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
channel = grpc.insecure_channel(hostport)
stub = prediction_service_pb2_grpc.PredictionServiceStub(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 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
channel = grpc.insecure_channel(hostport)
stub = prediction_service_pb2_grpc.PredictionServiceStub(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 __init__(self, input_dir, stop_at_record=-1, random=False):
mnist_data = mnist_input_data.read_data_sets(input_dir, one_hot=True)
self._samples = mnist_data.test.images
self._labels = mnist_data.test.labels
self._input_records = mnist_data.test.num_examples
super(MnistStreamInput, self).__init__(self._input_records,
stop_at_record, random)
def main(_):
# thread_num_array = [1, 2, 4, 8, 16, 32, 64, 128]
# batch_size_2d_array = [[1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024],
# [1, 2, 4, 8, 16, 32, 64, 128, 256, 512],
# [1, 2, 4, 8, 16, 32, 64, 128, 256],
# [1, 2, 4, 8, 16, 32, 64, 128],
# [1, 2, 4, 8, 16, 32, 64],
# [1, 2, 4, 8, 16, 32],
# [1, 2, 4, 8, 16],
# [1, 2, 4, 8]]
thread_num_array = [1, 2]
batch_size_2d_array = [[1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024],
[1, 2 ,4, 8, 16, 32, 64, 128, 256, 512]]
test_data_set = mnist_input_data.read_data_sets(FLAGS.work_dir).train
for i in range(len(thread_num_array)):
thread_num = thread_num_array[i]
batch_size_arrary = batch_size_2d_array[i]
for batch_size in batch_size_arrary:
time.sleep(2.0)
image, label = test_data_set.next_batch(batch_size)
run_num = FLAGS.num_tests / (thread_num * batch_size)
# print("[INFO] thread_num = %d, batch_size = %d, run_num = %d" % (thread_num, batch_size, run_num))
# warmup
warmup_num = 3
for i in range(warmup_num):
start = time.time()
do_inference(FLAGS.server, batch_size, 1, image, label, 0)
end = time.time()
duration = end - start
# print("warmup duration = %f" % duration)
# raw_input("Press Enter to continue...")
start = time.time()
thread_pool = []
for i in range(thread_num):
t = threading.Thread(target = do_inference, args = (FLAGS.server, batch_size, run_num, image, label, i))
thread_pool.append(t)
t.start()
for t in thread_pool:
t.join()
end = time.time()
print("overall duration for b = %3d, p = %3d is %.3f" % (batch_size, thread_num, end - start))
def main():
n_out = 10
learning_rate=0.01
n_iter=10000
batch_size=20
validate_frequency = 100
hiddenlayer_params = [500]
activate_func = tf.tanh
if 1:
mnist = mnist_input_data.read_data_sets("../data/MNIST_data/", one_hot=True)
model = MLP(mnist, n_out, learning_rate, n_iter, batch_size, \
validate_frequency, hiddenlayer_params, activate_func)
model.train_mnist()
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
channel = grpc.insecure_channel(hostport)
#print('hostport:\n%s%%' % hostport);
#print('channel:\n%s%%' % channel);
stub = prediction_service_pb2_grpc.PredictionServiceStub(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)
#print('image base64 str:\n%s%%' % base64.encodestring(image[0]));
request.inputs['images'].CopyFrom(
tf.contrib.util.make_tensor_proto(image[0],
shape=[1, image[0].size]))
#print('request:\n%s%%' % request);
#print('request:\n%s%%' % json.dumps(request));
result_counter.throttle()
#print('stub:\n%s%%' % stub);
#print('stub.Predict:\n%s%%' % stub.Predict);
result_future = stub.Predict.future(request, 5.0) # 5 seconds
#print('result_future:\n%s%%' % result_future);
print('\n')
print('label%s%%' % label)
print('result_future.socres%s%%' %
numpy.array(result_future.result().outputs['scores'].float_val))
print('\n')
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):
test_data_set = mnist_input_data.read_data_sets(work_dir).test
channel = grpc.insecure_channel(hostport)
stub = prediction_service_pb2_grpc.PredictionServiceStub(channel)
result_counter = _ResultCounter(num_tests, concurrency)
for _ in range(num_tests):
request = predict_pb2.PredictRequest()
request.model_spec.name = 'my_model'
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, batch_size, 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
channel = grpc.insecure_channel(hostport)
stub = prediction_service_pb2_grpc.PredictionServiceStub(channel)
result_counter = _ResultCounter(num_tests, concurrency)
for _ in range(num_tests / batch_size):
request = predict_pb2.PredictRequest()
request.model_spec.name = 'mnist'
request.model_spec.signature_name = 'predict_images'
image, label = test_data_set.next_batch(batch_size)
request.inputs['images'].CopyFrom(
tf.contrib.util.make_tensor_proto(image, shape=image.shape))
result = stub.Predict(request, 10.0)
response = tensor_util.MakeNdarray(result.outputs['scores'])
for i in range(len(label)):
this_label = label[i]
this_prediction = numpy.argmax(response[i])
if this_label != this_prediction:
result_counter.inc_error()
result_counter.inc_done()
result_counter.dec_active()
return result_counter.get_error_rate()
def train(sess):
images = tf.placeholder('float', shape=[None, 28, 28, 1], name='images')
x = tf.reshape(images, [-1, 784])
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())
scores = tf.nn.softmax(tf.matmul(x, w) + b, name='scores')
cross_entropy = -tf.reduce_sum(y_ * tf.log(scores))
train_step = tf.train.GradientDescentOptimizer(0.01).minimize(
cross_entropy)
mnist = mnist_input_data.read_data_sets(FLAGS.work_dir, one_hot=True)
for _ in range(FLAGS.training_iteration):
batch = mnist.train.next_batch(50)
train_step.run(feed_dict={x: batch[0], y_: batch[1]})
return (images, scores)
def do_inference(hostport, work_dir, concurrency, num_tests):
"""Tests PredictionService over Tensor-Bridge.
