def do_inference(hostport, work_dir, concurrency, num_tests):
"""Tests PredictionService with concurrent requests.
Args:
hostport: Host:port address of the PredictionService.
work_dir: The full path of working directory for test data set.
concurrency: Maximum number of concurrent requests.
num_tests: Number of test images to use.
Returns:
The classification error rate.
Raises:
IOError: An error occurred processing test data set.
"""
test_data_set = mnist_input_data.read_data_sets(work_dir).test
host, port = hostport.split(':')
channel = implementations.insecure_channel(host, int(port))
stub = prediction_service_pb2.beta_create_PredictionService_stub(channel)
result_counter = _ResultCounter(num_tests, concurrency)
for _ in range(num_tests):
request = predict_pb2.PredictRequest()
request.model_spec.name = 'mnist'
image, label = test_data_set.next_batch(1)
request.inputs['images'].CopyFrom(
tf.contrib.util.make_tensor_proto(image[0],
shape=[1, image[0].size]))
result_counter.throttle()
result_future = stub.Predict.future(request, 5.0) # 5 seconds
result_future.add_done_callback(
_create_rpc_callback(label[0], result_counter))
return result_counter.get_error_rate()
def do_inference(hostport, work_dir, concurrency, num_tests):
"""Tests PredictionService with concurrent requests.
Args:
hostport: Host:port address of the PredictionService.
work_dir: The full path of working directory for test data set.
concurrency: Maximum number of concurrent requests.
num_tests: Number of test images to use.
Returns:
The classification error rate.
Raises:
IOError: An error occurred processing test data set.
"""
test_data_set = mnist_input_data.read_data_sets(work_dir).test
host, port = hostport.split(':')
channel = implementations.insecure_channel(host, int(port))
stub = prediction_service_pb2.beta_create_PredictionService_stub(channel)
result_counter = _ResultCounter(num_tests, concurrency)
for _ in range(num_tests):
request = predict_pb2.PredictRequest()
request.model_spec.name = 'mnist'
request.model_spec.signature_name = 'predict_images'
image, label = test_data_set.next_batch(1)
request.inputs['images'].CopyFrom(
tf.contrib.util.make_tensor_proto(image[0], shape=[1, image[0].size]))
result_counter.throttle()
result_future = stub.Predict.future(request, 5.0) # 5 seconds
result_future.add_done_callback(
_create_rpc_callback(label[0], result_counter))
return result_counter.get_error_rate()
def do_inference(hostport, work_dir, concurrency, num_tests):
"""Tests mnist_inference service with concurrent requests.
Args:
hostport: Host:port address of the mnist_inference service.
work_dir: The full path of working directory for test data set.
concurrency: Maximum number of concurrent requests.
num_tests: Number of test images to use.
Returns:
The classification error rate.
Raises:
IOError: An error occurred processing test data set.
"""
test_data_set = mnist_input_data.read_data_sets(work_dir).test
host, port = hostport.split(':')
channel = implementations.insecure_channel(host, int(port))
stub = prediction_service_pb2.beta_create_PredictionService_stub(channel)
cv = threading.Condition()
result = {'active': 0, 'error': 0, 'done': 0}
def done(result_future, label):
with cv:
# Workaround for gRPC issue https://github.com/grpc/grpc/issues/7133
try:
exception = result_future.exception()
except AttributeError:
exception = None
if exception:
result['error'] += 1
print exception
else:
sys.stdout.write('.')
sys.stdout.flush()
response = numpy.array(result_future.result().outputs['scores'])
#argmax => Returns the indices of the maximum values along an axis.
prediction = numpy.argmax(response)
if label != prediction:
result['error'] += 1
result['done'] += 1
result['active'] -= 1
cv.notify()
for _ in range(num_tests):
request = predict_pb2.PredictRequest()
request.model_spec.name = 'mnist'
image, label = test_data_set.next_batch(1)
request.inputs['images'].CopyFrom(
tf.contrib.util.make_tensor_proto(image[0], shape=[1, image[0].size]))
with cv:
while result['active'] == concurrency:
cv.wait()
result['active'] += 1
#Kar: Call setup (made) to the server here! 'stub' holds the server config
result_future = stub.Predict.future(request, 5.0) # 5 seconds
result_future.add_done_callback(
lambda result_future, l=label[0]: done(result_future, l)) # pylint: disable=cell-var-from-loop
with cv:
while result['done'] != num_tests:
cv.wait()
return result['error'] / float(num_tests)
def do_inference(hostport, work_dir, concurrency, num_tests):
"""Tests mnist_inference service with concurrent requests.
