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

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

View aliases

Compat aliases for migration

tf.compat.v1.train.Features

Used in the notebooks

Used in the tutorials
TFRecord and tf.train.Example Graph regularization for sentiment classification using synthesized graphs Graph-based Neural Structured Learning in TFX Instance Segmentation with Model Garden Semantic Segmentation with Model Garden

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])>}

Attributes
feature	repeated FeatureEntry feature

Child Classes

class FeatureEntry