1 files changed, 115 insertions, 0 deletions
diff --git a/tensorflow/contrib/session_bundle/example/export_half_plus_two.py b/tensorflow/contrib/session_bundle/example/export_half_plus_two.py
new file mode 100644
index 0000000000..e4b1947e03
--- /dev/null
+++ b/tensorflow/contrib/session_bundle/example/export_half_plus_two.py
@@ -0,0 +1,115 @@
+# Copyright 2016 Google Inc. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Exports a toy linear regression inference graph.
+
+Exports a TensorFlow graph to /tmp/half_plus_two/ based on the Exporter
+format, go/tf-exporter.
+
+This graph calculates,
+  y = a*x + b
+where a and b are variables with a=0.5 and b=2.
+
+Output from this program is typically used to exercise Session
+loading and execution code.
+"""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import tensorflow as tf
+from tensorflow.contrib.session_bundle import exporter
+
+
+def Export():
+  export_path = "/tmp/half_plus_two"
+  with tf.Session() as sess:
+    # Make model parameters a&b variables instead of constants to
+    # exercise the variable reloading mechanisms.
+    a = tf.Variable(0.5, name="a")
+    b = tf.Variable(2.0, name="b")
+
+    # Calculate, y = a*x + b
+    # here we use a placeholder 'x' which is fed at inference time.
+    x = tf.placeholder(tf.float32, name="x")
+    y = tf.add(tf.mul(a, x), b, name="y")
+
+    # Setup a standard Saver for our variables.
+    save = tf.train.Saver({"a": a, "b": b}, sharded=True)
+
+    # asset_path contains the base directory of assets used in training (e.g.
+    # vocabulary files).
+    original_asset_path = tf.constant("/tmp/original/export/assets")
+    # Ops reading asset files should reference the asset_path tensor
+    # which stores the original asset path at training time and the
+    # overridden assets directory at restore time.
+    asset_path = tf.Variable(original_asset_path,
+                             name="asset_path",
+                             trainable=False,
+                             collections=[])
+    assign_asset_path = asset_path.assign(original_asset_path)
+
+    # Use a fixed global step number.
+    global_step_tensor = tf.Variable(123, name="global_step")
+
+    # Create a RegressionSignature for our input and output.
+    signature = exporter.regression_signature(input_tensor=x, output_tensor=y)
+
+    # Create two filename assets and corresponding tensors.
+    # TODO(b/26254158) Consider adding validation of file existance as well as
+    # hashes (e.g. sha1) for consistency.
+    original_filename1 = tf.constant("hello1.txt")
+    tf.add_to_collection(tf.GraphKeys.ASSET_FILEPATHS, original_filename1)
+    filename1 = tf.Variable(original_filename1,
+                            name="filename1",
+                            trainable=False,
+                            collections=[])
+    assign_filename1 = filename1.assign(original_filename1)
+    original_filename2 = tf.constant("hello2.txt")
+    tf.add_to_collection(tf.GraphKeys.ASSET_FILEPATHS, original_filename2)
+    filename2 = tf.Variable(original_filename2,
+                            name="filename2",
+                            trainable=False,
+                            collections=[])
+    assign_filename2 = filename2.assign(original_filename2)
+
+    # Init op contains a group of all variables that we assign.
+    init_op = tf.group(assign_asset_path, assign_filename1, assign_filename2)
+
+    # CopyAssets is used as a callback during export to copy files to the
+    # given export directory.
+    def CopyAssets(filepaths, export_path):
+      print("copying asset files to: %s" % export_path)
+      for filepath in filepaths:
+        print("copying asset file: %s" % filepath)
+
+    # Run an export.
+    tf.initialize_all_variables().run()
+    export = exporter.Exporter(save)
+    export.init(
+        sess.graph.as_graph_def(),
+        init_op=init_op,
+        default_graph_signature=signature,
+        assets_collection=tf.get_collection(tf.GraphKeys.ASSET_FILEPATHS),
+        assets_callback=CopyAssets)
+    export.export(export_path, global_step_tensor, sess)
+
+
+def main(_):
+  Export()
+
+
+if __name__ == "__main__":
+  tf.app.run()