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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Compat aliases for migration

SeeMigration guide for more details.

tf.compat.v1.train.Feature

Used in the notebooks

Used in the tutorials
TFRecord and tf.train.Example Graph-based Neural Structured Learning in TFX Feature Engineering using TFX Pipeline and TensorFlow Transform Graph regularization for sentiment classification using synthesized graphs Preprocessing data with TensorFlow Transform

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:

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
bytes_list BytesList bytes_list
float_list FloatList float_list
int64_list Int64List int64_list