def __init__(
self,
model: models.ConditionedModel = Ref("model"),
src_file: Union[None, str, Sequence[str]] = None,
trg_file: Optional[str] = None,
dev_every: numbers.Integral = 0,
dev_zero: bool = False,
batcher: batchers.Batcher = bare(batchers.SrcBatcher, batch_size=32),
loss_calculator: loss_calculators.LossCalculator = bare(
loss_calculators.MLELoss),
trainer: optimizers.XnmtOptimizer = bare(optimizers.SimpleSGDTrainer,
e0=0.1),
run_for_epochs: Optional[numbers.Integral] = None,
lr_decay: numbers.Real = 1.0,
lr_decay_times: numbers.Integral = 3,
patience: numbers.Integral = 1,
initial_patience: Optional[numbers.Integral] = None,
dev_tasks: Sequence[eval_tasks.EvalTask] = None,
dev_combinator: Optional[str] = None,
restart_trainer: bool = False,
reload_command: Optional[str] = None,
name: str = "{EXP}",
sample_train_sents: Optional[numbers.Integral] = None,
max_num_train_sents: Optional[numbers.Integral] = None,
max_src_len: Optional[numbers.Integral] = None,
max_trg_len: Optional[numbers.Integral] = None,
loss_comb_method: str = Ref("exp_global.loss_comb_method",
default="sum"),
update_every: numbers.Integral = 1,
commandline_args: dict = Ref("exp_global.commandline_args", default={})
) -> None:
super().__init__(model=model,
src_file=src_file,
trg_file=trg_file,
dev_every=dev_every,
batcher=batcher,
loss_calculator=loss_calculator,
run_for_epochs=run_for_epochs,
lr_decay=lr_decay,
lr_decay_times=lr_decay_times,
patience=patience,
initial_patience=initial_patience,
dev_tasks=dev_tasks,
dev_combinator=dev_combinator,
restart_trainer=restart_trainer,
reload_command=reload_command,
name=name,
sample_train_sents=sample_train_sents,
max_num_train_sents=max_num_train_sents,
max_src_len=max_src_len,
max_trg_len=max_trg_len)
self.dev_zero = dev_zero
self.trainer = trainer or optimizers.SimpleSGDTrainer(e0=0.1)
self.dynet_profiling = commandline_args.get(
"dynet_profiling", 0) if commandline_args else 0
self.train_loss_tracker = loss_trackers.TrainLossTracker(self)
self.loss_comb_method = loss_comb_method
self.update_every = update_every
self.num_updates_skipped = 0
def __init__(self,
model: models.ConditionedModel = Ref("model"),
src_file: Union[None, str, Sequence[str]] = None,
trg_file: Optional[str] = None,
dev_zero: bool = False,
batcher: batchers.Batcher = bare(batchers.SrcBatcher,
batch_size=32),
loss_calculator: loss_calculators.LossCalculator = bare(
loss_calculators.MLELoss),
trainer: optimizers.XnmtOptimizer = bare(
optimizers.SimpleSGDTrainer, e0=0.1),
run_for_epochs: Optional[numbers.Integral] = None,
lr_decay: numbers.Real = 1.0,
lr_decay_times: numbers.Integral = 3,
patience: numbers.Integral = 1,
initial_patience: Optional[numbers.Integral] = None,
dev_tasks: Sequence[eval_tasks.EvalTask] = None,
dev_combinator: Optional[str] = None,
restart_trainer: bool = False,
reload_command: Optional[str] = None,
name: str = "{EXP}",
sample_train_sents: Optional[numbers.Integral] = None,
max_num_train_sents: Optional[numbers.Integral] = None,
max_src_len: Optional[numbers.Integral] = None,
max_trg_len: Optional[numbers.Integral] = None,
loss_comb_method: str = Ref("exp_global.loss_comb_method",
default="sum"),
update_every: numbers.Integral = 1) -> None:
super().__init__(model=model,
src_file=src_file,
trg_file=trg_file,
batcher=batcher,
loss_calculator=loss_calculator,
run_for_epochs=run_for_epochs,
lr_decay=lr_decay,
lr_decay_times=lr_decay_times,
patience=patience,
initial_patience=initial_patience,
dev_tasks=dev_tasks,
dev_combinator=dev_combinator,
restart_trainer=restart_trainer,
reload_command=reload_command,
name=name,
sample_train_sents=sample_train_sents,
max_num_train_sents=max_num_train_sents,
max_src_len=max_src_len,
max_trg_len=max_trg_len)
if batcher.batch_size != 1:
raise ValueError(
"AutobatchTrainingRegimen forces the batcher to have batch_size 1. Use update_every to set the actual batch size in this regimen."
)
self.dev_zero = dev_zero
self.trainer = trainer or optimizers.SimpleSGDTrainer(e0=0.1)
self.loss_comb_method = loss_comb_method
self.update_every = update_every
self.num_updates_skipped = 0