tf.train.Feature  |  TensorFlow v2.16.1 (original) (raw)

tf.train.Feature

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Used in tf.train.Example protos. Contains a list of values.

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tf.compat.v1.train.Feature

Used in the notebooks

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

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

This proto implements the Union.

The contained list can be one of three types:

• tf.train.BytesList
• tf.train.FloatList
• tf.train.Int64List

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])>}