train=True,
target_extractor=noop,
),
),
(
"valid_loss",
BatchScoring(
valid_loss_score, name="valid_loss", target_extractor=noop,
),
),
("print_log", PrintLog()),
]