TensorFlow Python IO接口

TFRecord

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

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
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

    1
    2
    3
    4
    5
    6
    7
    8
    9
    10
    11
    12
    13
    14
    15
    16
    # 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

    1
    2
    3
    4
    5
    6
    7
    8
    9
    10
    11
    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

Author

UBeaRLy

Posted on

2019-08-19

Updated on

2019-08-19

Licensed under

Comments