Feature

 tf.train.Feature. https://www.tensorflow.org/api_docs/python/tf/train/Feature 

 tf.train.Example https://www.tensorflow.org/api_docs/python/tf/train/Example

tf.io.parse_example  tf.io.parse_example  |  TensorFlow Core v2.8.0 

 tf.RaggedTensor 

int_feature = tf.train.Feature(
    int64_list=tf.train.Int64List(value=[1, 2, 3, 4]))
float_feature = tf.train.Feature(
    float_list=tf.train.FloatList(value=[1., 2., 3., 4.]))
bytes_feature = tf.train.Feature(
    bytes_list=tf.train.BytesList(value=[b"abc", b"1234"]))

example = tf.train.Example(
    features=tf.train.Features(feature={
        'my_ints': int_feature,
        'my_floats': float_feature,
        'my_bytes': bytes_feature,
    }))

tf.io.parse_example(
    example.SerializeToString(),
    features = {
        'my_ints': tf.io.RaggedFeature(dtype=tf.int64),
        'my_floats': tf.io.RaggedFeature(dtype=tf.float32),
        'my_bytes': tf.io.RaggedFeature(dtype=tf.string)})

   tf.Example. https://www.tensorflow.org/tutorials/load_data/tfrecord?hl=zh-cn

# 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)

# 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()

make_parse_example_spec 

# Define features and transformations
feature_a = tf.feature_column.categorical_column_with_vocabulary_file(...)
feature_b = tf.feature_column.numeric_column(...)
feature_c_bucketized = tf.feature_column.bucketized_column(
    tf.feature_column.numeric_column("feature_c"), ...)
feature_a_x_feature_c = tf.feature_column.crossed_column(
    columns=["feature_a", feature_c_bucketized], ...)

feature_columns = set(
    [feature_b, feature_c_bucketized, feature_a_x_feature_c])
features = tf.io.parse_example(
    serialized=serialized_examples,
    features=tf.feature_column.make_parse_example_spec(feature_columns))

# For the above example, make_parse_example_spec would return the dict:
{
    "feature_a": parsing_ops.VarLenFeature(tf.string),
    "feature_b": parsing_ops.FixedLenFeature([1], dtype=tf.float32),
    "feature_c": parsing_ops.FixedLenFeature([1], dtype=tf.float32)
}