tf.train.Features

Used in tf.train.Example protos. Contains the mapping from keys to Feature.

Used in the notebooks

Used in the tutorials

An Example proto is a representation of the following python type:

Dict[str, Union[List[bytes], List[int64], List[float]]]

This proto implements the Dict.

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,  }))

Use tf.io.parse_example to extract tensors from a serialized Example proto:

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)}) {'my_bytes': <tf.Tensor: shape=(2,), dtype=string,  numpy=array([b'abc', b'1234'], dtype=object)>,  'my_floats': <tf.Tensor: shape=(4,), dtype=float32,  numpy=array([1., 2., 3., 4.], dtype=float32)>,  'my_ints': <tf.Tensor: shape=(4,), dtype=int64,  numpy=array([1, 2, 3, 4])>}

feature repeated FeatureEntry feature

Child Classes

class FeatureEntry