def logistic_regression_signature_fn(examples, unused_features, predictions):
"""Creates logistic regression signature from given examples and predictions.
Args:
examples: `Tensor`.
unused_features: `dict` of `Tensor`s.
predictions: `dict` of `Tensor`s.
Returns:
Tuple of default classification signature and named signature.
"""
# predictions should have shape [batch_size, 2] where first column is P(Y=0|x)
# while second column is P(Y=1|x). We are only interested in the second
# column for inference.
predictions_shape = predictions.get_shape()
predictions_rank = len(predictions_shape)
if predictions_rank != 2:
logging.fatal(
'Expected predictions to have rank 2, but received predictions with '
'rank: {} and shape: {}'.format(predictions_rank, predictions_shape))
if predictions_shape[1] != 2:
logging.fatal(
'Expected predictions to have 2nd dimension: 2, but received '
'predictions with 2nd dimension: {} and shape: {}. Did you mean to use '
'regression_signature_fn instead?'.format(predictions_shape[1],
predictions_shape))
positive_predictions = predictions[:, 1]
signatures = {}
signatures['regression'] = exporter.regression_signature(examples,
positive_predictions)
return signatures['regression'], signatures
def _regression_signature_fn(examples, unused_features, predictions):
if isinstance(predictions, dict):
score = predictions[PredictionKey.SCORES]
else:
score = predictions
default_signature = exporter.regression_signature(
input_tensor=examples, output_tensor=score)
# TODO(zakaria): add validation
return default_signature, {}
def _regression_signature_fn(examples, features, predictions):
# pylint: disable=missing-docstring
del features
if isinstance(predictions, dict):
score = predictions[prediction_key.PredictionKey.SCORES]
else:
score = predictions
default_signature = exporter.regression_signature(
input_tensor=examples, output_tensor=score)
# TODO(zakaria): add validation
return default_signature, {}
def regression_signature_fn(examples, unused_features, predictions):
"""Creates regression signature from given examples and predictions.
Args:
examples: `Tensor`.
unused_features: `dict` of `Tensor`s.
predictions: `Tensor`.
Returns:
Tuple of default regression signature and empty named signatures.
"""
default_signature = exporter.regression_signature(
input_tensor=examples, output_tensor=predictions)
return default_signature, {}
def logistic_regression_signature_fn(examples, unused_features, predictions):
"""Creates logistic regression signature from given examples and predictions.
Args:
examples: `Tensor`.
unused_features: `dict` of `Tensor`s.
predictions: `Tensor` of shape [batch_size, 2] of predicted probabilities or
dict that contains the probabilities tensor as in
{'probabilities', `Tensor`}.
Returns:
Tuple of default regression signature and named signature.
Raises:
ValueError: If examples is `None`.
"""
if examples is None:
raise ValueError("examples cannot be None when using this signature fn.")
if isinstance(predictions, dict):
predictions_tensor = predictions["probabilities"]
else:
predictions_tensor = predictions
# predictions should have shape [batch_size, 2] where first column is P(Y=0|x)
# while second column is P(Y=1|x). We are only interested in the second
# column for inference.
predictions_shape = predictions_tensor.get_shape()
predictions_rank = len(predictions_shape)
if predictions_rank != 2:
logging.fatal(
"Expected predictions to have rank 2, but received predictions with "
"rank: {} and shape: {}".format(predictions_rank, predictions_shape)
)
if predictions_shape[1] != 2:
logging.fatal(
"Expected predictions to have 2nd dimension: 2, but received "
"predictions with 2nd dimension: {} and shape: {}. Did you mean to use "
"regression_signature_fn or classification_signature_fn_with_prob "
"instead?".format(predictions_shape[1], predictions_shape)
)
positive_predictions = predictions_tensor[:, 1]
default_signature = exporter.regression_signature(input_tensor=examples, output_tensor=positive_predictions)
return default_signature, {}
def logistic_regression_signature_fn(examples, unused_features, predictions):
"""Creates logistic regression signature from given examples and predictions.
