tf.io.decode_json_example

Convert JSON-encoded Example records to binary protocol buffer strings.

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]])>}

json_examples A string tensor containing json-serialized tf.Example protos.
name A name for the op.

A string Tensor containing the binary-serialized tf.Example protos.

tf.errors.InvalidArgumentError: If the JSON could not be converted to a tf.Example