TensorFlow Python IO接口

TFRecord

TFRecord格式：序列化的tf.train.Example protbuf对象

class tf.python_io.TFRecordWriter:
	def __init__(self,
		?fileanme: str,
		options: tf.python_io.TFRecordOptions,
		name=None
	):
		pass

class tf.python_io.TFRecordReader:
	def __init__(self,
		options: tf.python_io.TFRecordOptions,
		name=None
	):
		pass

	def read(self):
		pass

###	Feature/Features

```python
class tf.train.Features:
	def __init__(self,
		feature: {str: tf.train.Feature}
	):
		pass

class tf.train.Feature:
	def __init__(self,
		int64_list: tf.train.Int64List,
		float64_list: tf.train.Float64List,
	)

示例

• 转换、写入TFRecord

  # 创建写入文件
  writer = tf.python_io.TFRecord(out_file)
  shape, binary_image = get_image_binary(image_file)
  # 创建Features对象
  featurs = tf.train.Features(
  	feature = {
  		"label": tf.train.Feature(int64_list=tf.train.Int64List(label)),
  		"shape": tf.train.Feature(bytes_list=tf.train.BytesList(shape)),
  		"image": tf.train.Feature(bytes_list=tf.train.BytesList(binary_image))
  	}
  )
  # 创建包含以上特征的示例对象
  sample = tf.train.Example(features=Features)
  # 写入文件
  writer.write(sample.SerializeToString())
  writer.close()

• 读取TFRecord

  dataset = tf.data.TFRecordDataset(tfrecord_files)
  dataset = dataset.map(_parse_function)
  def _parse_function(tf_record_serialized):
  	features = {
  		"labels": tf.FixedLenFeature([], tf.int64),
  		"shape": tf.FixedLenFeature([], tf.string),
  		"image": tf.FixedLenFeature([], tf.string)
  	}
  	parsed_features = tf.parse_single_example(tfrecord_serialized, features)
  	return parsed_features["label"], parsed_features["shape"],
  			parsed_features["image"]

其他函数