mirror of
https://github.com/wassname/Castor.git
synced 2026-10-04 12:10:17 +08:00
Victor Yang
51d8e29525
* update nce-sm * refactor code, update torchtext * use shared evaluation * refactor code, use shared data loader * refactor code * refactor code * refactor code according to Michael's great suggestions * update readme and requirement * update datasets and readme * update data loader * add space between + * update refactor code * add nce-mp * remove duplicate files * update readme, refactor code according to mp_cnn and delete duplicate code, follow PEP8 standard * refactor code, add/delete comments * import exit from sys
38 lines
1.5 KiB
Python
38 lines
1.5 KiB
Python
class Trainer(object):
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"""
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Abstraction for training a model on a Dataset.
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"""
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def __init__(self, model, train_loader, trainer_config, train_evaluator, test_evaluator, dev_evaluator=None):
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self.model = model
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self.optimizer = trainer_config['optimizer']
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self.train_loader = train_loader
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self.batch_size = trainer_config['batch_size']
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self.log_interval = trainer_config['log_interval']
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self.model_outfile = trainer_config['model_outfile']
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self.lr_reduce_factor = trainer_config['lr_reduce_factor']
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self.patience = trainer_config['patience']
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self.use_tensorboard = trainer_config['tensorboard']
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if self.use_tensorboard:
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from tensorboardX import SummaryWriter
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self.writer = SummaryWriter(log_dir=None, comment='' if trainer_config['run_label'] is None else trainer_config['run_label'])
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self.logger = trainer_config['logger']
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self.train_evaluator = train_evaluator
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self.test_evaluator = test_evaluator
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self.dev_evaluator = dev_evaluator
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def evaluate(self, evaluator, dataset_name):
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scores, metric_names = evaluator.get_scores()
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self.logger.info('\t'.join([' '] + metric_names))
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self.logger.info('\t'.join([dataset_name] + list(map(str, scores))))
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return scores
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def train_epoch(self, epoch):
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raise NotImplementedError()
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def train(self, epochs):
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raise NotImplementedError()
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