def main():
resize_shape = 64
print "data is loading..."
train_X, train_Y, test_X, test_Y = load_data(resize_shape)
print "data is loaded"
print "feature engineering..."
learning_rate = 0.01
training_iters = 100000
batch_size = 128
display_step = 10
# Network Parameters
n_input = resize_shape*resize_shape # MNIST data input (img shape: 28*28)
n_classes = 62 # MNIST total classes (0-9 digits)
dropout = 0.5 # Dropout, probability to keep units
with tf.Session() as sess:
cnn = CNN(sess, learning_rate, training_iters, batch_size, display_step, n_input, n_classes, dropout,resize_shape)
train_X = cnn.inference(train_X)
test_X = cnn.inference(test_X)
print "feature engineering is complete"
print 'training phase'
clf = svm.LinearSVC().fit(train_X, train_Y)
print 'test phase'
predicts = clf.predict(test_X)
# measure function
print 'measure phase'
print confusion_matrix(test_Y, predicts)
print f1_score(test_Y, predicts, average=None)
print precision_score(test_Y, predicts, average=None)
print recall_score(test_Y, predicts, average=None)
print accuracy_score(test_Y, predicts)
def main():
with tf.Graph().as_default():
cnn = CNN(image_size=FLAGS.image_size, class_count=len(Channel))
images, labels = load_data(
'data/test/data.csv',
batch_size=FLAGS.batch_size,
image_size=FLAGS.image_size,
class_count=len(Channel),
shuffle=False)
keep_prob = tf.placeholder(tf.float32)
logits = cnn.inference(images, keep_prob)
accuracy = cnn.accuracy(logits, labels)
saver = tf.train.Saver()
init_op = tf.global_variables_initializer()
with tf.Session() as sess:
sess.run(init_op)
saver.restore(sess, os.path.join(LOG_DIR, 'model.ckpt'))
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
accuracy_value = sess.run(accuracy, feed_dict={keep_prob: 0.5})
print(f'test accuracy: {accuracy_value}')
coord.request_stop()
coord.join(threads)
def main(imagepath):
cnn = CNN(image_size=FLAGS.image_size, class_count=len(Channel))
image = load_image(imagepath, image_size=FLAGS.image_size)
keep_prob = tf.placeholder(tf.float32)
logits = cnn.inference(image, keep_prob, softmax=True)
sess = tf.InteractiveSession()
saver = tf.train.Saver()
sess.run(tf.global_variables_initializer())
saver.restore(sess, os.path.join(LOG_DIR, 'model.ckpt'))
softmax = sess.run(logits, feed_dict={keep_prob: 1.0}).flatten()
print_results(imagepath, softmax)
class Prob(object):
def __init__(self):
self.c = CNN()
self.images_placeholder = tf.placeholder("float")
self.keep_prob = tf.placeholder("float")
self.softmax = self.c.inference(self.images_placeholder,
self.keep_prob)
self.saver = tf.train.Saver()
def get_prob(self, image_id):
save_path = get_save_path(image_id)
face_list = [img for img in os.listdir(save_path) if "face_" in img]
p = {}
for image_name in face_list:
_image_num = re.search(r"_\d+_", image_name).group()
image_num = _image_num[1:][:-1]
CVimage = cv2.imread(save_path + "/" + image_name)
image = self.c.shape_CVimage(CVimage)
with tf.Session() as sess:
ckpt = tf.train.get_checkpoint_state('./')
if ckpt: # checkpointがある場合
last_model = ckpt.model_checkpoint_path # 最後に保存したmodelへのパス
self.saver.restore(sess, last_model) # 変数データの読み込み
#self.saver.restore(sess, "/root/share/domain/model.ckpt")
prob = sess.run(self.softmax,
feed_dict={
self.images_placeholder: [image],
self.keep_prob: 1.0
})[0][0]
if prob:
p[image_num] = float(prob)
else:
res = {
"status": "error",
"message": "can not get valid probability",
}
return res
res = {
"status": "success",
"data_type": "detail",
"detail": {
"probability": p
}
}
return res
def export():
output_dir = os.path.join(FLAGS.ckpt, "export")
if tf.gfile.Exists(output_dir) == True:
tf.gfile.DeleteRecursively(output_dir)
if tf.gfile.Exists(output_dir) == False:
tf.gfile.MakeDirs(output_dir)
### EDIT TRAINING GRAPH ###
with tf.Graph().as_default() as g:
nn = CNN(FLAGS.network, FLAGS.num_of_classes, 1, FLAGS.image_size,
FLAGS.image_crop_size, FLAGS.log_input, FLAGS.grayscale,
FLAGS.log_feature, FLAGS.use_fp16)
with tf.device("/cpu:0"):
image = tf.placeholder(tf.float32,
shape=(None, None, 3),
name="image")
image = nn.input(image, True, False)
dtype = tf.float16 if FLAGS.use_fp16 else tf.float32
keep_prob = tf.constant(1.0, dtype=dtype)
batch_size = tf.constant(1)
logits = nn.inference(image, keep_prob, batch_size)