Args:
hostport: Host:port address of the mnist_inference service.
work_dir: The full path of working directory for test data set.
concurrency: Maximum number of concurrent requests.
num_tests: Number of test images to use.
Returns:
The classification error rate.
Raises:
IOError: An error occurred processing test data set.
"""
test_data_set = mnist_input_data.read_data_sets(work_dir).test
host, port = hostport.split(':')
channel = implementations.insecure_channel(host, int(port))
stub = prediction_service_pb2.beta_create_PredictionService_stub(channel)
cv = threading.Condition()
result = {'active': 0, 'error': 0, 'done': 0}
def done(result_future, label):
with cv:
# Workaround for gRPC issue https://github.com/grpc/grpc/issues/7133
try:
exception = result_future.exception()
except AttributeError:
exception = None
if exception:
result['error'] += 1
print exception
else:
sys.stdout.write('.')
sys.stdout.flush()
response = numpy.array(result_future.result().outputs['scores'])
prediction = numpy.argmax(response)
if label != prediction:
result['error'] += 1
result['done'] += 1
result['active'] -= 1
cv.notify()
for _ in range(num_tests):
request = predict_pb2.PredictRequest()
request.model_spec.name = 'mnist'
image, label = test_data_set.next_batch(1)
request.inputs['images'].CopyFrom(
tf.contrib.util.make_tensor_proto(image[0], shape=[1, image[0].size]))
with cv:
while result['active'] == concurrency:
cv.wait()
result['active'] += 1
result_future = stub.Predict.future(request, 5.0) # 5 seconds
result_future.add_done_callback(
lambda result_future, l=label[0]: done(result_future, l)) # pylint: disable=cell-var-from-loop
with cv:
while result['done'] != num_tests:
cv.wait()
return result['error'] / float(num_tests)
def do_inference(hostport, work_dir, concurrency, num_tests):
"""Tests mnist_inference service with concurrent requests.
Args:
hostport: Host:port address of the mnist_inference service.
work_dir: The full path of working directory for test data set.
concurrency: Maximum number of concurrent requests.
num_tests: Number of test images to use.
Returns:
The classification error rate.
Raises:
IOError: An error occurred processing test data set.
"""
test_data_set = mnist_input_data.read_data_sets(work_dir).test
host, port = hostport.split(':')
channel = implementations.insecure_channel(host, int(port))
stub = mnist_inference_pb2.beta_create_MnistService_stub(channel)
cv = threading.Condition()
result = {'active': 0, 'error': 0, 'done': 0}
def done(result_future, label):
with cv:
# Workaround for gRPC issue https://github.com/grpc/grpc/issues/7133
try:
exception = result_future.exception()
except AttributeError:
exception = None
if exception:
result['error'] += 1
print exception
else:
sys.stdout.write('.')
sys.stdout.flush()
response = numpy.array(result_future.result().value)
prediction = numpy.argmax(response)
if label != prediction:
result['error'] += 1
result['done'] += 1
result['active'] -= 1
cv.notify()
for _ in range(num_tests):
request = mnist_inference_pb2.MnistRequest()
image, label = test_data_set.next_batch(1)
for pixel in image[0]:
request.image_data.append(pixel.item())
with cv:
while result['active'] == concurrency:
cv.wait()
result['active'] += 1
result_future = stub.Classify.future(request, 5.0) # 5 seconds
result_future.add_done_callback(
lambda result_future, l=label[0]: done(result_future, l)) # pylint: disable=cell-var-from-loop
with cv:
while result['done'] != num_tests:
cv.wait()
return result['error'] / float(num_tests)
def main(_):
if len(sys.argv) < 2 or sys.argv[-1].startswith('-'):
print(
'Usage: mnist_export.py [--training_iteration=x] '
'[--export_version=y] export_dir')
sys.exit(-1)
if FLAGS.training_iteration <= 0:
print 'Please specify a positive value for training iteration.'