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
error = 0
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]))
response = requests.post(
'http://' + hostport + '/tensor-bridge/v1/prediction',
json=MessageToDict(request,
preserving_proto_field_name=True,
including_default_value_fields=True))
result = ParseDict(response.json(),
predict_pb2.PredictResponse(),
ignore_unknown_fields=True)
scores = numpy.array(result.outputs['scores'].float_val)
prediction = numpy.argmax(scores)
if label[0] != prediction:
error += 1
return error / num_tests
def main(_):
# if FLAGS.num_tests > 10000:
# print('num_tests should not be greater than 10k')
# return
# if not FLAGS.server:
# print('please specify server host:port')
# return
# do_inference(FLAGS.server, FLAGS.work_dir,
# FLAGS.concurrency, FLAGS.num_tests)
hostport = FLAGS.server
work_dir = FLAGS.work_dir
concurrency = FLAGS.concurrency
num_tests = FLAGS.num_tests
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)
myFuncWarmUp(stub, 0, test_data_set)
num_tests = 0
tPool = []
for i in range(num_tests):
tPool.append(threading.Thread(target = myFuncParallel, args = (stub, i, test_data_set)))
start = time.time()
for i in range(num_tests):
t = tPool[i]
t.start()
# time.sleep(2.0)
for i in range(num_tests):
t = tPool[i]
t.join()
end = time.time()
print('\nFinished!')
print('[Parallel] The total running time to run %d concurrent jobs is %s' % (num_tests, str(end - start)))
def do_inference(hostport, work_dir, concurrency, num_tests):
test_data_set = mnist_input_data.read_data_sets(work_dir).test
fs = []
fl = []
for _ in range(num_tests):
image, label = test_data_set.next_batch(1)
r = image[0].tolist()
data = {
"signature_name": "predict_images",
"instances": [{
"images": r
}]
}
data1 = json.dumps(data)
url = "http://localhost:8501/v1/models/my_model:predict"
response = requests.post(url, data=data1)
abcd = response.json()
fs.append(numpy.argmax(abcd['predictions'][0]))
fl.append(label)
print(fs)
return accuracy_score(fs, fl)
def main(_):
#if FLAGS.num_tests > 10000:
# print('num_tests should not be greater than 10k')
# return
#if not FLAGS.server:
# print('please specify server host:port')
# return
#error_rate = do_inference(FLAGS.server, FLAGS.work_dir,
# FLAGS.concurrency, FLAGS.num_tests)
#print('\nInference error rate: %s%%' % (error_rate * 100))
test_data = mnist_input_data.read_data_sets(FLAGS.work_dir).test
credentials = {}
credentials['email'] = "*****@*****.**"
credentials['password'] = "******"
login_request = requests.post(
'https://localhost:8181/hopsworks-api/api/auth/login',
data=credentials,
verify=False)
token = login_request.headers['Authorization']
thread_requests = FLAGS.num_tests / FLAGS.concurrency
threads = []
start = time.time()
for i in range(0, FLAGS.concurrency):
thread = threading.Thread(target=do_inference,
args=(thread_requests, token, test_data))
thread.start()
threads.append(thread)
for t in threads:
t.join()
end = time.time()
print("Time:", end - start)
def main(_):
if not FLAGS.server:
print('please specify server host:port')
return
if not FLAGS.model_name:
print('please specify model_name')
return
test_data_set = mnist_input_data.read_data_sets(FLAGS.work_dir).test
image, label = test_data_set.next_batch(1)
start = time.time()
print("The time start predicting: {}".format(start))
do_inference(FLAGS.server, FLAGS.num_tests, image, label)
end = time.time()
duration = end - start
qps = FLAGS.num_tests / duration
print("\nThe time end predicting: {}".format(end))
print("Total predict time: {}(s)".format(duration))
print("Total predict num: {}".format(FLAGS.num_tests))
print("qps: {}".format(qps))
def do_inference(hostport, work_dir, batch_size, run_num, thread_id):
durationSum = 0.0
# test_data_set = mnist_input_data.read_data_sets(work_dir).test
test_data_set = mnist_input_data.read_data_sets(work_dir).train
channel = grpc.insecure_channel(hostport)
stub = prediction_service_pb2_grpc.PredictionServiceStub(channel)
for i in range(run_num):
start = time.time()
request = predict_pb2.PredictRequest()
request.model_spec.name = 'mnist'
request.model_spec.signature_name = 'predict_images'
image, label = test_data_set.next_batch(batch_size)
request.inputs['images'].CopyFrom(
tf.contrib.util.make_tensor_proto(image, shape=image.shape))
result = stub.Predict(request, 10.0)
end = time.time()
duration = end - start
durationSum += duration
def main(_):
if not FLAGS.server:
print('please specify server host:port')
return
if not FLAGS.model_name:
print('please specify model_name')
return
test_data_set = mnist_input_data.read_data_sets(FLAGS.work_dir).test
image, label = test_data_set.next_batch(2)
#print(image, label)
#print(image.shape)
#return
#import ipdb; ipdb.set_trace()
#do_inference(0, FLAGS.server, FLAGS.num_tests, image, label)
pool = multiprocessing.Pool(processes=FLAGS.concurrency)
results = []
start_time = time.time()
print("The time start predicting: {}".format(start_time))
for process_num in range(FLAGS.concurrency):
pool.apply_async(
do_inference,
(process_num, FLAGS.server, FLAGS.num_tests, image, label))
pool.close()
pool.join()
global lock, finish_time, real_test_num
with lock:
duration = finish_time.value - start_time
#total_num_tests = FLAGS.num_tests * FLAGS.concurrency
total_num_tests = real_test_num.value
qps = total_num_tests / duration
print("The time end predicting: {}".format(finish_time.value))
print("Total predict time: {}(s)".format(duration))
print("Total predict num: {}".format(total_num_tests))
print("qps: {}".format(qps))
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.data_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!')
