Convert JSON-encoded Example records to binary protocol buffer strings.
View aliases
Compat aliases for migration
See Migration guide for more details.
tf.compat.v1.decode_json_example
, tf.compat.v1.io.decode_json_example
tf.io.decode_json_example(
json_examples, name=None
)
This op converts JSON-serialized tf.train.Example
(maybe created with
json_format.MessageToJson
, following the
standard JSON mapping)
to a binary-serialized tf.train.Example
(equivalent to
Example.SerializeToString()
) suitable for conversion to tensors with
tf.io.parse_example
.
Here is a tf.train.Example
proto:
example = tf.train.Example(
features=tf.train.Features(
feature={
"a": tf.train.Feature(
int64_list=tf.train.Int64List(
value=[1, 1, 3]))}))
Here it is converted to JSON:
from google.protobuf import json_format
example_json = json_format.MessageToJson(example)
print(example_json)
{
"features": {
"feature": {
"a": {
"int64List": {
"value": [
"1",
"1",
"3"
]
}
}
}
}
}
This op converts the above json string to a binary proto:
example_binary = tf.io.decode_json_example(example_json)
example_binary.numpy()
b'\n\x0f\n\r\n\x01a\x12\x08\x1a\x06\x08\x01\x08\x01\x08\x03'
The OP works on string tensors of andy shape:
tf.io.decode_json_example([
[example_json, example_json],
[example_json, example_json]]).shape.as_list()
[2, 2]
This resulting binary-string is equivalent to Example.SerializeToString()
,
and can be converted to Tensors using tf.io.parse_example
and related
functions:
tf.io.parse_example(
serialized=[example_binary.numpy(),
example.SerializeToString()],
features = {'a': tf.io.FixedLenFeature(shape=[3], dtype=tf.int64)})
{'a': <tf.Tensor: shape=(2, 3), dtype=int64, numpy=
array([[1, 1, 3],
[1, 1, 3]])>}
Args | |
---|---|
json_examples
|
A string tensor containing json-serialized tf.Example
protos.
|
name
|
A name for the op. |
Returns | |
---|---|
A string Tensor containing the binary-serialized tf.Example protos.
|
Raises | |
---|---|
tf.errors.InvalidArgumentError : If the JSON could not be converted to a
tf.Example
|