sys.exit(-1)
if FLAGS.export_version <= 0:
print 'Please specify a positive value for version number.'
sys.exit(-1)
# Train model
print 'Training model...'
mnist = mnist_input_data.read_data_sets(FLAGS.work_dir, one_hot=True)
sess = tf.InteractiveSession()
x = tf.placeholder('float', shape=[None, 784])
y_ = tf.placeholder('float', shape=[None, 10])
w = tf.Variable(tf.zeros([784, 10]))
b = tf.Variable(tf.zeros([10]))
sess.run(tf.initialize_all_variables())
y = tf.nn.softmax(tf.matmul(x, w) + b)
cross_entropy = -tf.reduce_sum(y_ * tf.log(y))
train_step = tf.train.GradientDescentOptimizer(0.01).minimize(
cross_entropy)
for _ in range(FLAGS.training_iteration):
batch = mnist.train.next_batch(50)
train_step.run(feed_dict={x: batch[0], y_: batch[1]})
correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, 'float'))
print 'training accuracy %g' % sess.run(accuracy,
feed_dict={
x: mnist.test.images,
y_: mnist.test.labels
})
print 'Done training!'
# Export model
# WARNING(break-tutorial-inline-code): The following code snippet is
# in-lined in tutorials, please update tutorial documents accordingly
# whenever code changes.
export_path = sys.argv[-1]
print 'Exporting trained model to', export_path
saver = tf.train.Saver(sharded=True)
model_exporter = exporter.Exporter(saver)
model_exporter.init(sess.graph.as_graph_def(),
named_graph_signatures={
'inputs': exporter.generic_signature({'images':
x}),
'outputs':
exporter.generic_signature({'scores': y})
})
model_exporter.export(export_path, tf.constant(FLAGS.export_version), sess)
print 'Done exporting!'
def do_inference(hostport, work_dir, concurrency, num_tests):
"""Tests mnist_inference service with concurrent requests.
Args:
hostport: Host:port address of the mnist_inference service.
work_dir: The full path of working directory for test data set.
concurrency: Maximum number of concurrent requests.
num_tests: Number of test images to use.
Returns:
The classification error rate.
Raises:
IOError: An error occurred processing test data set.
"""
test_data_set = mnist_input_data.read_data_sets(work_dir).test
host, port = hostport.split(':')
channel = implementations.insecure_channel(host, int(port))
stub = mnist_inference_pb2.beta_create_MnistService_stub(channel)
cv = threading.Condition()
result = {'active': 0, 'error': 0, 'done': 0}
def done(result_future, label):
with cv:
exception = result_future.exception()
if exception:
result['error'] += 1
print exception
else:
sys.stdout.write('.')
sys.stdout.flush()
response = numpy.array(result_future.result().value)
prediction = numpy.argmax(response)
if label != prediction:
result['error'] += 1
result['done'] += 1
result['active'] -= 1
cv.notify()
for _ in range(num_tests):
request = mnist_inference_pb2.MnistRequest()
image, label = test_data_set.next_batch(1)
for pixel in image[0]:
request.image_data.append(pixel.item())
with cv:
while result['active'] == concurrency:
cv.wait()
result['active'] += 1
result_future = stub.Classify.future(request, 5.0) # 5 seconds
result_future.add_done_callback(
lambda result_future, l=label[0]: done(result_future, l)) # pylint: disable=cell-var-from-loop
with cv:
while result['done'] != num_tests:
cv.wait()
return result['error'] / float(num_tests)
def main(_):
if len(sys.argv) < 2 or sys.argv[-1].startswith('-'):
print('Usage: mnist_export.py [--training_iteration=x] '
'[--export_version=y] export_dir')
sys.exit(-1)
if FLAGS.training_iteration <= 0:
print 'Please specify a positive value for training iteration.'
sys.exit(-1)
if FLAGS.export_version <= 0:
print 'Please specify a positive value for version number.'
sys.exit(-1)
# Train model
print 'Training model...'