# Calculate predictions.
logits = tf.cast(logits, tf.float32)
softmax = tf.nn.softmax(logits)
# Restore the moving average version of the learned variables for eval.
variable_averages = tf.train.ExponentialMovingAverage(
CNN.MOVING_AVERAGE_DECAY)
variables_to_restore = variable_averages.variables_to_restore()
saver = tf.train.Saver(variables_to_restore)
# Build the summary operation based on the TF collection of Summaries.
"""
summary_op = tf.summary.merge_all()
summary_writer = tf.summary.FileWriter(output_dir, g)
"""
with tf.Session() as sess:
ckpt = tf.train.get_checkpoint_state(FLAGS.ckpt)
if ckpt and ckpt.model_checkpoint_path:
saver.restore(sess, ckpt.model_checkpoint_path)
global_step = ckpt.model_checkpoint_path.split('/')[-1].split(
'-')[-1]
else:
print('No checkpoint file found')
return
tf.train.Saver().save(
sess,
os.path.join(output_dir, 'model.ckpt'),
global_step=tf.convert_to_tensor(global_step))
tf.train.write_graph(sess.graph.as_graph_def(),
output_dir,
'graph.pbtxt',
as_text=True)
"""
summary = tf.Summary()
summary.ParseFromString(sess.run(summary_op))
summary_writer.add_summary(summary, global_step)
"""
### EXPORT MODEL ###
graph_path = os.path.join(output_dir, 'graph.pbtxt')
if tf.gfile.Exists(graph_path) == False:
raise ValueError('Graph not found({})'.format(graph_path))
ckpt = tf.train.get_checkpoint_state(output_dir)
ckpt_path = ckpt.model_checkpoint_path
if ckpt == False or ckpt_path == False:
raise ValueError('Check point not found.')
output_path = os.path.join(output_dir, 'frozen.pb')
optimized_output_path = os.path.join(output_dir, 'optimized.pb')
freeze_graph.freeze_graph(input_graph=graph_path,
input_saver="",
input_binary=False,
input_checkpoint=ckpt_path,
output_node_names="softmax_linear/softmax",
restore_op_name="save/restore_all",
filename_tensor_name="save/Const:0",
output_graph=output_path,
clear_devices=True,
initializer_nodes="")
input_graph_def = tf.GraphDef()
with tf.gfile.Open(output_path, "r") as f:
data = f.read()
input_graph_def.ParseFromString(data)
output_graph_def = optimize_for_inference_lib.optimize_for_inference(
input_graph_def, ['image'], ["softmax_linear/softmax"],
tf.float32.as_datatype_enum)
f = tf.gfile.FastGFile(optimized_output_path, "w")
f.write(output_graph_def.SerializeToString())
output_size = os.path.getsize(output_path)
optimized_output_size = os.path.getsize(optimized_output_path)
print('Model Exported successfuly.')
print('- Frozen Model: {} ({})'.format(output_path,
_humansize(output_size)))
print('- Optimized Model: {} ({})'.format(
optimized_output_path, _humansize(optimized_output_size)))
def evaluate(data_dir):
# PreProcess
if not data_dir:
raise ValueError('Please supply a data_dir')
if tf.gfile.Exists(FLAGS.ckpt) == False:
raise ValueError('Please supply a checkpoint')
if FLAGS.event_dir == None:
FLAGS.event_dir = os.path.join(FLAGS.ckpt, 'event')
#raise ValueError('Please supply a event_dir')
if tf.gfile.Exists(FLAGS.event_dir):
tf.gfile.DeleteRecursively(FLAGS.event_dir)
tf.gfile.MakeDirs(FLAGS.event_dir)
num_of_classes = _num_of_folders(data_dir)
if num_of_classes <= 0:
raise ValueError('Invalid data_dir')
num_of_samples = _num_of_files(data_dir)
if num_of_samples == None:
raise ValueError('Please supply num_of_samples.')