Args:
examples: `Tensor`.
unused_features: `dict` of `Tensor`s.
predictions: `Tensor` of shape [batch_size, 2] of predicted probabilities or
dict that contains the probabilities tensor as in
{'probabilities', `Tensor`}.
Returns:
Tuple of default regression signature and named signature.
Raises:
ValueError: If examples is `None`.
"""
if examples is None:
raise ValueError('examples cannot be None when using this signature fn.')
if isinstance(predictions, dict):
predictions_tensor = predictions['probabilities']
else:
predictions_tensor = predictions
# predictions should have shape [batch_size, 2] where first column is P(Y=0|x)
# while second column is P(Y=1|x). We are only interested in the second
# column for inference.
predictions_shape = predictions_tensor.get_shape()
predictions_rank = len(predictions_shape)
if predictions_rank != 2:
logging.fatal(
'Expected predictions to have rank 2, but received predictions with '
'rank: {} and shape: {}'.format(predictions_rank, predictions_shape))
if predictions_shape[1] != 2:
logging.fatal(
'Expected predictions to have 2nd dimension: 2, but received '
'predictions with 2nd dimension: {} and shape: {}. Did you mean to use '
'regression_signature_fn or classification_signature_fn_with_prob '
'instead?'.format(predictions_shape[1], predictions_shape))
positive_predictions = predictions_tensor[:, 1]
default_signature = exporter.regression_signature(
input_tensor=examples, output_tensor=positive_predictions)
return default_signature, {}
def regression_signature_fn(examples, unused_features, predictions):
"""Creates regression signature from given examples and predictions.
Args:
examples: `Tensor`.
unused_features: `dict` of `Tensor`s.
predictions: `Tensor`.
Returns:
Tuple of default regression signature and empty named signatures.
Raises:
ValueError: If examples is `None`.
"""
if examples is None:
raise ValueError("examples cannot be None when using this signature fn.")
default_signature = exporter.regression_signature(input_tensor=examples, output_tensor=predictions)
return default_signature, {}
def regression_signature_fn(examples, unused_features, predictions):
"""Creates regression signature from given examples and predictions.
Args:
examples: `Tensor`.
unused_features: `dict` of `Tensor`s.
predictions: `Tensor`.
Returns:
Tuple of default regression signature and empty named signatures.
Raises:
ValueError: If examples is `None`.
"""
if examples is None:
raise ValueError('examples cannot be None when using this signature fn.')
default_signature = exporter.regression_signature(
input_tensor=examples, output_tensor=predictions)
return default_signature, {}
def logistic_regression_signature_fn(examples, unused_features, predictions):
"""Creates regression signature from given examples and predictions.
Args:
examples: `Tensor`.
unused_features: `dict` of `Tensor`s.
predictions: `dict` of `Tensor`s.
Returns:
Tuple of default classification signature and named signature.
"""
# predictions has shape [batch_size, 2] where first column is P(Y=0|x)
# while second column is P(Y=1|x). We are only interested in the second
# column for inference.
assert predictions.get_shape()[1] == 2
positive_predictions = predictions[:, 1]
signatures = {}
signatures['regression'] = exporter.regression_signature(
examples, positive_predictions)
return signatures['regression'], signatures
def logistic_regression_signature_fn(examples, unused_features, predictions):
"""Creates regression signature from given examples and predictions.
Args:
examples: `Tensor`.
unused_features: `dict` of `Tensor`s.
predictions: `dict` of `Tensor`s.
Returns:
Tuple of default classification signature and named signature.
