tf.train.Feature
View source on GitHub |
Used in tf.train.Example
protos. Contains a list of values.
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 |
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Licensed under the Creative Commons Attribution License 4.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/versions/r2.9/api_docs/python/tf/train/Feature