print('[ SUMMARY ]')
print('Num of classes: {}'.format(num_of_classes))
print('Num of samples: {}'.format(num_of_samples))
"""Eval CIFAR-10 for a number of steps."""
with tf.Graph().as_default() as g:
info = [['Number of classes', str(num_of_classes)],
['Number of samples', str(num_of_samples)],
['Image size', str(FLAGS.image_size)],
['Image Crop size',
str(FLAGS.image_crop_size)],
['Grayscale', str(FLAGS.grayscale)],
['Use Float16', str(FLAGS.use_fp16)]]
info_summary = tf.summary.text('NetworkInfo',
tf.convert_to_tensor(info),
collections=[])
nn = CNN(FLAGS.network, num_of_classes, num_of_samples,
FLAGS.image_size, FLAGS.image_crop_size, FLAGS.log_input,
FLAGS.grayscale, FLAGS.log_feature, FLAGS.use_fp16)
with tf.device("/cpu:0"):
# Get images and labels for CIFAR-10.
images, labels, filenames = nn.inputs(data_dir, FLAGS.batch_size,
FLAGS.batch_size * 100,
FLAGS.scaling, False)
# Build a Graph that computes the logits predictions from the
# inference model.
dtype = tf.float16 if FLAGS.use_fp16 else tf.float32
keep_prob = tf.constant(1.0, dtype=dtype)
batch_size = tf.constant(FLAGS.batch_size)
logits = nn.inference(images, keep_prob, batch_size)
# Calculate predictions.
logits = tf.cast(logits, tf.float32)
top_k_op = tf.nn.in_top_k(logits, labels, 1)
# Restore the moving average version of the learned variables for eval.
variable_averages = tf.train.ExponentialMovingAverage(
CNN.MOVING_AVERAGE_DECAY)
variables_to_restore = variable_averages.variables_to_restore()
saver = tf.train.Saver(variables_to_restore)
# Build the summary operation based on the TF collection of Summaries.
summary_op = tf.summary.merge_all()
summary_writer = tf.summary.FileWriter(FLAGS.event_dir, g)
#eval_once(saver, summary_writer, top_k_op, summary_op, labels, keep_prob)
with tf.Session() as sess:
summary_writer.add_summary(sess.run(info_summary))
ckpt = tf.train.get_checkpoint_state(FLAGS.ckpt)
if ckpt and ckpt.model_checkpoint_path:
# Restores from checkpoint
saver.restore(sess, ckpt.model_checkpoint_path)
# Assuming model_checkpoint_path looks something like:
# /my-favorite-path/cifar10_train/model.ckpt-0,
# extract global_step from it.
global_step = ckpt.model_checkpoint_path.split('/')[-1].split(
'-')[-1]
else:
print('No checkpoint file found')
return
# Start the queue runners.
coord = tf.train.Coordinator()
try:
threads = []
for qr in tf.get_collection(tf.GraphKeys.QUEUE_RUNNERS):
threads.extend(
qr.create_threads(sess,
coord=coord,
daemon=True,
start=True))
num_iter = int(math.ceil(num_of_samples / FLAGS.batch_size))
true_count = 0 # Counts the number of correct predictions.