"""
# predictions has shape [batch_size, 2] where first column is P(Y=0|x)
# while second column is P(Y=1|x). We are only interested in the second
# column for inference.
assert predictions.get_shape()[1] == 2
positive_predictions = predictions[:, 1]
signatures = {}
signatures['regression'] = exporter.regression_signature(examples,
positive_predictions)
return signatures['regression'], signatures
def ExportModel(sess, model_dir, input, output, assets):
if os.path.isdir(model_dir):
shutil.rmtree(model_dir)
# using TF Serving exporter to load into a TF Serving session bundle
logging.info('Exporting trained model to %s', model_dir)
saver = tf.train.Saver()
model_exporter = exporter.Exporter(saver)
signature = exporter.regression_signature(input_tensor=input,
output_tensor=output)
model_exporter.init(sess.graph.as_graph_def(),
default_graph_signature=signature,
assets_collection=assets)
model_exporter.export(model_dir, tf.constant(1), sess)
# using a SummaryWriter so graph can be loaded in TensorBoard
writer = tf.train.SummaryWriter(model_dir, sess.graph)
writer.flush()
# exporting the graph as a text protobuf, to view graph manualy
f1 = open(model_dir + '/graph.pbtxt', 'w+')
print >> f1, str(tf.get_default_graph().as_graph_def())
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)
b = tf.Variable(2.0)
# Calculate, y = a*x + b
# here we use a placeholder 'x' which is fed at inference time.
x = tf.placeholder(tf.float32)
y = tf.add(tf.multiply(a, x), b)
# Run an export.
tf.initialize_all_variables().run()
export = exporter.Exporter(tf.train.Saver())
export.init(named_graph_signatures={
"inputs": exporter.generic_signature({"x": x}),
"outputs": exporter.generic_signature({"y": y}),
"regress": exporter.regression_signature(x, y)
})
export.export(export_path, tf.constant(123), sess)
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)
b = tf.Variable(2.0)
# Calculate, y = a*x + b
# here we use a placeholder 'x' which is fed at inference time.
x = tf.placeholder(tf.float32)
y = tf.add(tf.mul(a, x), b)
# Run an export.
tf.initialize_all_variables().run()
export = exporter.Exporter(tf.train.Saver())
export.init(
named_graph_signatures={
"inputs": exporter.generic_signature({"x": x}),
"outputs": exporter.generic_signature({"y": y}),
"regress": exporter.regression_signature(x, y)
})
export.export(export_path, tf.constant(123), sess)
def doBasicsOneExportPath(self,
export_path,
clear_devices=False,
global_step=GLOBAL_STEP,
sharded=True):
# Build a graph with 2 parameter nodes on different devices.
tf.reset_default_graph()
with tf.Session(target="",
config=config_pb2.ConfigProto(
device_count={"CPU": 2})) as sess:
# v2 is an unsaved variable derived from v0 and v1. It is used to
# exercise the ability to run an init op when restoring a graph.
with sess.graph.device("/cpu:0"):
v0 = tf.Variable(10, name="v0")
with sess.graph.device("/cpu:1"):
v1 = tf.Variable(20, name="v1")
v2 = tf.Variable(1, name="v2", trainable=False, collections=[])
assign_v2 = tf.assign(v2, tf.add(v0, v1))
init_op = tf.group(assign_v2, name="init_op")
tf.add_to_collection("v", v0)
tf.add_to_collection("v", v1)
tf.add_to_collection("v", v2)
global_step_tensor = tf.Variable(global_step, name="global_step")
named_tensor_bindings = {
"logical_input_A": v0,
"logical_input_B": v1
}
signatures = {
"foo":
exporter.regression_signature(input_tensor=v0,
output_tensor=v1),
"generic":
exporter.generic_signature(named_tensor_bindings)
}
asset_filepath_orig = os.path.join(tf.test.get_temp_dir(),
"hello42.txt")
asset_file = tf.constant(asset_filepath_orig, name="filename42")
tf.add_to_collection(tf.GraphKeys.ASSET_FILEPATHS, asset_file)
with gfile.FastGFile(asset_filepath_orig, "w") as f:
f.write("your data here")
assets_collection = tf.get_collection(tf.GraphKeys.ASSET_FILEPATHS)
ignored_asset = os.path.join(tf.test.get_temp_dir(), "ignored.txt")
with gfile.FastGFile(ignored_asset, "w") as f:
f.write("additional data here")
tf.initialize_all_variables().run()
