Add api documentation for `tf.io.serialize_tensor`.
PiperOrigin-RevId: 367541420
Change-Id: I2f95e19ec861d5d8d08f70a46256c900f5c11384
diff --git a/tensorflow/core/api_def/python_api/api_def_SerializeTensor.pbtxt b/tensorflow/core/api_def/python_api/api_def_SerializeTensor.pbtxt
index 219b125..d650655 100644
--- a/tensorflow/core/api_def/python_api/api_def_SerializeTensor.pbtxt
+++ b/tensorflow/core/api_def/python_api/api_def_SerializeTensor.pbtxt
@@ -1,10 +1,4 @@
op {
graph_op_name: "SerializeTensor"
- endpoint {
- name: "io.serialize_tensor"
- }
- endpoint {
- name: "serialize_tensor"
- deprecation_version: 2
- }
+ visibility: HIDDEN
}
diff --git a/tensorflow/python/ops/io_ops.py b/tensorflow/python/ops/io_ops.py
index 763f507..4eb8a3b 100644
--- a/tensorflow/python/ops/io_ops.py
+++ b/tensorflow/python/ops/io_ops.py
@@ -30,6 +30,7 @@
from tensorflow.python.lib.io import python_io
from tensorflow.python.ops import gen_data_flow_ops
from tensorflow.python.ops import gen_io_ops
+from tensorflow.python.ops import gen_parsing_ops
# go/tf-wildcard-import
# pylint: disable=wildcard-import
from tensorflow.python.ops.gen_io_ops import *
@@ -138,6 +139,87 @@
return gen_io_ops.read_file(filename, name)
+@_dispatch.add_dispatch_list
+@tf_export(
+ "io.serialize_tensor", v1=["io.serialize_tensor", "serialize_tensor"])
+def serialize_tensor(tensor, name=None):
+ r"""Transforms a Tensor into a serialized TensorProto proto.
+
+ This operation transforms data in a `tf.Tensor` into a `tf.Tensor` of type
+ `tf.string` containing the data in a binary string format. This operation can
+ transform scalar data and linear arrays, but it is most useful in converting
+ multidimensional arrays into a format accepted by binary storage formats such
+ as a `TFRecord` or `tf.train.Example`.
+
+ See also:
+ - `tf.io.parse_tensor`: inverse operation of `tf.io.serialize_tensor` that
+ transforms a scalar string containing a serialized Tensor into a Tensor of a
+ specified type.
+ - `tf.ensure_shape`: `parse_tensor` cannot statically determine the shape of
+ the parsed tensor. Use `tf.ensure_shape` to set the static shape when running
+ under a `tf.function`
+ - `.SerializeToString`, serializes a proto to a binary-string
+
+ Example of serializing scalar data:
+
+ >>> t = tf.constant(1)
+ >>> tf.io.serialize_tensor(t)
+ <tf.Tensor: shape=(), dtype=string, numpy=b'\x08...\x00'>
+
+ Example of storing non-scalar data into a `tf.train.Example`:
+
+ >>> t1 = [[1, 2]]
+ >>> t2 = [[7, 8]]
+ >>> nonscalar = tf.concat([t1, t2], 0)
+ >>> nonscalar
+ <tf.Tensor: shape=(2, 2), dtype=int32, numpy=
+ array([[1, 2],
+ [7, 8]], dtype=int32)>
+
+ Serialize the data using `tf.io.serialize_tensor`.
+
+ >>> serialized_nonscalar = tf.io.serialize_tensor(nonscalar)
+ >>> serialized_nonscalar
+ <tf.Tensor: shape=(), dtype=string, numpy=b'\x08...\x00'>
+
+ Store the data in a `tf.train.Feature`.
+
+ >>> feature_of_bytes = tf.train.Feature(
+ ... bytes_list=tf.train.BytesList(value=[serialized_nonscalar.numpy()]))
+ >>> feature_of_bytes
+ bytes_list {
+ value: "\010...\000"
+ }
+
+ Put the `tf.train.Feature` message into a `tf.train.Example`.
+
+ >>> features_for_example = {
+ ... 'feature0': feature_of_bytes
+ ... }
+ >>> example_proto = tf.train.Example(
+ ... features=tf.train.Features(feature=features_for_example))
+ >>> example_proto
+ features {
+ feature {
+ key: "feature0"
+ value {
+ bytes_list {
+ value: "\010...\000"
+ }
+ }
+ }
+ }
+
+ Args:
+ tensor: A `tf.Tensor`.
+ name: string. Optional name for the op.
+
+ Returns:
+ A Tensor of dtype string.
+ """
+ return gen_parsing_ops.serialize_tensor(tensor, name)
+
+
@tf_export(v1=["ReaderBase"])
class ReaderBase(object):
"""Base class for different Reader types, that produce a record every step.