mnist = mnist_input_data.read_data_sets(FLAGS.work_dir, one_hot=True)
sess = tf.InteractiveSession()
x = tf.placeholder('float', shape=[None, 784])
y_ = tf.placeholder('float', shape=[None, 10])
w = tf.Variable(tf.zeros([784, 10]))
b = tf.Variable(tf.zeros([10]))
sess.run(tf.initialize_all_variables())
y = tf.nn.softmax(tf.matmul(x, w) + b)
cross_entropy = -tf.reduce_sum(y_ * tf.log(y))
train_step = tf.train.GradientDescentOptimizer(0.01).minimize(cross_entropy)
for _ in range(FLAGS.training_iteration):
batch = mnist.train.next_batch(50)
train_step.run(feed_dict={x: batch[0], y_: batch[1]})
correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, 'float'))
print 'training accuracy %g' % sess.run(accuracy,
feed_dict={x: mnist.test.images,
y_: mnist.test.labels})
print 'Done training!'
# Export model
# WARNING(break-tutorial-inline-code): The following code snippet is
# in-lined in tutorials, please update tutorial documents accordingly
# whenever code changes.
export_path = sys.argv[-1]
print 'Exporting trained model to', export_path
saver = tf.train.Saver(sharded=True)
model_exporter = exporter.Exporter(saver)
model_exporter.init(
sess.graph.as_graph_def(),
named_graph_signatures={
'inputs': exporter.generic_signature({'images': x}),
'outputs': exporter.generic_signature({'scores': y})})
model_exporter.export(export_path, tf.constant(FLAGS.export_version), sess)
print 'Done exporting!'
def main(_):
if len(sys.argv) < 2 or sys.argv[-1].startswith('-'):
print(
'Usage: mnist_export.py [--training_iteration=x] '
'[--model_version=y] export_dir')
sys.exit(-1)
if FLAGS.training_iteration <= 0:
print 'Please specify a positive value for training iteration.'
sys.exit(-1)
if FLAGS.model_version <= 0:
print 'Please specify a positive value for version number.'
sys.exit(-1)
# Train model
print 'Training model...'
mnist = mnist_input_data.read_data_sets(FLAGS.work_dir, one_hot=True)
sess = tf.InteractiveSession()
serialized_tf_example = tf.placeholder(tf.string, name='tf_example')
feature_configs = {
'x': tf.FixedLenFeature(shape=[784], dtype=tf.float32),
}
tf_example = tf.parse_example(serialized_tf_example, feature_configs)
x = tf.identity(tf_example['x'],
name='x') # use tf.identity() to assign name
y_ = tf.placeholder('float', shape=[None, 10])
w = tf.Variable(tf.zeros([784, 10]))
b = tf.Variable(tf.zeros([10]))
sess.run(tf.global_variables_initializer())
y = tf.nn.softmax(tf.matmul(x, w) + b, name='y')
cross_entropy = -tf.reduce_sum(y_ * tf.log(y))
train_step = tf.train.GradientDescentOptimizer(0.01).minimize(
cross_entropy)
values, indices = tf.nn.top_k(y, 10)
table = tf.contrib.lookup.index_to_string_table_from_tensor(
tf.constant([str(i) for i in xrange(10)]))
prediction_classes = table.lookup(tf.to_int64(indices))
for _ in range(FLAGS.training_iteration):
batch = mnist.train.next_batch(50)
train_step.run(feed_dict={x: batch[0], y_: batch[1]})
correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, 'float'))
print 'training accuracy %g' % sess.run(accuracy,
feed_dict={
x: mnist.test.images,
y_: mnist.test.labels
})
print 'Done training!'