total_sample_count = num_iter * FLAGS.batch_size
step = 0
#print(sess.run(labels, {keep_prob: 1.0}))
errors = []
while step < num_iter and not coord.should_stop():
predictions, labels_value, filenames_value = sess.run(
[top_k_op, labels, filenames])
true_count += np.sum(predictions)
step += 1
print(labels_value)
#print(predictions)
errors += [
x for i, x in enumerate(filenames_value)
if predictions[i] == False
]
# Print errors
print('Errors:')
print('\n'.join(f for f in errors))
# Copy Errors
copyErrors = True if FLAGS.error_dir != None else False
if copyErrors:
_copy_errors(errors, FLAGS.error_dir)
# Compute precision @ 1.
precision = true_count / total_sample_count
print('%s: precision @ 1 = %.3f' % (datetime.now(), precision))
summary = tf.Summary()
summary.ParseFromString(sess.run(summary_op))
summary.value.add(tag='Precision @ 1', simple_value=precision)
summary_writer.add_summary(summary, global_step)
except Exception as e: # pylint: disable=broad-except
coord.request_stop(e)
coord.request_stop()
coord.join(threads, stop_grace_period_secs=10)
def evaluate_single():
# PreProcess
if not FLAGS.image:
raise ValueError('Please supply a image')
if tf.gfile.Exists(FLAGS.image) == False:
raise ValueError('Image not found.')
if tf.gfile.Exists(FLAGS.ckpt) == False:
raise ValueError('Please supply a checkpoint')
if FLAGS.num_of_classes == None:
raise ValueError('Please supply num_of_classes.')
if FLAGS.event_dir == None:
FLAGS.event_dir = os.path.join(FLAGS.ckpt, 'event')
#raise ValueError('Please supply a event_dir')
if tf.gfile.Exists(FLAGS.event_dir):
tf.gfile.DeleteRecursively(FLAGS.event_dir)
tf.gfile.MakeDirs(FLAGS.event_dir)
with tf.Graph().as_default() as g:
nn = CNN(FLAGS.network, FLAGS.num_of_classes, 1, FLAGS.image_size,
FLAGS.image_crop_size, True, FLAGS.grayscale, True,
FLAGS.use_fp16)
with tf.device("/cpu:0"):
file_data = tf.read_file(FLAGS.image)
image = tf.image.decode_jpeg(file_data, channels=3)
image = nn.input(image, True, False)
dtype = tf.float16 if FLAGS.use_fp16 else tf.float32
keep_prob = tf.constant(1.0, dtype=dtype)
batch_size = tf.constant(1)
logits = nn.inference(image, keep_prob, batch_size)
# Calculate predictions.
logits = tf.cast(logits, tf.float32)
softmax = tf.nn.softmax(logits)
# Restore the moving average version of the learned variables for eval.
variable_averages = tf.train.ExponentialMovingAverage(
CNN.MOVING_AVERAGE_DECAY)
variables_to_restore = variable_averages.variables_to_restore()
saver = tf.train.Saver(variables_to_restore)
# Build the summary operation based on the TF collection of Summaries.
info = [['Image', str(FLAGS.image)],
['Image size', str(FLAGS.image_size)],
['Image Crop size',
str(FLAGS.image_crop_size)],
['Grayscale', str(FLAGS.grayscale)],
['Use Float16', str(FLAGS.use_fp16)]]
info_summary = tf.summary.text('Info',
tf.convert_to_tensor(info),
collections=[])
summary_op = tf.summary.merge_all()
summary_writer = tf.summary.FileWriter(FLAGS.event_dir, g)
with tf.Session() as sess:
summary_writer.add_summary(sess.run(info_summary))
ckpt = tf.train.get_checkpoint_state(FLAGS.ckpt)
if ckpt and ckpt.model_checkpoint_path:
saver.restore(sess, ckpt.model_checkpoint_path)
global_step = ckpt.model_checkpoint_path.split('/')[-1].split(
'-')[-1]
else:
print('No checkpoint file found')
return
result = sess.run(softmax)
result = result[0]
print('- Result')
for i, v in enumerate(result):
print('[%d]: %f' % (i, v))
summary = tf.Summary()
summary.ParseFromString(sess.run(summary_op))
summary_writer.add_summary(summary, global_step)
def train(data_dir):
# PreProcess
if not data_dir:
raise ValueError('Please supply a data_dir')
num_of_classes = _num_of_folders(data_dir)
if num_of_classes <= 0:
raise ValueError('Invalid num_of_classes')
num_of_samples = _num_of_files(data_dir)
if num_of_samples == None:
raise ValueError('Please supply num_of_samples.')
