def __init__(self, **kwargs):
"""
:param kwargs:
Args of data:
train_iter : train batch data iterator
dev_iter : dev batch data iterator
test_iter : test batch data iterator
Args of train:
model : nn model
config : config
"""
print("Training Start......")
# for k, v in kwargs.items():
# self.__setattr__(k, v)
self.train_iter = kwargs["train_iter"]
self.dev_iter = kwargs["dev_iter"]
self.test_iter = kwargs["test_iter"]
self.model = kwargs["model"]
self.config = kwargs["config"]
self.early_max_patience = self.config.early_max_patience
self.optimizer = Optimizer(name=self.config.learning_algorithm,
model=self.model,
lr=self.config.learning_rate,
weight_decay=self.config.weight_decay,
grad_clip=self.config.clip_max_norm)
self.loss_function = self._loss(
learning_algorithm=self.config.learning_algorithm)
print(self.optimizer)
print(self.loss_function)
self.best_score = Best_Result()
self.train_iter_len = len(self.train_iter)
def __init__(self, **kwargs):
"""
:param kwargs:
Args of data:
train_iter : train batch data iterator
dev_iter : dev batch data iterator
test_iter : test batch data iterator
Args of train:
model : nn model
config : config
"""
print("Training Start......")
# for k, v in kwargs.items():
# self.__setattr__(k, v)
self.train_iter = kwargs["train_iter"]
self.dev_iter = kwargs["dev_iter"]
self.test_iter = kwargs["test_iter"]
self.model = kwargs["model"]
self.config = kwargs["config"]
self.early_max_patience = self.config.early_max_patience
self.optimizer = Optimizer(name=self.config.learning_algorithm,
model=self.model,
lr=self.config.learning_rate,
weight_decay=self.config.weight_decay,
grad_clip=self.config.clip_max_norm)
if self.config.learning_algorithm == "SGD":
self.loss_function = nn.CrossEntropyLoss(size_average=False)
else:
self.loss_function = nn.CrossEntropyLoss(size_average=True)
print(self.optimizer)
self.best_score = Best_Result()
self.train_eval, self.dev_eval_seg, self.dev_eval_pos, self.test_eval_seg, self.test_eval_pos = Eval(
), Eval(), Eval(), Eval(), Eval()
self.train_iter_len = len(self.train_iter)
def __init__(self, **kwargs):
self.config = kwargs["config"]
self.config.logger.info("Training Start......")
self.train_iter = kwargs["train_iter"]
self.dev_iter = kwargs["dev_iter"]
self.test_iter = kwargs["test_iter"]
self.model = kwargs["model"]
self.use_crf = self.config.use_crf
self.average_batch = self.config.average_batch
self.early_max_patience = self.config.early_max_patience
self.optimizer = Optimizer(name=self.config.learning_algorithm,
model=self.model,
lr=self.config.learning_rate,
weight_decay=self.config.weight_decay,
grad_clip=self.config.clip_max_norm)
self.loss_function = self._loss(
learning_algorithm=self.config.learning_algorithm,
label_paddingId=self.config.label_paddingId,
use_crf=self.use_crf)
self.config.logger.info(self.optimizer)
self.config.logger.info(self.loss_function)
self.best_score = Best_Result()
self.train_eval, self.dev_eval, self.test_eval = Eval(), Eval(), Eval()
self.train_iter_len = len(self.train_iter)
def __init__(self, **kwargs):
"""
:param kwargs:
Args of data:
train_iter : train batch data iterator
dev_iter : dev batch data iterator
test_iter : test batch data iterator
Args of train:
model : nn model
config : config
"""
print("Training Start......")
# for k, v in kwargs.items():
# self.__setattr__(k, v)
self.train_iter = kwargs["train_iter"]
self.dev_iter = kwargs["dev_iter"]
self.test_iter = kwargs["test_iter"]
self.model = kwargs["model"]
self.config = kwargs["config"]
self.device = self.config.device
self.cuda = False
if self.device != cpu_device:
self.cuda = True
self.early_max_patience = self.config.early_max_patience
self.optimizer = Optimizer(name=self.config.learning_algorithm,
model=self.model,
lr=self.config.learning_rate,
weight_decay=self.config.weight_decay,
grad_clip=self.config.clip_max_norm)
if self.config.learning_algorithm == "SGD":
self.loss_function = nn.CrossEntropyLoss(reduction="sum")
else:
self.loss_function = nn.CrossEntropyLoss(reduction="mean")
# self.loss_function = nn.MultiLabelSoftMarginLoss(size_average=True)
print(self.optimizer)
self.best_score = Best_Result()
self.train_iter_len = len(self.train_iter)
# define accu eval
self.accu_train_eval_micro, self.accu_dev_eval_micro, self.accu_test_eval_micro = Eval(
), Eval(), Eval()
self.accu_train_eval_macro, self.accu_dev_eval_macro, self.accu_test_eval_macro = [], [], []
for i in range(self.config.accu_class_num):
self.accu_train_eval_macro.append(Eval())
self.accu_dev_eval_macro.append(Eval())
self.accu_test_eval_macro.append(Eval())
# define law eval
self.law_train_eval_micro, self.law_dev_eval_micro, self.law_test_eval_micro = Eval(
), Eval(), Eval()
self.law_train_eval_macro, self.law_dev_eval_macro, self.law_test_eval_macro = [], [], []
for i in range(self.config.law_class_num):
self.law_train_eval_macro.append(Eval())
self.law_dev_eval_macro.append(Eval())
self.law_test_eval_macro.append(Eval())
def __init__(self, **kwargs):
"""
:param kwargs:
Args of data:
train_iter : train batch data iterator
dev_iter : dev batch data iterator
test_iter : test batch data iterator
Args of train:
model : nn model
config : config
"""
print("Training Start......")
# for k, v in kwargs.items():
# self.__setattr__(k, v)
self.train_iter = kwargs["train_iter"]
self.dev_iter = kwargs["dev_iter"]
self.test_iter = kwargs["test_iter"]
self.parser = kwargs["model"]
self.config = kwargs["config"]
self.device = self.config.device
self.cuda = False
if self.device != cpu_device:
self.cuda = True
self.early_max_patience = self.config.early_max_patience
self.optimizer = Optimizer(
name=self.config.learning_algorithm,
model=self.parser.model,
lr=self.config.learning_rate,
# weight_decay=self.config.weight_decay, grad_clip=self.config.clip_max_norm,
weight_decay=self.config.weight_decay,
grad_clip="None",
betas=(0.9, 0.9),
eps=1.0e-12)
if self.config.learning_algorithm == "SGD":
self.loss_function = nn.CrossEntropyLoss(reduction="sum")
else:
self.loss_function = nn.CrossEntropyLoss(reduction="mean")
print(self.optimizer)
self.best_score = Best_Result()
self.train_iter_len = len(self.train_iter)