# Export model
# WARNING(break-tutorial-inline-code): The following code snippet is
# in-lined in tutorials, please update tutorial documents accordingly
# whenever code changes.
export_path_base = sys.argv[-1]
export_path = os.path.join(compat.as_bytes(export_path_base),
compat.as_bytes(str(FLAGS.model_version)))
print 'Exporting trained model to', export_path
builder = saved_model_builder.SavedModelBuilder(export_path)
# Build the signature_def_map.
classification_inputs = utils.build_tensor_info(serialized_tf_example)
classification_outputs_classes = utils.build_tensor_info(
prediction_classes)
classification_outputs_scores = utils.build_tensor_info(values)
classification_signature = signature_def_utils.build_signature_def(
inputs={signature_constants.CLASSIFY_INPUTS: classification_inputs},
outputs={
signature_constants.CLASSIFY_OUTPUT_CLASSES:
classification_outputs_classes,
signature_constants.CLASSIFY_OUTPUT_SCORES:
classification_outputs_scores
},
method_name=signature_constants.CLASSIFY_METHOD_NAME)
tensor_info_x = utils.build_tensor_info(x)
tensor_info_y = utils.build_tensor_info(y)
prediction_signature = signature_def_utils.build_signature_def(
inputs={'images': tensor_info_x},
outputs={'scores': tensor_info_y},
method_name=signature_constants.PREDICT_METHOD_NAME)
legacy_init_op = tf.group(tf.tables_initializer(), name='legacy_init_op')
builder.add_meta_graph_and_variables(
sess, [tag_constants.SERVING],
signature_def_map={
'predict_images':
prediction_signature,
signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY:
classification_signature,
},
legacy_init_op=legacy_init_op)
builder.save()
print 'Done exporting!'
def main(_):
if len(sys.argv) < 2 or sys.argv[-1].startswith('-'):
print(
'Usage: mnist_export.py [--training_iteration=x] '
'[--export_version=y] export_dir')
sys.exit(-1)
if FLAGS.training_iteration <= 0:
print 'Please specify a positive value for training iteration.'
sys.exit(-1)
if FLAGS.export_version <= 0:
print 'Please specify a positive value for version number.'
sys.exit(-1)
# Train model
print 'Training model...'
mnist = mnist_input_data.read_data_sets(FLAGS.work_dir, one_hot=True)
sess = tf.InteractiveSession()
serialized_tf_example = tf.placeholder(tf.string, name='tf_example')
feature_configs = {
'x': tf.FixedLenFeature(shape=[784], dtype=tf.float32),
}
tf_example = tf.parse_example(serialized_tf_example, feature_configs)
x = tf_example['x']
y_ = tf.placeholder('float', shape=[None, 10])
w = tf.Variable(tf.zeros([784, 10]))
b = tf.Variable(tf.zeros([10]))
sess.run(tf.initialize_all_variables())
y = tf.nn.softmax(tf.matmul(x, w) + b)
cross_entropy = -tf.reduce_sum(y_ * tf.log(y))
train_step = tf.train.GradientDescentOptimizer(0.01).minimize(
cross_entropy)
values, indices = tf.nn.top_k(y, 10)
prediction_classes = tf.contrib.lookup.index_to_string(
tf.to_int64(indices),
mapping=tf.constant([str(i) for i in xrange(10)]))
for _ in range(FLAGS.training_iteration):
batch = mnist.train.next_batch(50)
train_step.run(feed_dict={x: batch[0], y_: batch[1]})
correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, 'float'))
print 'training accuracy %g' % sess.run(accuracy,
feed_dict={
x: mnist.test.images,
y_: mnist.test.labels
})
print 'Done training!'
# Export model
# WARNING(break-tutorial-inline-code): The following code snippet is
# in-lined in tutorials, please update tutorial documents accordingly
# whenever code changes.
export_path = sys.argv[-1]
print 'Exporting trained model to', export_path
init_op = tf.group(tf.initialize_all_tables(), name='init_op')
saver = tf.train.Saver(sharded=True)
model_exporter = exporter.Exporter(saver)
model_exporter.init(
sess.graph.as_graph_def(),
init_op=init_op,
default_graph_signature=exporter.classification_signature(
input_tensor=serialized_tf_example,
classes_tensor=prediction_classes,
scores_tensor=values),
named_graph_signatures={
'inputs': exporter.generic_signature({'images': x}),
'outputs': exporter.generic_signature({'scores': y})
})
model_exporter.export(export_path, tf.constant(FLAGS.export_version), sess)
print 'Done exporting!'
