def test_keyed_parse_json(self):
gfile.Glob = self._orig_glob
filename = self._create_temp_file(
'{"features": {"feature": {"age": {"int64_list": {"value": [0]}}}}}\n'
'{"features": {"feature": {"age": {"int64_list": {"value": [1]}}}}}\n'
'{"features": {"feature": {"age": {"int64_list": {"value": [2]}}}}}\n'
)
batch_size = 1
queue_capacity = 5
name = "my_batch"
with tf.Graph().as_default() as g, self.test_session(
graph=g) as session:
dtypes = {"age": tf.FixedLenFeature([1], tf.int64)}
parse_fn = lambda example: tf.parse_single_example( # pylint: disable=g-long-lambda
tf.decode_json_example(example), dtypes)
keys, inputs = tf.contrib.learn.io.read_keyed_batch_examples(
filename,
batch_size,
reader=tf.TextLineReader,
randomize_input=False,
num_epochs=1,
queue_capacity=queue_capacity,
parse_fn=parse_fn,
name=name)
self.assertAllEqual((None, ), keys.get_shape().as_list())
self.assertEqual(1, len(inputs))
self.assertAllEqual((None, 1), inputs["age"].get_shape().as_list())
session.run(tf.local_variables_initializer())
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(session, coord=coord)
key, age = session.run([keys, inputs["age"]])
self.assertAllEqual(age, [[0]])
self.assertAllEqual(key, [filename.encode("utf-8") + b":1"])
key, age = session.run([keys, inputs["age"]])
self.assertAllEqual(age, [[1]])
self.assertAllEqual(key, [filename.encode("utf-8") + b":2"])
key, age = session.run([keys, inputs["age"]])
self.assertAllEqual(age, [[2]])
self.assertAllEqual(key, [filename.encode("utf-8") + b":3"])
with self.assertRaises(errors.OutOfRangeError):
session.run(inputs)
coord.request_stop()
coord.join(threads)
def _serving_input_receiver_fn():
"""Applies transforms to raw data in json-example strings."""
json_example_placeholder = tf.placeholder(tf.string, shape=[None])
example_strings = tf.decode_json_example(json_example_placeholder)
raw_features = tf.parse_example(example_strings, raw_serving_feature_spec)
inputs = {"json_example": json_example_placeholder}
_, transformed_features = (
saved_transform_io.partially_apply_saved_transform_internal(
transform_savedmodel_dir, raw_features))
if convert_scalars_to_vectors:
transformed_features = _convert_scalars_to_vectors(transformed_features)
return tf.estimator.export.ServingInputReceiver(
transformed_features, inputs)
def test_keyed_parse_json(self):
gfile.Glob = self._orig_glob
filename = self._create_temp_file(
'{"features": {"feature": {"age": {"int64_list": {"value": [0]}}}}}\n'
'{"features": {"feature": {"age": {"int64_list": {"value": [1]}}}}}\n'
'{"features": {"feature": {"age": {"int64_list": {"value": [2]}}}}}\n'
)
batch_size = 1
queue_capacity = 5
name = "my_batch"
with tf.Graph().as_default() as g, self.test_session(graph=g) as session:
dtypes = {"age": tf.FixedLenFeature([1], tf.int64)}
parse_fn = lambda example: tf.parse_single_example( # pylint: disable=g-long-lambda
tf.decode_json_example(example), dtypes
)
keys, inputs = tf.contrib.learn.io.read_keyed_batch_examples(
filename,
batch_size,
reader=tf.TextLineReader,
randomize_input=False,
num_epochs=1,
queue_capacity=queue_capacity,
parse_fn=parse_fn,
name=name,
)
session.run(tf.initialize_local_variables())
coord = tf.train.Coordinator()
tf.train.start_queue_runners(session, coord=coord)
key, age = session.run([keys, inputs["age"]])
self.assertAllEqual(age, [[0]])
self.assertAllEqual(key, [filename.encode("utf-8") + b":1"])
key, age = session.run([keys, inputs["age"]])
self.assertAllEqual(age, [[1]])
self.assertAllEqual(key, [filename.encode("utf-8") + b":2"])
key, age = session.run([keys, inputs["age"]])
self.assertAllEqual(age, [[2]])
self.assertAllEqual(key, [filename.encode("utf-8") + b":3"])
with self.assertRaises(errors.OutOfRangeError):
session.run(inputs)
coord.request_stop()
def _serving_input_fn():
"""Applies transforms to raw data in json-example strings."""