print('[ SUMMARY ]')
print('Num of classes: {}'.format(num_of_classes))
print('Num of samples: {}'.format(num_of_samples))
# Training
with tf.Graph().as_default():
info = [['Number of classes', str(num_of_classes)],
['Number of samples', str(num_of_samples)],
['Image size', str(FLAGS.image_size)],
['Image Crop size',
str(FLAGS.image_crop_size)],
['Grayscale', str(FLAGS.grayscale)],
['Use Float16', str(FLAGS.use_fp16)]]
tf.summary.text('NetworkInfo',
tf.convert_to_tensor(info),
collections=[])
nn = CNN(FLAGS.network, num_of_classes, num_of_samples,
FLAGS.image_size, FLAGS.image_crop_size, FLAGS.log_input,
FLAGS.grayscale, FLAGS.log_feature, FLAGS.use_fp16)
global_step = tf.contrib.framework.get_or_create_global_step()
with tf.device('/gpu:0'):
images, labels, filenames = nn.inputs(data_dir, FLAGS.batch_size,
FLAGS.batch_size * 500,
FLAGS.scaling,
FLAGS.destorted)
dtype = tf.float16 if FLAGS.use_fp16 else tf.float32
keep_prob = tf.placeholder(dtype, name="keep_prob")
batch_size = tf.placeholder(tf.float32, name="batch_size")
logits = nn.inference(images, keep_prob, batch_size)
loss, accuracy = nn.loss(logits, labels)
train_op = nn.train(loss, global_step, FLAGS.batch_size,
FLAGS.learning_rate, FLAGS.num_epochs_per_decay,
FLAGS.learning_rate_decay_factor)
class _LoggerHook(tf.train.SessionRunHook):
def begin(self):
self._step = -1
self._start_time = time.time()
def before_run(self, run_context):
self._step += 1
return tf.train.SessionRunArgs([loss, accuracy])
def after_run(self, run_context, run_values):
if self._step % FLAGS.log_frequency == 0:
current_time = time.time()
duration = current_time - self._start_time
self._start_time = current_time
loss_value, accuracy_value = run_values.results
examples_per_sec = FLAGS.log_frequency * FLAGS.batch_size / duration
sec_per_batch = float(duration / FLAGS.log_frequency)
format_str = (
'%s: step %d, accuracy = %.2f, loss = %.2f (%1.f examples/sec; %.3f sec/batch)'
)
print(format_str %
(datetime.now(), self._step, accuracy_value,
loss_value, examples_per_sec, sec_per_batch))
conf = tf.ConfigProto(log_device_placement=FLAGS.log_device_placement,
allow_soft_placement=True,
intra_op_parallelism_threads=8)
with tf.train.MonitoredTrainingSession(
checkpoint_dir=FLAGS.ckpt,
hooks=[
tf.train.StopAtStepHook(last_step=FLAGS.max_steps),
tf.train.NanTensorHook(loss),
_LoggerHook()
],
save_summaries_steps=FLAGS.save_steps,
config=conf) as mon_sess:
while not mon_sess.should_stop():
mon_sess.run(train_op, {
keep_prob: FLAGS.keep_prob,
batch_size: FLAGS.batch_size
})
train_images, train_labels = fetch_images_and_labels(TRAIN_DIR)
train_images, train_labels = shaffle_images_and_labels(train_images,
train_labels)
test_images, test_labels = fetch_images_and_labels(TEST_DIR)
test_images, test_labels = shaffle_images_and_labels(test_images, test_labels)
cnn = CNN(image_size=FLAGS.image_size, class_count=len(CLASSES))
with tf.Graph().as_default():
x = tf.placeholder(tf.float32, [None, PIXEL_COUNT])
labels = tf.placeholder(tf.float32, [None, len(CLASSES)])
keep_prob = tf.placeholder(tf.float32)
y = cnn.inference(x, keep_prob)
v = cnn.cross_entropy(y, labels)
train_step = cnn.train_step(v, FLAGS.learning_rate)
accuracy = cnn.accuracy(y, labels)
saver = tf.train.Saver()
init = tf.global_variables_initializer()
with tf.Session() as sess:
sess.run(init)
summary_op = tf.summary.merge_all()
summary_writer = tf.summary.FileWriter(LOG_DIR, sess.graph)
for i in range(FLAGS.step_count):
for j in range(int(len(train_images) / FLAGS.batch_size)):
cv2.putText(image, CLASSES[prediction], (x, y), cv2.FONT_HERSHEY_DUPLEX,
