def recognize_char_p():
label_map = load_label_map()
model = load_model_nn()
x = model['x']
keep_prob = model['keep_prob']
saver = model['saver']
prediction = model['prediction']
graph = model['graph']
model_ckpt_path, _ = find_model_ckpt()
# print('load check-point %s'%model_ckpt_path, file=sys.stderr)
with tf.Session(graph=graph) as session:
tf.global_variables_initializer().run()
saver.restore(session, model_ckpt_path)
while True:
sys.stdout.flush()
captcha_path = input().strip()
if captcha_path == '$exit': # for close session
break
im = np.reshape(cv2.imread(captcha_path, cv2.IMREAD_GRAYSCALE),
IMAGE_SIZE)
label = prediction.eval(feed_dict={
x: [im],
keep_prob: 1.0
},
session=session)[0]
sys.stdout.write(label_map[label])
sys.stdout.write('\n')
def recognize_p():
"""
captcha_path
$exit to exit
"""
# print("recognize_p")
label_map = load_label_map()
model = load_model_nn()
x = model['x']
keep_prob = model['keep_prob']
saver = model['saver']
prediction = model['prediction']
graph = model['graph']
model_ckpt_path, _ = find_model_ckpt()
print('load check-point %s' % model_ckpt_path, file=sys.stderr)
with tf.Session(graph=graph) as session:
tf.global_variables_initializer().run()
saver.restore(session, model_ckpt_path)
while True:
sys.stdout.flush()
captcha_path = input().strip()
# print("_recoginze", captcha_path)
if captcha_path == '$exit': # for close session
break
spliter = Spliter(os.curdir)
try:
letters = spliter.split_letters(captcha_path)
formatted_letters = map(spliter.format_splited_image, letters)
formatted_letters = [
letter.reshape(image_size) for letter in formatted_letters
]
except Exception as ex:
sys.stdout.write('\n')
continue
result = []
for letter in formatted_letters:
label = prediction.eval(feed_dict={
x: [letter],
keep_prob: 1.0
},
session=session)[0]
result.append(label_map[label])
sys.stdout.write(label_map[label])
sys.stdout.write('\n')
def recognize_p():
"""
captcha_path
$exit to exit
"""
# print("recognize_p")
label_map = load_label_map()
model = load_model_nn()
x = model['x']
keep_prob = model['keep_prob']
saver = model['saver']
prediction = model['prediction']
graph = model['graph']
model_ckpt_path, _ = find_model_ckpt()
print('load check-point %s' % model_ckpt_path, file=sys.stderr)
with tf.Session(graph=graph) as session:
tf.global_variables_initializer().run()
saver.restore(session, model_ckpt_path)
while True:
sys.stdout.flush()
captcha_path = input().strip()
# print("_recoginze", captcha_path)
if captcha_path == '$exit': # for close session
break
spliter = Spliter(os.curdir)
try:
letters = spliter.split_letters(captcha_path)
formatted_letters = map(spliter.format_splited_image,letters)
formatted_letters = [letter.reshape(image_size) for letter in formatted_letters]
except Exception as ex:
sys.stdout.write('\n')
continue
result = []
for letter in formatted_letters:
label = prediction.eval(feed_dict={x: [letter], keep_prob: 1.0}, session=session)[0]
result.append(label_map[label])
sys.stdout.write(label_map[label])
sys.stdout.write('\n')
def recognize_char_p():
label_map = load_label_map()
model = load_model_nn()
x = model['x']
keep_prob = model['keep_prob']
saver = model['saver']
prediction = model['prediction']
graph = model['graph']
model_ckpt_path, _ = find_model_ckpt()
# print('load check-point %s'%model_ckpt_path, file=sys.stderr)
with tf.Session(graph=graph) as session:
tf.global_variables_initializer().run()
saver.restore(session, model_ckpt_path)
while True:
sys.stdout.flush()
captcha_path = input().strip()
if captcha_path == '$exit': # for close session
break
im = np.reshape(cv2.imread(captcha_path, cv2.IMREAD_GRAYSCALE), IMAGE_SIZE)
label = prediction.eval(feed_dict={x: [im], keep_prob: 1.0}, session=session)[0]
sys.stdout.write(label_map[label])
sys.stdout.write('\n')
def train(alpha=5e-5):
print("loading %s..." % formatted_dataset_path)
with open(formatted_dataset_path, 'rb') as f:
import sys
if sys.version_info.major == 3:
save = pickle.load(f, encoding='latin1')
else:
save = pickle.load(f)
train_dataset = save['train_dataset']
train_labels = save['train_labels']
test_dataset = save['test_dataset']
test_labels = save['test_labels']
label_map = save['label_map']
num_labels = len(label_map)
print("train_dataset:", train_dataset.shape)
print("train_labels:", train_labels.shape)
print("test_dataset:", test_dataset.shape)
print("test_labels:", test_labels.shape)
print("num_labels:", num_labels)
model = load_model_nn(alpha)
x = model['x']
y = model['y']
loss = model['loss']
optimizer = model['optimizer']
accuracy = model['accuracy']
keep_prob = model['keep_prob']
saver = model['saver']
graph = model['graph']
save_dir = os.path.join(trainer_dir, '.checkpoint')
print("Model saved path: ", save_dir)
batch_size = 64
def save_model(_step):
saver.save(session,
os.path.join(save_dir, 'weibo.cn-model.ckpt'),
global_step=_step)
with tf.Session(graph=graph) as session:
tf.summary.scalar('loss', loss)
tf.summary.scalar('accuracy', accuracy)
merged = tf.summary.merge_all()
writer = tf.summary.FileWriter(graph_log_dir, session.graph)
tf.global_variables_initializer().run()
step = 0
try:
model_ckpt_path, global_step = find_model_ckpt(
) # try to continue ....