# Run an export.
save = tf.train.Saver({
"v0": v0,
"v1": v1
},
restore_sequentially=True,
sharded=sharded)
export = exporter.Exporter(save)
export.init(
sess.graph.as_graph_def(),
init_op=init_op,
clear_devices=clear_devices,
default_graph_signature=exporter.classification_signature(
input_tensor=v0),
named_graph_signatures=signatures,
assets_collection=assets_collection)
export.export(export_path,
global_step_tensor,
sess,
exports_to_keep=gc.largest_export_versions(2))
# Restore graph.
compare_def = tf.get_default_graph().as_graph_def()
tf.reset_default_graph()
with tf.Session(target="",
config=config_pb2.ConfigProto(
device_count={"CPU": 2})) as sess:
save = tf.train.import_meta_graph(
os.path.join(export_path,
constants.VERSION_FORMAT_SPECIFIER % global_step,
constants.META_GRAPH_DEF_FILENAME))
self.assertIsNotNone(save)
meta_graph_def = save.export_meta_graph()
collection_def = meta_graph_def.collection_def
# Validate custom graph_def.
graph_def_any = collection_def[constants.GRAPH_KEY].any_list.value
self.assertEquals(len(graph_def_any), 1)
graph_def = tf.GraphDef()
graph_def_any[0].Unpack(graph_def)
if clear_devices:
for node in compare_def.node:
node.device = ""
self.assertProtoEquals(compare_def, graph_def)
# Validate init_op.
init_ops = collection_def[constants.INIT_OP_KEY].node_list.value
self.assertEquals(len(init_ops), 1)
self.assertEquals(init_ops[0], "init_op")
# Validate signatures.
signatures_any = collection_def[
constants.SIGNATURES_KEY].any_list.value
self.assertEquals(len(signatures_any), 1)
signatures = manifest_pb2.Signatures()
signatures_any[0].Unpack(signatures)
default_signature = signatures.default_signature
self.assertEqual(
default_signature.classification_signature.input.tensor_name,
"v0:0")
bindings = signatures.named_signatures[
"generic"].generic_signature.map
self.assertEquals(bindings["logical_input_A"].tensor_name, "v0:0")
self.assertEquals(bindings["logical_input_B"].tensor_name, "v1:0")
read_foo_signature = (
signatures.named_signatures["foo"].regression_signature)
self.assertEquals(read_foo_signature.input.tensor_name, "v0:0")
self.assertEquals(read_foo_signature.output.tensor_name, "v1:0")
# Validate the assets.
assets_any = collection_def[constants.ASSETS_KEY].any_list.value
self.assertEquals(len(assets_any), 1)
asset = manifest_pb2.AssetFile()
assets_any[0].Unpack(asset)
assets_path = os.path.join(
export_path, constants.VERSION_FORMAT_SPECIFIER % global_step,
constants.ASSETS_DIRECTORY, "hello42.txt")
asset_contents = gfile.GFile(assets_path).read()
self.assertEqual(asset_contents, "your data here")
self.assertEquals("hello42.txt", asset.filename)
self.assertEquals("filename42:0", asset.tensor_binding.tensor_name)
ignored_asset_path = os.path.join(
export_path, constants.VERSION_FORMAT_SPECIFIER % global_step,
constants.ASSETS_DIRECTORY, "ignored.txt")
self.assertFalse(gfile.Exists(ignored_asset_path))