print(sess.graph.as_graph_def())
# Export model
export_path_base = FLAGS.model_dir
# path是路径+版本号，下次导出新版本的模型时，要把版本号设置成比这个大的数字
export_path = os.path.join(
tf.compat.as_bytes(export_path_base),
tf.compat.as_bytes(str(FLAGS.model_version)))
print('Exporting trained model to', export_path)
# SavedModelBuilder saves a "snapshot" of the trained model to reliable storage so that it can be loaded later for inference.
builder = tf.saved_model.builder.SavedModelBuilder(export_path)
# Build the signature_def_map.
classification_inputs = tf.saved_model.utils.build_tensor_info(
serialized_tf_example)
classification_outputs_classes = tf.saved_model.utils.build_tensor_info(
prediction_classes)
classification_outputs_scores = tf.saved_model.utils.build_tensor_info(values)
# Define classification signature.
classification_signature = (
tf.saved_model.signature_def_utils.build_signature_def(
inputs={
tf.saved_model.signature_constants.CLASSIFY_INPUTS:
classification_inputs
},
outputs={
tf.saved_model.signature_constants.CLASSIFY_OUTPUT_CLASSES:
classification_outputs_classes,
tf.saved_model.signature_constants.CLASSIFY_OUTPUT_SCORES:
classification_outputs_scores
},
method_name=tf.saved_model.signature_constants.CLASSIFY_METHOD_NAME))
# =========================================================================== #
# Define prediction signature
# images and scores are tensor alias names, they can be whatever unique strings you want
# they will become the logical names of tensor x and y that you refer to for tensor binding when sending prediction requests later.
# =========================================================================== #
tensor_info_x = tf.saved_model.utils.build_tensor_info(x)
tensor_info_y = tf.saved_model.utils.build_tensor_info(y)
prediction_signature = (
tf.saved_model.signature_def_utils.build_signature_def(
inputs={'images': tensor_info_x},
outputs={'scores': tensor_info_y},
method_name=tf.saved_model.signature_constants.PREDICT_METHOD_NAME))
legacy_init_op = tf.group(tf.tables_initializer(), name='legacy_init_op')
# tags is the set of tags with which to save the meta graph.
builder.add_meta_graph_and_variables(
sess, [tf.saved_model.tag_constants.SERVING],
signature_def_map={
'predict_images':
prediction_signature,
tf.saved_model.signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY:
classification_signature,
},
legacy_init_op=legacy_init_op)
builder.save()
print('Done exporting!')
test_data_set = mnist_input_data.read_data_sets("model/data").test
print (len(test_data_set.images))
print(len(test_data_set.labels))
predictor = tf.argmax(y, 1)
acc, predictions = sess.run([accuracy, predictor], feed_dict={
x: test_data_set.images
})
print('Accuracy', acc)
print('Predictions', predictions)
'''
from tensorflow.core.framework import types_pb2, tensor_shape_pb2
test_data_set = mnist_input_data.read_data_sets("model/data").test
inputs = {
'x':
tf.TensorInfo(name='x:0',
dtype=types_pb2.DT_FLOAT,
tensor_shape=tensor_shape_pb2.TensorShapeProto())
}
outputs = {'y': tf.TensorInfo(name='y:0', dtype=types_pb2.DT_FLOAT)}
signature_def = tf.saved_model.signature_def_utils.build_signature_def(
inputs=inputs,
outputs=outputs,
method_name='tensorflow/serving/predict')
saved_model_predictor = predictor.from_saved_model(
export_dir="model/4", signature_def=signature_def)
def read_image_arr(image_file):
img = Image.open(image_file)
img_array = np.array(img, dtype=np.uint8)
# print(img_array)
img_array = img_array.reshape(img_array.size)
img_array = np.divide(np.mod(img_array, 255), 255.0)
#img_array = np.divide(np.subtract(255.0, img_array), 255.0)
# print(img_array)
return img_array
if __name__ == '__main__':
test_data_set = mnist_input_data.read_data_sets(input_dir).test
num_tests = 1
for idx in range(num_tests):
image, label = test_data_set.next_batch(1)
# print(image[0])
# arr = np.asanyarray(image[0]).reshape(28, 28)
# print(arr)
print("############", type(image[0][0]))
print("############", label[0])
idx_num = "%05d" % int(idx)
img_1 = trans_image(idx_num, image)
# print(image[0])
out_file = out_dir % idx_num
img_2 = read_image_arr(out_file)
print("############")
def main():
parser = argparse.ArgumentParser()
add_parameters(parser)
args = parser.parse_args()
if args.training_iteration <= 0:
print('Please specify a positive value for training iteration.')
sys.exit(-1)
# Read the train and test data sets
mnist = mnist_input_data.read_data_sets(args.input_cache_dir, one_hot=True)
## MLOps start
# Initialize the mlops library
mlops.init()
# Report the feature distribution for the training data
train_images = mnist.train.images
mlops.set_data_distribution_stat(train_images)
# Initialize a table to track training accuracy and cost
train_table = Table().name("Training Stats").cols(["Accuracy", "Cost"])
## MLOps end
# Create the model
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')
# Set the cost function and optimizer
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 range(10)]))
prediction_classes = table.lookup(tf.to_int64(indices))
correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, 'float'))
# Train the model
print('Training model...')
for i in range(args.training_iteration):
batch = mnist.train.next_batch(50)
_, train_cost, train_acc = sess.run(
[train_step, cross_entropy, accuracy],
feed_dict={
x: batch[0],
y_: batch[1]
})
# Display stats
if (i + 1
) % args.display_step == 0 or i + 1 == args.training_iteration:
# Report training accuracy and cost
print("Training. step={}, accuracy={}, cost={}".format(
i + 1, train_acc, train_cost))
# MLOps start
# multiply by 1 to convert into double
train_table.add_row("Iterations: {}".format(i + 1),
[train_acc * 100, train_cost * 1])
mlops.set_stat(train_table)
# MLOps end
print('Done training!')
