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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
|