# Validate graph restoration.
if sharded:
save.restore(
sess,
os.path.join(
export_path,
constants.VERSION_FORMAT_SPECIFIER % global_step,
constants.VARIABLES_FILENAME_PATTERN))
else:
save.restore(
sess,
os.path.join(
export_path,
constants.VERSION_FORMAT_SPECIFIER % global_step,
constants.VARIABLES_FILENAME))
self.assertEqual(10, tf.get_collection("v")[0].eval())
self.assertEqual(20, tf.get_collection("v")[1].eval())
tf.get_collection(constants.INIT_OP_KEY)[0].run()
self.assertEqual(30, tf.get_collection("v")[2].eval())
def doBasicsOneExportPath(self,
export_path,
clear_devices=False,
global_step=GLOBAL_STEP,
sharded=True):
# Build a graph with 2 parameter nodes on different devices.
tf.reset_default_graph()
with tf.Session(
target="",
config=config_pb2.ConfigProto(device_count={"CPU": 2})) as sess:
# v2 is an unsaved variable derived from v0 and v1. It is used to
# exercise the ability to run an init op when restoring a graph.
with sess.graph.device("/cpu:0"):
v0 = tf.Variable(10, name="v0")
with sess.graph.device("/cpu:1"):
v1 = tf.Variable(20, name="v1")
v2 = tf.Variable(1, name="v2", trainable=False, collections=[])
assign_v2 = tf.assign(v2, tf.add(v0, v1))
init_op = tf.group(assign_v2, name="init_op")
tf.add_to_collection("v", v0)
tf.add_to_collection("v", v1)
tf.add_to_collection("v", v2)
global_step_tensor = tf.Variable(global_step, name="global_step")
named_tensor_bindings = {"logical_input_A": v0, "logical_input_B": v1}
signatures = {
"foo": exporter.regression_signature(input_tensor=v0,
output_tensor=v1),
"generic": exporter.generic_signature(named_tensor_bindings)
}
asset_filepath_orig = os.path.join(tf.test.get_temp_dir(), "hello42.txt")
asset_file = tf.constant(asset_filepath_orig, name="filename42")
tf.add_to_collection(tf.GraphKeys.ASSET_FILEPATHS, asset_file)
with gfile.FastGFile(asset_filepath_orig, "w") as f:
f.write("your data here")
assets_collection = tf.get_collection(tf.GraphKeys.ASSET_FILEPATHS)
ignored_asset = os.path.join(tf.test.get_temp_dir(), "ignored.txt")
with gfile.FastGFile(ignored_asset, "w") as f:
f.write("additional data here")
tf.initialize_all_variables().run()
# Run an export.
save = tf.train.Saver({"v0": v0,
"v1": v1},
restore_sequentially=True,
sharded=sharded)
export = exporter.Exporter(save)
export.init(sess.graph.as_graph_def(),
init_op=init_op,
clear_devices=clear_devices,
default_graph_signature=exporter.classification_signature(
input_tensor=v0),
named_graph_signatures=signatures,
assets_collection=assets_collection)
export.export(export_path,
global_step_tensor,
sess,
exports_to_keep=gc.largest_export_versions(2))
# Restore graph.
compare_def = tf.get_default_graph().as_graph_def()
tf.reset_default_graph()
with tf.Session(
target="",
config=config_pb2.ConfigProto(device_count={"CPU": 2})) as sess:
save = tf.train.import_meta_graph(
os.path.join(export_path, constants.VERSION_FORMAT_SPECIFIER %
global_step, constants.META_GRAPH_DEF_FILENAME))
self.assertIsNotNone(save)
meta_graph_def = save.export_meta_graph()
collection_def = meta_graph_def.collection_def
# Validate custom graph_def.
graph_def_any = collection_def[constants.GRAPH_KEY].any_list.value
self.assertEquals(len(graph_def_any), 1)
graph_def = tf.GraphDef()
graph_def_any[0].Unpack(graph_def)
if clear_devices:
for node in compare_def.node:
node.device = ""
self.assertProtoEquals(compare_def, graph_def)
# Validate init_op.
init_ops = collection_def[constants.INIT_OP_KEY].node_list.value
self.assertEquals(len(init_ops), 1)
self.assertEquals(init_ops[0], "init_op")