# Report final cost and accuracy on test set
test_cost, test_acc = sess.run([cross_entropy, accuracy],
feed_dict={
x: mnist.test.images,
y_: mnist.test.labels
})
print("Testing. accuracy={}, cost={}".format(test_acc, test_cost))
## MLOps start
acc_table = Table().name("Test Accuracy").cols(["Accuracy"])
acc_table.add_row("Total iterations: {}".format(args.training_iteration),
[test_acc])
mlops.set_stat(acc_table)
# Release mlops resources
mlops.done()
## MLOps end
# Export the trained model so it can be used for inference
# WARNING(break-tutorial-inline-code): The following code snippet is
# in-lined in tutorials, please update tutorial documents accordingly
# whenever code changes.
export_path = args.save_dir
print('Exporting trained model to', export_path)
builder = tf.saved_model.builder.SavedModelBuilder(export_path)
# Build the signature_def_map.
classification_inputs = tf.saved_model.utils.build_tensor_info(
serialized_tf_example)
classification_outputs_classes = tf.saved_model.utils.build_tensor_info(
prediction_classes)
classification_outputs_scores = tf.saved_model.utils.build_tensor_info(
values)
classification_signature = (
tf.saved_model.signature_def_utils.build_signature_def(
inputs={
tf.saved_model.signature_constants.CLASSIFY_INPUTS:
classification_inputs
},
outputs={
tf.saved_model.signature_constants.CLASSIFY_OUTPUT_CLASSES:
classification_outputs_classes,
tf.saved_model.signature_constants.CLASSIFY_OUTPUT_SCORES:
classification_outputs_scores
},
method_name=tf.saved_model.signature_constants.CLASSIFY_METHOD_NAME
))
tensor_info_x = tf.saved_model.utils.build_tensor_info(x)
tensor_info_y = tf.saved_model.utils.build_tensor_info(y)
prediction_signature = (
tf.saved_model.signature_def_utils.build_signature_def(
inputs={'inputs': tensor_info_x},
outputs={'outputs': tensor_info_y},
method_name=tf.saved_model.signature_constants.PREDICT_METHOD_NAME)
)
legacy_init_op = tf.group(tf.tables_initializer(), name='legacy_init_op')
builder.add_meta_graph_and_variables(
sess, [tf.saved_model.tag_constants.SERVING],
signature_def_map={
'predict_images':
prediction_signature,
tf.saved_model.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.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 range(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 = sys.argv[-1]
print('Exporting trained model to %s' % export_path)
init_op = tf.group(tf.tables_initializer(), 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...'
#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!'
#encoding=utf-8
import os
import sys
import numpy as np
import tensorflow as tf
import mnist_input_data
mnist = mnist_input_data.read_data_sets("/tmp", one_hot=True)
sess = tf.InteractiveSession()
saved_model_dir = "./model/1"
meta_graph_def = tf.saved_model.loader.load(
sess, [tf.saved_model.tag_constants.SERVING], saved_model_dir)
signature_key = "predict_images"
input_key = "images"
output_key = "scores"
# 从meta_graph_def中取出SignatureDef对象
signature = meta_graph_def.signature_def
# 从signature中找出具体输入输出的tensor name
x_tensor_name = signature[signature_key].inputs[input_key].name
y_tensor_name = signature[signature_key].outputs[output_key].name
# 获取tensor 并inference
x = sess.graph.get_tensor_by_name(x_tensor_name)
y = sess.graph.get_tensor_by_name(y_tensor_name)
'''
A Reccurent Neural Network (LSTM) implementation example using TensorFlow library.
This example is using the MNIST database of handwritten digits (http://yann.lecun.com/exdb/mnist/)
Long Short Term Memory paper: http://deeplearning.cs.cmu.edu/pdfs/Hochreiter97_lstm.pdf
Author: Aymeric Damien
Project: https://github.com/aymericdamien/TensorFlow-Examples/
'''
# Import MINST data
import mnist_input_data
import datetime
mnist = mnist_input_data.read_data_sets("/Users/rmencis/Temp", one_hot=True)
import tensorflow as tf
from tensorflow.models.rnn import rnn, rnn_cell
import numpy as np
'''
To classify images using a reccurent neural network, we consider every image row as a sequence of pixels.
Because MNIST image shape is 28*28px, we will then handle 28 sequences of 28 steps for every sample.
'''
# Parameters
learning_rate = 0.001
training_iters = 100000
batch_size = 100
display_time = 5
# Network Parameters
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 range(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(
tf.compat.as_bytes(export_path_base),
tf.compat.as_bytes(str(FLAGS.model_version)))
print('Exporting trained model to', export_path)
builder = tf.saved_model.builder.SavedModelBuilder(export_path)