def main(_):
if len(sys.argv) < 2 or sys.argv[-1].startswith('-'):
print('Usage: mnist_export.py [--training_iteration=x] '
'[--model_version=y] export_dir')
sys.exit(-1)
if FLAGS.training_iteration <= 0:
print 'Please specify a positive value for training iteration.'
sys.exit(-1)
if FLAGS.model_version <= 0:
print 'Please specify a positive value for version number.'
sys.exit(-1)
# Train model
print 'Training model...'
mnist = mnist_input_data.read_data_sets(FLAGS.work_dir, one_hot=True)
sess = tf.InteractiveSession()
serialized_tf_example = tf.placeholder(tf.string, name='tf_example')
feature_configs = {'x': tf.FixedLenFeature(shape=[784], dtype=tf.float32),}
tf_example = tf.parse_example(serialized_tf_example, feature_configs)
x = tf.identity(tf_example['x'], name='x') # use tf.identity() to assign name
y_ = tf.placeholder('float', shape=[None, 10])
w = tf.Variable(tf.zeros([784, 10]))
b = tf.Variable(tf.zeros([10]))
sess.run(tf.initialize_all_variables())
y = tf.nn.softmax(tf.matmul(x, w) + b, name='y')
cross_entropy = -tf.reduce_sum(y_ * tf.log(y))
train_step = tf.train.GradientDescentOptimizer(0.01).minimize(cross_entropy)
values, indices = tf.nn.top_k(y, 10)
prediction_classes = tf.contrib.lookup.index_to_string(
tf.to_int64(indices), mapping=tf.constant([str(i) for i in xrange(10)]))
for _ in range(FLAGS.training_iteration):
batch = mnist.train.next_batch(50)
train_step.run(feed_dict={x: batch[0], y_: batch[1]})
correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, 'float'))
print 'training accuracy %g' % sess.run(
accuracy, feed_dict={x: mnist.test.images,
y_: mnist.test.labels})
print 'Done training!'
# Export model
# WARNING(break-tutorial-inline-code): The following code snippet is
# in-lined in tutorials, please update tutorial documents accordingly
# whenever code changes.
export_path_base = sys.argv[-1]
export_path = os.path.join(
compat.as_bytes(export_path_base),
compat.as_bytes(str(FLAGS.model_version)))
print 'Exporting trained model to', export_path
builder = saved_model_builder.SavedModelBuilder(export_path)
# Build the signature_def_map.
classification_inputs = utils.build_tensor_info(serialized_tf_example)
classification_outputs_classes = utils.build_tensor_info(prediction_classes)
classification_outputs_scores = utils.build_tensor_info(values)
classification_signature = signature_def_utils.build_signature_def(
inputs={signature_constants.CLASSIFY_INPUTS: classification_inputs},
outputs={
signature_constants.CLASSIFY_OUTPUT_CLASSES:
classification_outputs_classes,
signature_constants.CLASSIFY_OUTPUT_SCORES:
classification_outputs_scores
},
method_name=signature_constants.CLASSIFY_METHOD_NAME)
tensor_info_x = utils.build_tensor_info(x)
tensor_info_y = utils.build_tensor_info(y)
prediction_signature = signature_def_utils.build_signature_def(
inputs={'images': tensor_info_x},
outputs={'scores': tensor_info_y},
method_name=signature_constants.PREDICT_METHOD_NAME)
legacy_init_op = tf.group(tf.initialize_all_tables(), name='legacy_init_op')
builder.add_meta_graph_and_variables(
sess, [tag_constants.SERVING],
signature_def_map={
'predict_images':
prediction_signature,
signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY:
classification_signature,
},
legacy_init_op=legacy_init_op)
builder.save()
print 'Done exporting!'
def main(_):
if len(sys.argv) < 2 or sys.argv[-1].startswith('-'):
print('Usage: mnist_export.py [--training_iteration=x] '
'[--model_version=y] export_dir')
sys.exit(-1)
if FLAGS.training_iteration <= 0:
print 'Please specify a positive value for training iteration.'
sys.exit(-1)
if FLAGS.model_version <= 0:
print 'Please specify a positive value for version number.'
sys.exit(-1)
# Train model
print 'Training model...'