0.5, (255, 255, 102), 1, CV_AA)
return image
test_image = cv2.imread(sys.argv[1])
face_rects = detect(test_image)
face_images = get_face_images(test_image, face_rects)
x = tf.placeholder(tf.float32, [None, PIXEL_COUNT])
labels = tf.placeholder(tf.float32, [None, len(CLASSES)])
keep_prob = tf.placeholder(tf.float32)
cnn = CNN(image_size=FLAGS.image_size, class_count=len(CLASSES))
y = cnn.inference(x, keep_prob, softmax=True)
sess = tf.InteractiveSession()
saver = tf.train.Saver()
sess.run(tf.global_variables_initializer())
saver.restore(sess, os.path.join(LOG_DIR, 'model.ckpt'))
out_image = test_image.copy()
for i in range(len(face_images)):
face_image = face_images[i]
softmax = sess.run(y, feed_dict={
x: [face_image],
keep_prob: 1.0
}).flatten()
prediction = np.argmax(softmax)
def export_serving():
output_dir = os.path.join(FLAGS.ckpt, "export_serving")
if tf.gfile.Exists(output_dir) == True:
tf.gfile.DeleteRecursively(output_dir)
if tf.gfile.Exists(output_dir) == False:
tf.gfile.MakeDirs(output_dir)
### EDIT TRAINING GRAPH ###
with tf.Graph().as_default() as g:
nn = CNN(FLAGS.network, FLAGS.num_of_classes, 1, FLAGS.image_size,
FLAGS.image_crop_size, FLAGS.log_input, FLAGS.grayscale,
FLAGS.log_feature, FLAGS.use_fp16)
with tf.device("/cpu:0"):
image = tf.placeholder(tf.float32,
shape=(None, None, 3),
name="image")
image = nn.input(image, True, False)
dtype = tf.float16 if FLAGS.use_fp16 else tf.float32
keep_prob = tf.constant(1.0, dtype=dtype)
batch_size = tf.constant(1)
logits = nn.inference(image, keep_prob, batch_size)
# Calculate predictions.
logits = tf.cast(logits, tf.float32)
softmax = tf.nn.softmax(logits)
# Restore the moving average version of the learned variables for eval.
variable_averages = tf.train.ExponentialMovingAverage(
CNN.MOVING_AVERAGE_DECAY)
variables_to_restore = variable_averages.variables_to_restore()
saver = tf.train.Saver(variables_to_restore)
# Build the summary operation based on the TF collection of Summaries.
"""
summary_op = tf.summary.merge_all()
summary_writer = tf.summary.FileWriter(output_dir, g)
"""
with tf.Session() as sess:
ckpt = tf.train.get_checkpoint_state(FLAGS.ckpt)
if ckpt and ckpt.model_checkpoint_path:
saver.restore(sess, ckpt.model_checkpoint_path)
global_step = ckpt.model_checkpoint_path.split('/')[-1].split(
'-')[-1]
else:
print('No checkpoint file found')
return
tf.train.Saver().save(
sess,
os.path.join(output_dir, 'model.ckpt'),
global_step=tf.convert_to_tensor(global_step))
tf.train.write_graph(sess.graph.as_graph_def(),
output_dir,
'graph.pbtxt',
as_text=True)
"""
summary = tf.Summary()
summary.ParseFromString(sess.run(summary_op))
summary_writer.add_summary(summary, global_step)
"""
### EXPORT MODEL ###
export_path_base = output_dir
export_path = os.path.join(
tf.compat.as_bytes(export_path_base),
tf.compat.as_bytes(str(FLAGS.model_version)))
print('Exporting model to '.format(export_path))
builder = tf.saved_model.builder.SavedModelBuilder(export_path)
# Build the signature_def_map.
classification_inputs = tf.saved_model.utils.build_tensor_info(
image)
classification_outputs_classes = tf.saved_model.utils.build_tensor_info(
softmax)
classification_outputs_scores = tf.saved_model.utils.build_tensor_info(
softmax)
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(image)
tensor_info_y = tf.saved_model.utils.build_tensor_info(softmax)
prediction_signature = (
tf.saved_model.signature_def_utils.build_signature_def(
inputs={'image': tensor_info_x},
outputs={'score': 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_image':
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!')