# Build the signature_def_map.
classification_inputs = tf.saved_model.utils.build_tensor_info(
serialized_tf_example)
classification_outputs_classes = tf.saved_model.utils.build_tensor_info(
prediction_classes)
classification_outputs_scores = tf.saved_model.utils.build_tensor_info(values)
classification_signature = (
tf.saved_model.signature_def_utils.build_signature_def(
inputs={
tf.saved_model.signature_constants.CLASSIFY_INPUTS:
classification_inputs
},
outputs={
tf.saved_model.signature_constants.CLASSIFY_OUTPUT_CLASSES:
classification_outputs_classes,
tf.saved_model.signature_constants.CLASSIFY_OUTPUT_SCORES:
classification_outputs_scores
},
method_name=tf.saved_model.signature_constants.CLASSIFY_METHOD_NAME))
tensor_info_x = tf.saved_model.utils.build_tensor_info(x)
tensor_info_y = tf.saved_model.utils.build_tensor_info(y)
prediction_signature = (
tf.saved_model.signature_def_utils.build_signature_def(
inputs={'images': tensor_info_x},
outputs={'scores': tensor_info_y},
method_name=tf.saved_model.signature_constants.PREDICT_METHOD_NAME))
legacy_init_op = tf.group(tf.tables_initializer(), name='legacy_init_op')
builder.add_meta_graph_and_variables(
sess, [tf.saved_model.tag_constants.SERVING],
signature_def_map={
'predict_images':
prediction_signature,
tf.saved_model.signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY:
classification_signature,
},
legacy_init_op=legacy_init_op)
builder.save()
print('Done exporting!')
'''
A Bidirectional Reccurent Neural Network (LSTM) implementation example using TensorFlow library.
This example is using the MNIST database of handwritten digits (http://yann.lecun.com/exdb/mnist/)
Long Short Term Memory paper: http://deeplearning.cs.cmu.edu/pdfs/Hochreiter97_lstm.pdf
Author: Aymeric Damien
Project: https://github.com/aymericdamien/TensorFlow-Examples/
'''
# Import MINST data
import mnist_input_data
mnist = mnist_input_data.read_data_sets("/tmp/data/", one_hot=True)
import tensorflow as tf
from tensorflow.python.ops.constant_op import constant
from tensorflow.models.rnn import rnn, rnn_cell
import numpy as np
'''
To classify images using a bidirectional reccurent neural network, we consider every image row as a sequence of pixels.
Because MNIST image shape is 28*28px, we will then handle 28 sequences of 28 steps for every sample.
'''
# Parameters
learning_rate = 0.001
training_iters = 100000
batch_size = 128
display_step = 10
# Network Parameters
name: tf_example:0
The given SavedModel SignatureDef contains the following output(s):
outputs['classes'] tensor_info:
dtype: DT_STRING
shape: (-1, 10)
name: index_to_string_Lookup:0
outputs['scores'] tensor_info:
dtype: DT_FLOAT
shape: (-1, 10)
name: TopKV2:0
Method name is: tensorflow/serving/classify
'''
export_dir = 'model/1/'
mnist = mnist_input_data.read_data_sets('/tmp', one_hot=True)
image0 = mnist.test.images[0]
batch0 = mnist.test.next_batch(1)
print(image0.shape)
with tf.Session(graph=tf.Graph()) as sess:
metagraph_def = tf.saved_model.loader.load(
sess, [tf.saved_model.tag_constants.SERVING], export_dir)
signature_def = metagraph_def.signature_def['predict_images']
input_tensor = sess.graph.get_tensor_by_name(
signature_def.inputs['images'].name)
output_tensor = sess.graph.get_tensor_by_name(
signature_def.outputs['scores'].name)
def forward_images(images):
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(
tf.compat.as_bytes(export_path_base),
tf.compat.as_bytes(str(FLAGS.model_version)))
print 'Exporting trained model to', export_path
builder = tf.saved_model.builder.SavedModelBuilder(export_path)
# Build the signature_def_map.
classification_inputs = tf.saved_model.utils.build_tensor_info(
serialized_tf_example)
classification_outputs_classes = tf.saved_model.utils.build_tensor_info(
prediction_classes)
classification_outputs_scores = tf.saved_model.utils.build_tensor_info(values)
classification_signature = (
tf.saved_model.signature_def_utils.build_signature_def(
inputs={
tf.saved_model.signature_constants.CLASSIFY_INPUTS:
classification_inputs
},
outputs={
tf.saved_model.signature_constants.CLASSIFY_OUTPUT_CLASSES:
classification_outputs_classes,
tf.saved_model.signature_constants.CLASSIFY_OUTPUT_SCORES:
classification_outputs_scores
},
method_name=tf.saved_model.signature_constants.CLASSIFY_METHOD_NAME))
tensor_info_x = tf.saved_model.utils.build_tensor_info(x)
tensor_info_y = tf.saved_model.utils.build_tensor_info(y)
prediction_signature = (
tf.saved_model.signature_def_utils.build_signature_def(
inputs={'images': tensor_info_x},
outputs={'scores': tensor_info_y},
method_name=tf.saved_model.signature_constants.PREDICT_METHOD_NAME))
legacy_init_op = tf.group(tf.tables_initializer(), name='legacy_init_op')
builder.add_meta_graph_and_variables(
sess, [tf.saved_model.tag_constants.SERVING],
signature_def_map={
'predict_images':
prediction_signature,
tf.saved_model.signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY:
classification_signature,
},
legacy_init_op=legacy_init_op)
builder.save()
print 'Done exporting!'
import mnist_input_data
import tensorflow as tf
mnist = mnist_input_data.read_data_sets("MNIST_data/", one_hot=True)
def weight_variable(shape):
initial = tf.truncated_normal(shape, stddev=0.1)
return tf.Variable(initial)
def bias_variable(shape):
initial = tf.constant(0.1, shape=shape)
return tf.Variable(initial)
# Create the model
x = tf.placeholder(tf.float32, [None, 784])
W_fc1 = weight_variable([784, 200])
b_fc1 = bias_variable([200])
W_fc2 = weight_variable([200, 200])
b_fc2 = bias_variable([200])
W_out = weight_variable([200, 10])
b_out = bias_variable([10])
hidden_1 = tf.nn.relu(tf.matmul(x, W_fc1) + b_fc1)
hidden_2 = tf.nn.relu(tf.matmul(hidden_1, W_fc2) + b_fc2)
y = tf.nn.softmax(tf.matmul(hidden_2, W_out) + b_out)
# Define loss and optimizer
y_ = tf.placeholder(tf.float32, [None, 10])
correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
def run_training():
"""Train MNIST for a number of steps."""
