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# 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()
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