tf.train.Example and tf.train.Feature-蒲公英云

tf.train.Example and tf.train.Feature

叁歲伎倆 2022-12-24 15:54 288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)