tf.train.Example and tf.train.Feature

叁歲伎倆 2022-12-24 15:54 171阅读 0赞

例子：

#coding:utf-8
    import tensorflow as tf
    
    import numpy as np
    import IPython.display as display
    
    def _bytes_feature(value):
      """Returns a bytes_list from a string / byte."""
      if isinstance(value, type(tf.constant(0))):
        value = value.numpy() # BytesList won't unpack a string from an EagerTensor.
      # if isinstance(value, list):
      #   return tf.train.Feature(bytes_list=tf.train.BytesList(value=value))
      return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))
    
    def _float_feature(value):
      """Returns a float_list from a float / double."""
      # if isinstance(value, list):
      #   return tf.train.Feature(float_list=tf.train.FloatList(value=value))
      return tf.train.Feature(float_list=tf.train.FloatList(value=[value]))
    
    def _int64_feature(value):
      """Returns an int64_list from a bool / enum / int / uint."""
      # if isinstance(value, list):
      #   return tf.train.Feature(int64_list=tf.train.Int64List(value=value))
      return tf.train.Feature(int64_list=tf.train.Int64List(value=[value]))
    
    print(_bytes_feature(b'test_string'))
    print(_bytes_feature(u'test_bytes'.encode('utf-8')))
    
    print(_float_feature(np.exp(1)))
    
    print(_int64_feature(True))
    print(_int64_feature(1))
    
    # convert feature to searilize string
    print("SerializeToString:", _int64_feature(True).SerializeToString())
    
    # 随机生成feature 数据
    # The number of observations in the dataset.
    n_observations = int(1e4)
    # Boolean feature, encoded as False or True.
    feature0 = np.random.choice([False, True], n_observations)
    # Integer feature, random from 0 to 4.
    feature1 = np.random.randint(0, 5, n_observations)
    print("feature1:", feature1)
    # String feature
    strings = np.array([b'cat', b'dog', b'chicken', b'horse', b'goat'])
    feature2 = strings[feature1]
    # Float feature, from a standard normal distribution
    feature3 = np.random.randn(n_observations)
    
    def serialize_example(feature0, feature1, feature2, feature3):
      """
      Creates a tf.Example message ready to be written to a file.
      """
      # Create a dictionary mapping the feature name to the tf.Example-compatible
      # data type.
      feature = {
          'feature0': _int64_feature(feature0),
          'feature1': _int64_feature(feature1),
          'feature2': _bytes_feature(feature2),
          'feature3': _float_feature(feature3),
      }
    
      # Create a Features message using tf.train.Example.
    
      example_proto = tf.train.Example(features=tf.train.Features(feature=feature))
      return example_proto.SerializeToString()
    # This is an example observation from the dataset.
    
    example_observation = []
    serialized_example = serialize_example(False, 4, b'goat', 0.9876)
    print("serialized_example:", serialized_example)
    
    # 解码消息
    example_proto = tf.train.Example.FromString(serialized_example)
    print("example_proto:", example_proto)