network.py 文件源码

def __init__(self, model, *args, **kwargs):
    """"""

    if args:
      if len(args) > 1:
        raise TypeError('Parser takes at most one argument')

    kwargs['name'] = kwargs.pop('name', model.__name__)
    super(Network, self).__init__(*args, **kwargs)
    if not os.path.isdir(self.save_dir):
      os.mkdir(self.save_dir)
    with open(os.path.join(self.save_dir, 'config.cfg'), 'w') as f:
      self._config.write(f)

    self._global_step = tf.Variable(0., trainable=False)
    self._global_epoch = tf.Variable(0., trainable=False)
    self._model = model(self._config, global_step=self.global_step)

    self._vocabs = []
    vocab_files = [(self.word_file, 1, 'Words'),
                   (self.tag_file, [3, 4], 'Tags'),
                   (self.rel_file, 7, 'Rels')]
    for i, (vocab_file, index, name) in enumerate(vocab_files):
      vocab = Vocab(vocab_file, index, self._config,
                    name=name,
                    cased=self.cased if not i else True,
                    use_pretrained=(not i),
                    global_step=self.global_step)
      self._vocabs.append(vocab)

    self._trainset = Dataset(self.train_file, self._vocabs, model, self._config, name='Trainset')
    self._validset = Dataset(self.valid_file, self._vocabs, model, self._config, name='Validset')
    self._testset = Dataset(self.test_file, self._vocabs, model, self._config, name='Testset')

    self._ops = self._gen_ops()
    self._save_vars = filter(lambda x: u'Pretrained' not in x.name, tf.all_variables())
    self.history = {
      'train_loss': [],
      'train_accuracy': [],
      'valid_loss': [],
      'valid_accuracy': [],
      'test_acuracy': 0
    }
    return

  #=============================================================