#Read the data and format it
mnist = mnist_input_data.read_data_sets(FLAGS.work_dir, one_hot=True)
sess = tf.InteractiveSession()
serialized_tf_example = tf.placeholder(tf.string, name='tf_example')
feature_configs = {'x': tf.FixedLenFeature(shape=[784], dtype=tf.float32),}
tf_example = tf.parse_example(serialized_tf_example, feature_configs)
#Build model
x = tf.identity(tf_example['x'], name='x') # use tf.identity() to assign name
y_ = tf.placeholder('float', shape=[None, 10])
w = tf.Variable(tf.zeros([784, 10]))
b = tf.Variable(tf.zeros([10]))
sess.run(tf.global_variables_initializer())
y = tf.nn.softmax(tf.matmul(x, w) + b, name='y')
cross_entropy = -tf.reduce_sum(y_ * tf.log(y))
train_step = tf.train.GradientDescentOptimizer(0.01).minimize(cross_entropy)
values, indices = tf.nn.top_k(y, 10)
table = tf.contrib.lookup.index_to_string_table_from_tensor(
tf.constant([str(i) for i in xrange(10)]))
#train the model
prediction_classes = table.lookup(tf.to_int64(indices))
for _ in range(FLAGS.training_iteration):
batch = mnist.train.next_batch(50)
train_step.run(feed_dict={x: batch[0], y_: batch[1]})
correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, 'float'))
print 'training accuracy %g' % sess.run(
accuracy, feed_dict={x: mnist.test.images,
y_: mnist.test.labels})
print 'Done training!'
# Save the model
#where to save to?
export_path_base = sys.argv[-1]
export_path = os.path.join(
compat.as_bytes(export_path_base),
compat.as_bytes(str(FLAGS.model_version)))
print 'Exporting trained model to', export_path
#This creates a SERVABLE from our model
#saves a "snapshot" of the trained model to reliable storage
#so that it can be loaded later for inference.
#can save as many version as necessary
#the tensoroflow serving main file tensorflow_model_server
#will create a SOURCE out of it, the source
#can house state that is shared across multiple servables
#or versions
#we can later create a LOADER from it using tf.saved_model.loader.load
#then the MANAGER decides how to handle its lifecycle
builder = saved_model_builder.SavedModelBuilder(export_path)
# Build the signature_def_map.
#Signature specifies what type of model is being exported,
#and the input/output tensors to bind to when running inference.
#think of them as annotiations on the graph for serving
#we can use them a number of ways
#grabbing whatever inputs/outputs/models we want either on server
#or via client
classification_inputs = utils.build_tensor_info(serialized_tf_example)
classification_outputs_classes = utils.build_tensor_info(prediction_classes)
classification_outputs_scores = utils.build_tensor_info(values)
classification_signature = signature_def_utils.build_signature_def(
inputs={signature_constants.CLASSIFY_INPUTS: classification_inputs},
outputs={
signature_constants.CLASSIFY_OUTPUT_CLASSES:
classification_outputs_classes,
signature_constants.CLASSIFY_OUTPUT_SCORES:
classification_outputs_scores
},
method_name=signature_constants.CLASSIFY_METHOD_NAME)
tensor_info_x = utils.build_tensor_info(x)
tensor_info_y = utils.build_tensor_info(y)
prediction_signature = signature_def_utils.build_signature_def(
inputs={'images': tensor_info_x},
outputs={'scores': tensor_info_y},
method_name=signature_constants.PREDICT_METHOD_NAME)
legacy_init_op = tf.group(tf.tables_initializer(), name='legacy_init_op')
#add the sigs to the servable
builder.add_meta_graph_and_variables(
sess, [tag_constants.SERVING],
signature_def_map={
'predict_images':
prediction_signature,
signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY:
classification_signature,
},
legacy_init_op=legacy_init_op)
#save it!
builder.save()
print 'Done exporting!'
if len(sys.argv) < 2 or sys.argv[-1].startswith('-'):
print('Usage: mnist_export.py [--training_iteration=x] '
'[--model_version=y] export_dir')
sys.exit(-1)
if FLAGS.training_iteration <= 0:
print 'Please specify a positive value for training iteration.'