# Get the sets of images and labels for training, validation, and
# test on MNIST.
data_sets = mnist_input_data.read_data_sets(FLAGS.input_data_dir,
FLAGS.fake_data)
# Tell TensorFlow that the model will be built into the default Graph.
with tf.Graph().as_default():
# Generate placeholders for the images and labels.
images_placeholder, labels_placeholder = placeholder_inputs(
FLAGS.batch_size)
# Build a Graph that computes predictions from the inference model.
logits = mnist.inference(images_placeholder, FLAGS.hidden1,
FLAGS.hidden2)
# Add to the Graph the Ops for loss calculation.
loss = mnist.loss(logits, labels_placeholder)
# Add to the Graph the Ops that calculate and apply gradients.
train_op = mnist.training(loss, FLAGS.learning_rate)
# Add the Op to compare the logits to the labels during evaluation.
eval_correct = mnist.evaluation(logits, labels_placeholder)
# Build the summary Tensor based on the TF collection of Summaries.
summary = tf.summary.merge_all()
# Add the variable initializer Op.
init = tf.global_variables_initializer()
# Create a saver for writing training checkpoints.
saver = tf.train.Saver()
# Create a session for running Ops on the Graph.
sess = tf.Session()
# Instantiate a SummaryWriter to output summaries and the Graph.
summary_writer = tf.summary.FileWriter(FLAGS.log_dir, sess.graph)
# And then after everything is built:
# Run the Op to initialize the variables.
sess.run(init)
# Start the training loop.
for step in xrange(FLAGS.max_steps):
start_time = time.time()
# Fill a feed dictionary with the actual set of images and labels
# for this particular training step.
feed_dict = fill_feed_dict(data_sets.train, images_placeholder,
labels_placeholder)
# Run one step of the model. The return values are the activations
# from the `train_op` (which is discarded) and the `loss` Op. To
# inspect the values of your Ops or variables, you may include them
# in the list passed to sess.run() and the value tensors will be
# returned in the tuple from the call.
_, loss_value = sess.run([train_op, loss], feed_dict=feed_dict)
duration = time.time() - start_time
# Write the summaries and print an overview fairly often.
if step % 100 == 0:
# Print status to stdout.
print('Step %d: loss = %.2f (%.3f sec)' %
(step, loss_value, duration))
# Update the events file.
summary_str = sess.run(summary, feed_dict=feed_dict)
summary_writer.add_summary(summary_str, step)
summary_writer.flush()
# Save a checkpoint and evaluate the model periodically.
if (step + 1) % 1000 == 0 or (step + 1) == FLAGS.max_steps:
checkpoint_file = os.path.join(FLAGS.log_dir, 'model.ckpt')
saver.save(sess, checkpoint_file, global_step=step)
# Evaluate against the training set.
print('Training Data Eval:')
do_eval(sess, eval_correct, images_placeholder,
labels_placeholder, data_sets.train)
# Evaluate against the validation set.
print('Validation Data Eval:')
do_eval(sess, eval_correct, images_placeholder,
labels_placeholder, data_sets.validation)
# Evaluate against the test set.
print('Test Data Eval:')
do_eval(sess, eval_correct, images_placeholder,
labels_placeholder, data_sets.test)
def train():
with tf.device("/gpu:0"):
# Import data
mnist = mnist_input_data.read_data_sets(FLAGS.data_dir, one_hot=True)
sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True,
log_device_placement=True))
# Create a multilayer model.
# Input placeholders
with tf.name_scope('input'):
x = tf.placeholder(tf.float32, [None, 784], name='x-input')
y_ = tf.placeholder(tf.float32, [None, 10], name='y-input')
with tf.name_scope('input_reshape'):
image_shaped_input = tf.reshape(x, [-1, 28, 28, 1])
tf.summary.image('input', image_shaped_input, 10)
# We can't initialize these variables to 0 - the network will get stuck.
def weight_variable(shape):
"""Create a weight variable with appropriate initialization."""
initial = tf.truncated_normal(shape, stddev=0.1)
return tf.Variable(initial)
def bias_variable(shape):
"""Create a bias variable with appropriate initialization."""
initial = tf.constant(0.1, shape=shape)
return tf.Variable(initial)
def variable_summaries(var):
"""Attach a lot of summaries to a Tensor (for TensorBoard visualization)."""
with tf.name_scope('summaries'):
mean = tf.reduce_mean(var)
tf.summary.scalar('mean', mean)
with tf.name_scope('stddev'):
stddev = tf.sqrt(tf.reduce_mean(tf.square(var - mean)))
tf.summary.scalar('stddev', stddev)
tf.summary.scalar('max', tf.reduce_max(var))
tf.summary.scalar('min', tf.reduce_min(var))
tf.summary.histogram('histogram', var)
def nn_layer(input_tensor,
input_dim,
output_dim,
layer_name,
act=tf.nn.relu):
"""Reusable code for making a simple neural net layer.
It does a matrix multiply, bias add, and then uses relu to nonlinearize.
It also sets up name scoping so that the resultant graph is easy to read,
and adds a number of summary ops.
