|---|
| [ View source on GitHub](https://github.com/tensorflow/tensorflow/blob/v2.21.0/tensorflow/core/example/feature.proto) |
Used in [`tf.train.Example`](https://www.tensorflow.org/api_docs/python/tf/train/Example) protos. Contains a list of values.
#### View aliases
**Compat aliases for migration**
See
[Migration guide](https://www.tensorflow.org/guide/migrate) for
more details.
[`tf.compat.v1.train.Feature`](https://www.tensorflow.org/api_docs/python/tf/train/Feature)
### Used in the notebooks
| Used in the tutorials |
|---|
| - [TFRecord and tf.train.Example](https://www.tensorflow.org/tutorials/load_data/tfrecord) - [Graph-based Neural Structured Learning in TFX](https://www.tensorflow.org/tfx/tutorials/tfx/neural_structured_learning) - [Feature Engineering using TFX Pipeline and TensorFlow Transform](https://www.tensorflow.org/tfx/tutorials/tfx/penguin_tft) - [Graph regularization for sentiment classification using synthesized graphs](https://www.tensorflow.org/neural_structured_learning/tutorials/graph_keras_lstm_imdb) - [Preprocessing data with TensorFlow Transform](https://www.tensorflow.org/tfx/tutorials/transform/census) |
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`](https://www.tensorflow.org/api_docs/python/tf/train/BytesList)
- [`tf.train.FloatList`](https://www.tensorflow.org/api_docs/python/tf/train/FloatList)
- [`tf.train.Int64List`](https://www.tensorflow.org/api_docs/python/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`](https://www.tensorflow.org/api_docs/python/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': ,
'my_floats': ,
'my_ints': }
| ## Attributes ||
|---|---|
| `bytes_list` | `BytesList bytes_list` |
| `float_list` | `FloatList float_list` |
| `int64_list` | `Int64List int64_list` |