tensorflow生成tfrecord格式的数据

tensorflow生成tfrecord格式的数据

tfrecord格式数据能高效的组织数据,提升训练时的IO性能
1,2步骤定义了函数,3步骤生成tfrecord格式的数据 1.TF-Feature 将数据(values)封装于tf.train.Featurepython

def int64_feature(values):
  """Returns a TF-Feature of int64s. Args: values: A scalar or list of values. Returns: A TF-Feature. """
  if not isinstance(values, (tuple, list)):
    values = [values]
  return tf.train.Feature(int64_list=tf.train.Int64List(value=values))


def bytes_feature(values):
  """Returns a TF-Feature of bytes. Args: values: A string. Returns: A TF-Feature. """
  return tf.train.Feature(bytes_list=tf.train.BytesList(value=[values]))


def float_feature(values):
  """Returns a TF-Feature of floats. Args: values: A scalar of list of values. Returns: A TF-Feature. """
  if not isinstance(values, (tuple, list)):
    values = [values]
  return tf.train.Feature(float_list=tf.train.FloatList(value=values))
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2.tf.train.Examplebash

def create_tf_example(features_dict):
    ''' :param features_dict: { img_query_bytes:bytes :return: tf.train.Example '''
    feature_map={  'img_query':bytes_feature(features_dict['img_query_bytes'])
                  }
    return tf.train.Example(features=tf.train.Features(feature=feature_map))
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3.写入tfrecord文件 使用tf.python_io.TFRecordWriter(out_path) 写入 tf.train.Example函数

out_path = './dataset/train.record'
    with tf.python_io.TFRecordWriter(out_path) as writer:
            features_dict=dict()
            with tf.gfile.GFile(img_path,'rb') as fid:
                features_dict['img_query_bytes']=fid.read()

            example=create_tf_example(features_dict)
            writer.write(example.SerializeToString())
            if iter_num%1000==0:
                print('done : {} % {}'.format(iter_num,iter_steps))
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一个Example结构

dict 表示字典类型性能

tf.train.Example {
    features: tf.train.Features{
        feature: dict{
            'feature_name':tf.train.Feature{
                int64_list:tf.train.Int64List{value:list}
                bytes_list:tf.train.BytesList{value:list}
                float_list:tf.train.FloatList{value:list}
            }
        }
    }
}
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