sys.exit(-1)
if FLAGS.model_version <= 0:
print 'Please specify a positive value for version number.'
sys.exit(-1)
# Train model
print 'Training model...'
mnist = mnist_input_data.read_data_sets(FLAGS.work_dir, one_hot=True)
sess = tf.InteractiveSession()
serialized_tf_example = tf.placeholder(tf.string, name='tf_example')
feature_configs = {'x': tf.FixedLenFeature(shape=[784], dtype=tf.float32),}
tf_example = tf.parse_example(serialized_tf_example, feature_configs)
x = tf.identity(tf_example['x'], name='x') # use tf.identity() to assign name
y_ = tf.placeholder('float', shape=[None, 10])
w = tf.Variable(tf.zeros([784, 10]))
b = tf.Variable(tf.zeros([10]))
sess.run(tf.global_variables_initializer())
y = tf.nn.softmax(tf.matmul(x, w) + b, name='y')
cross_entropy = -tf.reduce_sum(y_ * tf.log(y))
train_step = tf.train.GradientDescentOptimizer(0.01).minimize(cross_entropy)
values, indices = tf.nn.top_k(y, 10)
table = tf.contrib.lookup.index_to_string_table_from_tensor(
tf.constant([str(i) for i in xrange(10)]))
def main(_):
if len(sys.argv) < 2 or sys.argv[-1].startswith('-'):
print('Usage: mnist_export.py [--training_iteration=x] '
'[--export_version=y] export_dir')
sys.exit(-1)
if FLAGS.training_iteration <= 0:
print('Please specify a positive value for training iteration.')
sys.exit(-1)
if FLAGS.export_version <= 0:
print('Please specify a positive value for version number.')
sys.exit(-1)
# Train model
print('Training model...')
mnist = mnist_input_data.read_data_sets(FLAGS.work_dir, one_hot=True)
sess = tf.InteractiveSession()
serialized_tf_example = tf.placeholder(tf.string, name='tf_example')
feature_configs = {
'x': tf.FixedLenFeature(shape=[784], dtype=tf.float32),
}
tf_example = tf.parse_example(serialized_tf_example, feature_configs)
x = tf.identity(tf_example['x'], name='x') # use tf.identity() to assign name
y_ = tf.placeholder('float', shape=[None, 10])
w = tf.Variable(tf.zeros([784, 10]))
b = tf.Variable(tf.zeros([10]))
sess.run(tf.initialize_all_variables())
y = tf.nn.softmax(tf.matmul(x, w) + b, name='y')
cross_entropy = -tf.reduce_sum(y_ * tf.log(y))
train_step = tf.train.GradientDescentOptimizer(0.01).minimize(cross_entropy)
values, indices = tf.nn.top_k(y, 10)
prediction_classes = tf.contrib.lookup.index_to_string(
tf.to_int64(indices),
mapping=tf.constant([str(i) for i in range(10)]))
for _ in range(FLAGS.training_iteration):
batch = mnist.train.next_batch(50)
train_step.run(feed_dict={x: batch[0], y_: batch[1]})
correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, 'float'))
print('training accuracy %g' % sess.run(accuracy,
feed_dict={x: mnist.test.images,
y_: mnist.test.labels}))
print('Done training!')
# Export model
# WARNING(break-tutorial-inline-code): The following code snippet is
# in-lined in tutorials, please update tutorial documents accordingly
# whenever code changes.
export_path = sys.argv[-1]
print('Exporting trained model to %s' % export_path)
init_op = tf.group(tf.initialize_all_tables(), name='init_op')
saver = tf.train.Saver(sharded=True)
model_exporter = exporter.Exporter(saver)
model_exporter.init(
sess.graph.as_graph_def(),
init_op=init_op,
default_graph_signature=exporter.classification_signature(
input_tensor=serialized_tf_example,
classes_tensor=prediction_classes,
scores_tensor=values),
named_graph_signatures={
'inputs': exporter.generic_signature({'images': x}),
'outputs': exporter.generic_signature({'scores': y})})
model_exporter.export(export_path, tf.constant(FLAGS.export_version), sess)
print('Done exporting!')