def train(self,
train_data,
epochs,
valid_data,
final_rate=0.5,
resume_chkpt=None):
"""
Args:
train_data (iterator): train data iterator
epochs (int): total epochs of training
valid_data (iterator): valid data iterator
final_rate (float): final sample rate for ground truth
resume_chkpt (str): resume checkpoint path
"""
if resume_chkpt is not None:
self.start_epoch, self.model, self.optimizer = \
load_chkpt(resume_chkpt, self.model, self.optimizer, self.cuda)
self.start_epoch += 1
self.schedule_func = self.get_sample_func(epochs, len(train_data),
final_rate)
for epoch in range(self.start_epoch, epochs + 1):
_, _ = self.train_on_epoch(train_data, epoch)
acc, ppl = self.eval_on_epoch(valid_data, epoch)
self.epoch_step(ppl, epoch)
drop_chkpt(epoch, self.model, self.optimizer, acc, ppl)
def train(self,
train_data,
epochs,
valid_data,
sample_size=10,
sample_length=20,
resume_chkpt=None):
"""
Args:
train_data (iterator): train data iterator
epochs (int): total epochs of training
valid_data (iterator): valid data iterator
sample_size (int): sample size of each sentence in every batch
sample_length (int): max sample length of decoder
resume_chkpt (str): resume checkpoint path
"""
if resume_chkpt is not None:
self.start_epoch, self.model, self.optimizer = \
load_chkpt(resume_chkpt, self.model, self.optimizer, self.cuda)
self.start_epoch += 1
self.sample_size = sample_size
self.sample_length = sample_length
for epoch in range(self.start_epoch, epochs + 1):
_ = self.train_on_epoch(train_data, epoch)
acc, ppl = self.eval_on_epoch(valid_data, epoch)
self.epoch_step(ppl, epoch)
drop_chkpt(epoch, self.model, self.optimizer, acc, ppl)
def train(self, train_data, epochs, valid_data, resume_chkpt=None):
"""
Args:
train_data (iterator): train data iterator
epochs (int): total epochs of training
valid_data (iterator): valid data iterator
resume_chkpt (str): resume checkpoint path
"""
if resume_chkpt is not None:
self.start_epoch, self.model, self.optimizer = \
load_chkpt(resume_chkpt, self.model, self.optimizer, self.cuda)
self.start_epoch += 1
for epoch in range(self.start_epoch, epochs + 1):
_, _ = self.train_on_epoch(train_data, epoch)
acc, ppl = self.eval_on_epoch(valid_data, epoch)
self.epoch_step(ppl, epoch)
drop_chkpt(epoch, self.model, self.optimizer, acc, ppl)
def prepare_model(self, chkpt, cuda):
_, self.model = load_chkpt(chkpt, self.model)
if cuda:
self.cuda = True
self.model = self.model.cuda()
self.model.eval()