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