(self, feature)
Write a InputFeature to the TFRecordWriter as a tf.train.Example.
Write a InputFeature to the TFRecordWriter as a tf.train.Example.
Write a InputFeature to the TFRecordWriter as a tf . train . Example .
def process_feature(self, feature):
    """Write a InputFeature to the TFRecordWriter as a tf.train.Example."""
    self.num_features += 1

    def create_int_feature(values):
      feature = tf.train.Feature(
          int64_list=tf.train.Int64List(value=list(values)))
      return feature

    def create_float_feature(values):
      f = tf.train.Feature(float_list=tf.train.FloatList(value=list(values)))
      return f

    features = collections.OrderedDict()
    features["unique_ids"] = create_int_feature([feature.unique_id])
    features["input_ids"] = create_int_feature(feature.input_ids)
    features["input_mask"] = create_float_feature(feature.input_mask)
    features["p_mask"] = create_float_feature(feature.p_mask)
    features["segment_ids"] = create_int_feature(feature.segment_ids)

    features["cls_index"] = create_int_feature([feature.cls_index])

    if self.is_training:
      features["start_positions"] = create_int_feature([feature.start_position])
      features["end_positions"] = create_int_feature([feature.end_position])
      impossible = 0
      if feature.is_impossible:
        impossible = 1
      features["is_impossible"] = create_float_feature([impossible])

    tf_example = tf.train.Example(features=tf.train.Features(feature=features))
    self._writer.write(tf_example.SerializeToString())
def process_feature ( self , feature ) : self . num_features += 1 def create_int_feature ( values ) : feature = tf . train . Feature ( int64_list = tf . train . Int64List ( value = list ( values ) ) ) return feature def create_float_feature ( values ) : f = tf . train . Feature ( float_list = tf . train . FloatList ( value = list ( values ) ) ) return f features = collections . OrderedDict ( ) features [ "unique_ids" ] = create_int_feature ( [ feature . unique_id ] ) features [ "input_ids" ] = create_int_feature ( feature . input_ids ) features [ "input_mask" ] = create_float_feature ( feature . input_mask ) features [ "p_mask" ] = create_float_feature ( feature . p_mask ) features [ "segment_ids" ] = create_int_feature ( feature . segment_ids ) features [ "cls_index" ] = create_int_feature ( [ feature . cls_index ] ) if self . is_training : features [ "start_positions" ] = create_int_feature ( [ feature . start_position ] ) features [ "end_positions" ] = create_int_feature ( [ feature . end_position ] ) impossible = 0 if feature . is_impossible : impossible = 1 features [ "is_impossible" ] = create_float_feature ( [ impossible ] ) tf_example = tf . train . Example ( features = tf . train . Features ( feature = features ) ) self . _writer . write ( tf_example . SerializeToString ( ) )