(self,
                  images,
                  labels_one_hot,
                  scope='AttentionOcr_v1',
                  reuse=None)
return OutputEndpoints(
      chars_logit=chars_logit,
      chars_log_prob=chars_log_prob,
      predicted_chars=predicted_chars,
      predicted_scores=predicted_scores,
      predicted_text=predicted_text)
Creates a base part of the Model (no gradients, losses or summaries).

    Args:
      images: A tensor of shape [batch_size, height, width, channels].
      labels_one_hot: Optional (can be None) one-hot encoding for ground truth
        labels. If provided the function will create a model for training.
      scope: Optional variable_scope.
      reuse: whether or not the network and its variables should be reused. To
        be able to reuse 'scope' must be given.

    Returns:
      A named tuple OutputEndpoints.
Creates a base part of the Model (no gradients, losses or summaries).
Creates a base part of the Model ( no gradients losses or summaries ) .
def create_base(self,
                  images,
                  labels_one_hot,
                  scope='AttentionOcr_v1',
                  reuse=None):
    """Creates a base part of the Model (no gradients, losses or summaries).

    Args:
      images: A tensor of shape [batch_size, height, width, channels].
      labels_one_hot: Optional (can be None) one-hot encoding for ground truth
        labels. If provided the function will create a model for training.
      scope: Optional variable_scope.
      reuse: whether or not the network and its variables should be reused. To
        be able to reuse 'scope' must be given.

    Returns:
      A named tuple OutputEndpoints.
    """
    logging.debug('images: %s', images)
    is_training = labels_one_hot is not None
    
    with tf.variable_scope(scope, reuse=reuse):
      views = tf.split(
        value=images, num_or_size_splits=self._params.num_views, axis=2)
      logging.debug('Views=%d single view: %s', len(views), views[0])

      nets = [
        self.conv_tower_fn(v, is_training, reuse=(i != 0))
        for i, v in enumerate(views)
      ]
      logging.debug('Conv tower: %s', nets[0])

      nets = [self.encode_coordinates_fn(net) for net in nets]
      logging.debug('Conv tower w/ encoded coordinates: %s', nets[0])

      net = self.pool_views_fn(nets)
      logging.debug('Pooled views: %s', net)

      chars_logit = self.sequence_logit_fn(net, labels_one_hot)
      logging.debug('chars_logit: %s', chars_logit)

      predicted_chars, chars_log_prob, predicted_scores = (
        self.char_predictions(chars_logit))
      if self._charset:
        character_mapper = CharsetMapper(self._charset)
        predicted_text = character_mapper.get_text(predicted_chars)
      else:
        predicted_text = tf.constant([])
    return OutputEndpoints(
      chars_logit=chars_logit,
      chars_log_prob=chars_log_prob,
      predicted_chars=predicted_chars,
      predicted_scores=predicted_scores,
      predicted_text=predicted_text)
def create_base ( self , images , labels_one_hot , scope = 'AttentionOcr_v1' , reuse = None ) : logging . debug ( 'images: %s' , images ) is_training = labels_one_hot is not None with tf . variable_scope ( scope , reuse = reuse ) : views = tf . split ( value = images , num_or_size_splits = self . _params . num_views , axis = 2 ) logging . debug ( 'Views=%d single view: %s' , len ( views ) , views [ 0 ] ) nets = [ self . conv_tower_fn ( v , is_training , reuse = ( i != 0 ) ) for i , v in enumerate ( views ) ] logging . debug ( 'Conv tower: %s' , nets [ 0 ] ) nets = [ self . encode_coordinates_fn ( net ) for net in nets ] logging . debug ( 'Conv tower w/ encoded coordinates: %s' , nets [ 0 ] ) net = self . pool_views_fn ( nets ) logging . debug ( 'Pooled views: %s' , net ) chars_logit = self . sequence_logit_fn ( net , labels_one_hot ) logging . debug ( 'chars_logit: %s' , chars_logit ) predicted_chars , chars_log_prob , predicted_scores = ( self . char_predictions ( chars_logit ) ) if self . _charset : character_mapper = CharsetMapper ( self . _charset ) predicted_text = character_mapper . get_text ( predicted_chars ) else : predicted_text = tf . constant ( [ ] ) return OutputEndpoints ( chars_logit = chars_logit , chars_log_prob = chars_log_prob , predicted_chars = predicted_chars , predicted_scores = predicted_scores , predicted_text = predicted_text )