# Validate signatures.
signatures_any = collection_def[constants.SIGNATURES_KEY].any_list.value
self.assertEquals(len(signatures_any), 1)
signatures = manifest_pb2.Signatures()
signatures_any[0].Unpack(signatures)
default_signature = signatures.default_signature
self.assertEqual(
default_signature.classification_signature.input.tensor_name, "v0:0")
bindings = signatures.named_signatures["generic"].generic_signature.map
self.assertEquals(bindings["logical_input_A"].tensor_name, "v0:0")
self.assertEquals(bindings["logical_input_B"].tensor_name, "v1:0")
read_foo_signature = (
signatures.named_signatures["foo"].regression_signature)
self.assertEquals(read_foo_signature.input.tensor_name, "v0:0")
self.assertEquals(read_foo_signature.output.tensor_name, "v1:0")
# Validate the assets.
assets_any = collection_def[constants.ASSETS_KEY].any_list.value
self.assertEquals(len(assets_any), 1)
asset = manifest_pb2.AssetFile()
assets_any[0].Unpack(asset)
assets_path = os.path.join(export_path,
constants.VERSION_FORMAT_SPECIFIER %
global_step, constants.ASSETS_DIRECTORY,
"hello42.txt")
asset_contents = gfile.GFile(assets_path).read()
self.assertEqual(asset_contents, "your data here")
self.assertEquals("hello42.txt", asset.filename)
self.assertEquals("filename42:0", asset.tensor_binding.tensor_name)
ignored_asset_path = os.path.join(export_path,
constants.VERSION_FORMAT_SPECIFIER %
global_step, constants.ASSETS_DIRECTORY,
"ignored.txt")
self.assertFalse(gfile.Exists(ignored_asset_path))
# Validate graph restoration.
if sharded:
save.restore(sess,
os.path.join(
export_path, constants.VERSION_FORMAT_SPECIFIER %
global_step, constants.VARIABLES_FILENAME_PATTERN))
else:
save.restore(sess,
os.path.join(
export_path, constants.VERSION_FORMAT_SPECIFIER %
global_step, constants.VARIABLES_FILENAME))
self.assertEqual(10, tf.get_collection("v")[0].eval())
self.assertEqual(20, tf.get_collection("v")[1].eval())
tf.get_collection(constants.INIT_OP_KEY)[0].run()
self.assertEqual(30, tf.get_collection("v")[2].eval())
def Export():
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")
# Create a placeholder for serialized tensorflow.Example messages to be fed.
serialized_tf_example = tf.placeholder(tf.string, name="tf_example")
# Parse the tensorflow.Example looking for a feature named "x" with a single
# floating point value.
feature_configs = {
"x": tf.FixedLenFeature([1], dtype=tf.float32),
}
tf_example = tf.parse_example(serialized_tf_example, feature_configs)
# Use tf.identity() to assign name
x = tf.identity(tf_example["x"], name="x")
# Calculate, y = a*x + b
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,
write_version=tf.train.SaverDef.V2
if FLAGS.use_checkpoint_v2 else tf.train.SaverDef.V1)
# 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.
regression_signature = exporter.regression_signature(
input_tensor=serialized_tf_example,
# Use tf.identity here because we export two signatures here.
# Otherwise only graph for one of the signatures will be loaded
# (whichever is created first) during serving.
output_tensor=tf.identity(y))
named_graph_signature = {
"inputs": exporter.generic_signature({"x": x}),
"outputs": exporter.generic_signature({"y": 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=regression_signature,
named_graph_signatures=named_graph_signature,
assets_collection=tf.get_collection(
tf.GraphKeys.ASSET_FILEPATHS),
assets_callback=CopyAssets)
export.export(FLAGS.export_dir, global_step_tensor, sess)
def Export():
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")