json_example_placeholder = tf.placeholder(tf.string, shape=[None])
example_strings = tf.decode_json_example(json_example_placeholder)
raw_features = tf.parse_example(example_strings,
raw_serving_feature_spec)
inputs = {"json_example": json_example_placeholder}
_, transformed_features = (
saved_transform_io.partially_apply_saved_transform(
transform_savedmodel_dir, raw_features))
if convert_scalars_to_vectors:
transformed_features = _convert_scalars_to_vectors(
transformed_features)
return input_fn_utils.InputFnOps(transformed_features, None, inputs)
def _testRoundTrip(self, examples):
with self.test_session() as sess:
examples = np.array(examples, dtype=np.object)
json_tensor = tf.constant(
[json_format.MessageToJson(m) for m in examples.flatten()], shape=examples.shape, dtype=tf.string
)
binary_tensor = tf.decode_json_example(json_tensor)
binary_val = sess.run(binary_tensor)
if examples.shape:
self.assertShapeEqual(binary_val, json_tensor)
for input_example, output_binary in zip(np.array(examples).flatten(), binary_val.flatten()):
output_example = tf.train.Example()
output_example.ParseFromString(output_binary)
self.assertProtoEquals(input_example, output_example)
else:
output_example = tf.train.Example()
output_example.ParseFromString(binary_val)
self.assertProtoEquals(examples.item(), output_example)
def _testRoundTrip(self, examples):
with self.test_session() as sess:
examples = np.array(examples, dtype=np.object)
json_tensor = tf.constant(
[json_format.MessageToJson(m) for m in examples.flatten()],
shape=examples.shape, dtype=tf.string)
binary_tensor = tf.decode_json_example(json_tensor)
binary_val = sess.run(binary_tensor)
if examples.shape:
self.assertShapeEqual(binary_val, json_tensor)
for input_example, output_binary in zip(np.array(examples).flatten(),
binary_val.flatten()):
output_example = tf.train.Example()
output_example.ParseFromString(output_binary)
self.assertProtoEquals(input_example, output_example)
else:
output_example = tf.train.Example()
output_example.ParseFromString(binary_val)
self.assertProtoEquals(examples.item(), output_example)
def testInvalidSyntax(self):
with self.test_session() as sess:
json_tensor = tf.constant(["{]"])
binary_tensor = tf.decode_json_example(json_tensor)
with self.assertRaisesOpError("Error while parsing JSON"):
sess.run(binary_tensor)
def testInvalidSyntax(self):
with self.test_session() as sess:
json_tensor = tf.constant(["{]"])
binary_tensor = tf.decode_json_example(json_tensor)
with self.assertRaisesOpError("Error while parsing JSON"):
sess.run(binary_tensor)
tf.colocate_with() tf.complex() tf.cond tf.confusion_matrix() tf.conj() tf.cross() tf.cumprod() tf.cumsum() tf.constant() tf.convert_to_tensor() tf.convert_to_tensor_or_indexed_slices() tf.convert_to_tensor_or_sparse_tensor() tf.decode_base64() tf.decode_csv() tf.decode_json_example() tf.decode_raw() tf.device() tf.diag() tf.diag_part() tf.div() tf.divide() tf.batch_to_space_nd() tf.space_to_batch_nd() tf.batch_to_space() tf.space_to_batch() tf.depth_to_space() tf.space_to_depth() tf.dtypes