"""
# Adding a name scope ensures logical grouping of the layers in the graph.
with tf.name_scope(layer_name):
# This Variable will hold the state of the weights for the layer
with tf.name_scope('weights'):
weights = weight_variable([input_dim, output_dim])
variable_summaries(weights)
with tf.name_scope('biases'):
biases = bias_variable([output_dim])
variable_summaries(biases)
with tf.name_scope('Wx_plus_b'):
preactivate = tf.matmul(input_tensor, weights) + biases
tf.summary.histogram('pre_activations', preactivate)
activations = act(preactivate, name='activation')
tf.summary.histogram('activations', activations)
return activations
hidden1 = nn_layer(x, 784, 500, 'layer1')
with tf.name_scope('dropout'):
keep_prob = tf.placeholder(tf.float32)
tf.summary.scalar('dropout_keep_probability', keep_prob)
dropped = tf.nn.dropout(hidden1, keep_prob)
# Do not apply softmax activation yet, see below.
y = nn_layer(dropped, 500, 10, 'layer2', act=tf.identity)
with tf.name_scope('cross_entropy'):
# The raw formulation of cross-entropy,
#
# tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(tf.softmax(y)),
# reduction_indices=[1]))
#
# can be numerically unstable.
#
# So here we use tf.nn.softmax_cross_entropy_with_logits on the
# raw outputs of the nn_layer above, and then average across
# the batch.
diff = tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y)
with tf.name_scope('total'):
cross_entropy = tf.reduce_mean(diff)
tf.summary.scalar('cross_entropy', cross_entropy)
with tf.name_scope('train'):
train_step = tf.train.AdamOptimizer(
FLAGS.learning_rate).minimize(cross_entropy)
with tf.name_scope('accuracy'):
with tf.name_scope('correct_prediction'):
correct_prediction = tf.equal(tf.argmax(y, 1),
tf.argmax(y_, 1))
with tf.name_scope('accuracy'):
accuracy = tf.reduce_mean(
tf.cast(correct_prediction, tf.float32))
tf.summary.scalar('accuracy', accuracy)
# Merge all the summaries and write them out to /tmp/mnist_logs (by default)
merged = tf.summary.merge_all()
train_writer = tf.summary.FileWriter(FLAGS.log_dir + '/train',
sess.graph)
test_writer = tf.summary.FileWriter(FLAGS.log_dir + '/test')
sess.run(tf.global_variables_initializer())
# Train the model, and also write summaries.
# Every 10th step, measure test-set accuracy, and write test summaries
# All other steps, run train_step on training data, & add training summaries
def feed_dict(train):
"""Make a TensorFlow feed_dict: maps data onto Tensor placeholders."""
if train or FLAGS.fake_data:
xs, ys = mnist.train.next_batch(100, fake_data=FLAGS.fake_data)
k = FLAGS.dropout
else:
xs, ys = mnist.test.images, mnist.test.labels
k = 1.0
return {x: xs, y_: ys, keep_prob: k}
for i in range(FLAGS.max_steps):
if i % 10 == 0: # Record summaries and test-set accuracy
summary, acc = sess.run([merged, accuracy],
feed_dict=feed_dict(False))
test_writer.add_summary(summary, i)
print('Accuracy at step %s: %s' % (i, acc))
else: # Record train set summaries, and train
if i % 100 == 99: # Record execution stats
run_metadata = tf.RunMetadata()
summary, _ = sess.run([merged, train_step],
feed_dict=feed_dict(True))
train_writer.add_run_metadata(run_metadata, 'step%03d' % i)
train_writer.add_summary(summary, i)
print('Adding run metadata for', i)
else: # Record a summary
summary, _ = sess.run([merged, train_step],
feed_dict=feed_dict(True))
train_writer.add_summary(summary, i)
train_writer.close()
test_writer.close()
# Export model
# WARNING(break-tutorial-inline-code): The following code snippet is
# in-lined in tutorials, please update tutorial documents accordingly
# whenever code changes.
with tf.device("/cpu:0"):
export_path = FLAGS.model_dir + '/1'
print('Exporting trained model to', export_path)
builder = tf.saved_model.builder.SavedModelBuilder(export_path)
tensor_info_x = tf.saved_model.utils.build_tensor_info(x)
tensor_info_y = tf.saved_model.utils.build_tensor_info(y)
tensor_info_keepp = tf.saved_model.utils.build_tensor_info(keep_prob)
prediction_signature = (
tf.saved_model.signature_def_utils.build_signature_def(
inputs={
'images': tensor_info_x,
'keep_prob': tensor_info_keepp
},
outputs={'scores': tensor_info_y},
method_name=tf.saved_model.signature_constants.
PREDICT_METHOD_NAME))
legacy_init_op = tf.group(tf.tables_initializer(),
name='legacy_init_op')
builder.add_meta_graph_and_variables(
sess, [tf.saved_model.tag_constants.SERVING],
signature_def_map={
'predict_images': prediction_signature,
},
clear_devices=True,
legacy_init_op=legacy_init_op)
builder.save()
print('Done exporting!')
tf.app.flags.DEFINE_integer('model_version', 1, 'version number of the model.')
tf.app.flags.DEFINE_string('work_dir', '/tmp', 'Working directory.')