# Create a placeholder for serialized tensorflow.Example messages to be fed.
serialized_tf_example = tf.placeholder(tf.string, name="tf_example")
# Parse the tensorflow.Example looking for a feature named "x" with a single
# floating point value.
feature_configs = {"x": tf.FixedLenFeature([1], dtype=tf.float32),}
tf_example = tf.parse_example(serialized_tf_example, feature_configs)
# Use tf.identity() to assign name
x = tf.identity(tf_example["x"], name="x")
# Calculate, y = a*x + b
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,
write_version=tf.train.SaverDef.V2 if FLAGS.use_checkpoint_v2 else
tf.train.SaverDef.V1)
# 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.
regression_signature = exporter.regression_signature(
input_tensor=serialized_tf_example,
# Use tf.identity here because we export two signatures here.
# Otherwise only graph for one of the signatures will be loaded
# (whichever is created first) during serving.
output_tensor=tf.identity(y))
named_graph_signature = {
"inputs": exporter.generic_signature({"x": x}),
"outputs": exporter.generic_signature({"y": 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=regression_signature,
named_graph_signatures=named_graph_signature,
assets_collection=tf.get_collection(tf.GraphKeys.ASSET_FILEPATHS),
assets_callback=CopyAssets)
export.export(FLAGS.export_dir, global_step_tensor, sess)
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 train(mnist):
x = tf.placeholder(tf.float32, [None, INPUT_NODE], name='x-input')
y_ = tf.placeholder(tf.float32, [None, OUTPUT_NODE], name='y-input')
regularizer = tf.contrib.layers.l2_regularizer(REGULARIZATION_RATE)
y = inference(x, regularizer)
global_step = tf.Variable(0, trainable=False)
variable_averages = \
tf.train.ExponentialMovingAverage \
(MOVING_AVERAGE_DECAY, global_step)
variables_averages_op = \
variable_averages.apply \
(tf.trainable_variables())
cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits \
(logits=y, labels=tf.argmax(y_, 1))
cross_entropy_mean = tf.reduce_mean(cross_entropy)
loss = cross_entropy_mean + tf.add_n(tf.get_collection('losses'))
learning_rate = tf.train.exponential_decay(LEARNING_RATE_BASE,
global_step,
mnist.train.num_examples /
BATCH_SIZE,
LEARNING_RATE_DECAY,
staircase=True)
train_step = \
tf.train.GradientDescentOptimizer(learning_rate) \
.minimize(loss, global_step=global_step)
with tf.control_dependencies([train_step, variables_averages_op]):
train_op = tf.no_op(name='train')
saver = tf.train.Saver()
with tf.Session() as sess:
tf.global_variables_initializer().run()
export = exporter.Exporter(saver)
for i in range(TRAINING_STEPS):
xs, ys = mnist.train.next_batch(BATCH_SIZE)
_, loss_value, step = sess.run([train_op, loss, global_step],
feed_dict={
x: xs,
y_: ys
})
if i % 1000 == 0:
print(
"After %d training step(s), loss on training batch is %g."
% (step, loss_value))
saver.save(sess,
os.path.join(MODEL_SAVE_PATH, MODEL_NAME),
global_step=global_step)
saver.export_meta_graph(os.path.join(MODEL_SAVE_PATH, MODEL_NAME) +
".json",
as_text=True)
export.init(
named_graph_signatures={
"inputs":
exporter.generic_signature({"input_matrix": x}),
"outputs":
exporter.generic_signature({"output_lable": tf.argmax(y, 1)}),
"regress":
exporter.regression_signature(x, y)
})
export.export(MODEL_SAVE_PATH + MODEL_NAME, tf.constant(123), sess)
def _regression_signature(examples, unused_features, predictions):
signatures = {}
signatures['regression'] = (exporter.regression_signature(
examples, predictions))
return signatures['regression'], signatures
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 _regression_signature(examples, unused_features, predictions):
signatures = {}
signatures['regression'] = (
exporter.regression_signature(examples, predictions))
return signatures['regression'], signatures