FLAGS = tf.app.flags.FLAGS
sess = tf.InteractiveSession()
x = tf.placeholder(tf.float32, shape=[None, 784], name='x')
# x = tf.reshape(images, [-1, 784])
y_ = tf.placeholder(tf.float32, shape=[None, 10])
w = tf.Variable(tf.zeros([784, 10]))
b = tf.Variable(tf.zeros([10]))
sess.run(tf.global_variables_initializer())
scores = tf.nn.softmax(tf.matmul(x, w) + b, name='scores')
cross_entropy = -tf.reduce_sum(y_ * tf.log(scores))
train_step = tf.train.GradientDescentOptimizer(0.01).minimize(cross_entropy)
mnist = mnist_input_data.read_data_sets(FLAGS.work_dir, one_hot=True)
for _ in range(FLAGS.training_iteration):
batch = mnist.train.next_batch(50)
train_step.run(feed_dict={x: batch[0], y_: batch[1]})
# print("train_step==")
# print(train_step)
# print("scores==")
# print(scores)
# print(scores.eval())
sess.close()
def main(args):
prog_start_time = time.time()
# handle parameters
parser = argparse.ArgumentParser()
add_parameters(parser)
args = parser.parse_args()
print('Training for %i iterations' % args.iterations)
# Load training and eval data
mnist_data = mnist_input_data.read_data_sets(args.input_dir, one_hot=True)
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])
# Create the model
model = cnn_model(x)
y = tf.identity(model, name='y')
# Calculate Loss (for both TRAIN and EVAL modes)
loss = tf.losses.softmax_cross_entropy(onehot_labels=y_, logits=y)
# Training
train_step = tf.train.GradientDescentOptimizer(
learning_rate=args.step_size).minimize(loss)
# Evaluation
correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, 'float'))
# Track cost and accuracy for the training and test sets in TensorBoard
tb_writer = tf.summary.FileWriter(args.tf_log,
graph=tf.get_default_graph())
train_cost_op = tf.summary.scalar("train_cost", loss)
train_acc_op = tf.summary.scalar("train_accuracy", accuracy)
train_stats_op = tf.summary.merge([train_cost_op, train_acc_op])
test_cost_op = tf.summary.scalar("test_cost", loss)
test_acc_op = tf.summary.scalar("test_accuracy", accuracy)
test_stats_op = tf.summary.merge([test_cost_op, test_acc_op])
# Train the model
training_start_time = time.time()
sess = tf.InteractiveSession()
sess.run(tf.global_variables_initializer())
for i in range(args.iterations):
batch = mnist_data.train.next_batch(args.batch_size)
_, train_summary, train_cost, train_acc = sess.run(
[train_step, train_stats_op, loss, accuracy],
feed_dict={
x: batch[0],
y_: batch[1]
})
if (i % args.stats_interval == 0 or i == (args.iterations - 1)):
test_acc, test_cost, test_summary = sess.run(
[accuracy, loss, test_stats_op],
feed_dict={
x: mnist_data.test.images,
y_: mnist_data.test.labels
})
print("step=", i, "accuracy: train=", train_acc, "test=", test_acc,
"cost: train=", train_cost, "test=", test_cost)
tb_writer.add_summary(train_summary, i)
tb_writer.add_summary(test_summary, i)
tb_writer.flush()
training_elapsed_time = time.time() - training_start_time
# Save the model
export_path = args.save_dir
print('Exporting trained model to', export_path)
builder = tf.saved_model.builder.SavedModelBuilder(export_path)
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 range(10)]))
prediction_classes = table.lookup(tf.to_int64(indices))
# Build the signature_def_map.
classification_inputs = tf.saved_model.utils.build_tensor_info(
serialized_tf_example)
classification_outputs_classes = tf.saved_model.utils.build_tensor_info(
prediction_classes)
classification_outputs_scores = tf.saved_model.utils.build_tensor_info(
values)
classification_signature = (
tf.saved_model.signature_def_utils.build_signature_def(
inputs={
tf.saved_model.signature_constants.CLASSIFY_INPUTS:
classification_inputs
},
outputs={
tf.saved_model.signature_constants.CLASSIFY_OUTPUT_CLASSES:
classification_outputs_classes,
tf.saved_model.signature_constants.CLASSIFY_OUTPUT_SCORES:
classification_outputs_scores
},
method_name=tf.saved_model.signature_constants.CLASSIFY_METHOD_NAME
))
tensor_info_x = tf.saved_model.utils.build_tensor_info(x)
tensor_info_y = tf.saved_model.utils.build_tensor_info(y)
prediction_signature = (
tf.saved_model.signature_def_utils.build_signature_def(
inputs={'inputs': tensor_info_x},
outputs={'outputs': tensor_info_y},
method_name=tf.saved_model.signature_constants.PREDICT_METHOD_NAME)
)
legacy_init_op = tf.group(tf.tables_initializer(), name='legacy_init_op')
builder.add_meta_graph_and_variables(
sess, [tf.saved_model.tag_constants.SERVING],
signature_def_map={
'predict_images':
prediction_signature,
tf.saved_model.signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY:
classification_signature,
},
legacy_init_op=legacy_init_op)
builder.save(as_text=args.use_text)
prog_elapsed_time = time.time() - prog_start_time
print("Training seconds: {}. Total seconds: {}".format(
training_elapsed_time, prog_elapsed_time))
def main(_):
if len(sys.argv) < 2 or sys.argv[-1].startswith('-'):
print('Usage: mnist_saved_model.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()
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.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)
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(tf.compat.as_bytes(export_path_base),
tf.compat.as_bytes(str(FLAGS.model_version)))
print('Exporting trained model to', export_path)
builder = tf.saved_model.builder.SavedModelBuilder(export_path)
# 生成 输入tensor x 和 输出tensor y的 tensor info
tensor_info_x = tf.saved_model.utils.build_tensor_info(x)
tensor_info_y = tf.saved_model.utils.build_tensor_info(y)
# 生成prediction_signature
prediction_signature = (
tf.saved_model.signature_def_utils.build_signature_def(
# 客户端request的时候 输入的key 要与设置的相同
inputs={'images': tensor_info_x},
# 获得的response 结构体中 通过 设置的key('score')字段获得结果
outputs={'scores': tensor_info_y},
method_name=tf.saved_model.signature_constants.PREDICT_METHOD_NAME)
)
# 定义 meta
builder.add_meta_graph_and_variables(
sess,
[tf.saved_model.tag_constants.SERVING],
signature_def_map={
# 用于客户端request时 的 signature_name
'predict_images': prediction_signature,
},
main_op=tf.tables_initializer(),
# 向上兼容
strip_default_attrs=True)
builder.save()
print('Done exporting!')