except FileNotFoundError:
print("Initialized")
else: # try continue to train
saver.restore(session, model_ckpt_path)
step = global_step
print('found %s, step from %d' % (model_ckpt_path, step))
origin_step = step
while True:
offset = (step * batch_size) % (train_labels.shape[0] - batch_size)
# Generate a minibatch.
batch_data = train_dataset[offset:(offset + batch_size), :]
batch_labels = train_labels[offset:(offset + batch_size), :]
# print(batch_data, batch_labels)
session.run([optimizer, loss],
feed_dict={
x: batch_data,
y: batch_labels,
keep_prob: 0.5
})
step += 1
if step % 50 == 0:
train_accuracy = session.run(accuracy,
feed_dict={
x: batch_data,
y: batch_labels,
keep_prob: 1.0
})
test_accuracy = session.run(accuracy,
feed_dict={
x: test_dataset,
y: test_labels,
keep_prob: 1.0
})
print(("Step %5d, Training accuracy: %4f, Test accuracy: %4f" %
(step, train_accuracy, test_accuracy)))
if step % 100 == 0: # save the model every 100 step
save_model(step)
if test_accuracy > 0.999 or step - origin_step > 2000:
print('you can re-format dataset and give a smaller alpha '
'to continue training')
save_model(step)
break
print("Test accuracy: %g" % session.run(accuracy,
feed_dict={
x: test_dataset,
y: test_labels,
keep_prob: 1.0
}))
def train(alpha=5e-5):
print("loading %s..." % formatted_dataset_path)
with open(formatted_dataset_path, 'rb') as f:
import sys
if sys.version_info.major == 3:
save = pickle.load(f, encoding='latin1')
else:
save = pickle.load(f)
train_dataset = save['train_dataset']
train_labels = save['train_labels']
test_dataset = save['test_dataset']
test_labels = save['test_labels']
label_map = save['label_map']
num_labels = len(label_map)
print("train_dataset:", train_dataset.shape)
print("train_labels:", train_labels.shape)
print("test_dataset:", test_dataset.shape)
print("test_labels:", test_labels.shape)
print("num_labels:", num_labels)
model = load_model_nn(alpha)
x = model['x']
y = model['y']
loss = model['loss']
optimizer = model['optimizer']
accuracy = model['accuracy']
keep_prob = model['keep_prob']
saver = model['saver']
graph = model['graph']
save_dir = os.path.join(trainer_dir, '.checkpoint')
print("Model saved path: ", save_dir)
batch_size = 64
def save_model(_step):
saver.save(
session,
os.path.join(save_dir, 'weibo.cn-model.ckpt'),
global_step=_step
)
with tf.Session(graph=graph) as session:
tf.summary.scalar('loss', loss)
tf.summary.scalar('accuracy', accuracy)
merged = tf.summary.merge_all()
writer = tf.summary.FileWriter(graph_log_dir, session.graph)
tf.global_variables_initializer().run()
step = 0
try:
model_ckpt_path, global_step = find_model_ckpt() # try to continue ....
except FileNotFoundError:
print("Initialized")
else: # try continue to train
saver.restore(session, model_ckpt_path)
step = global_step
print('found %s, step from %d' % (model_ckpt_path, step))
origin_step = step
while True:
offset = (step * batch_size) % (train_labels.shape[0] - batch_size)
# Generate a minibatch.
batch_data = train_dataset[offset:(offset + batch_size), :]
batch_labels = train_labels[offset:(offset + batch_size), :]
# print(batch_data, batch_labels)
session.run(
[optimizer, loss],
feed_dict={
x: batch_data,
y: batch_labels,
keep_prob: 0.5
}
)
step += 1
if step % 50 == 0:
train_accuracy = session.run(
accuracy,
feed_dict={
x: batch_data,
y: batch_labels,
keep_prob: 1.0
}
)
test_accuracy = session.run(
accuracy,
feed_dict={
x: test_dataset,
y: test_labels,
keep_prob: 1.0
}
)
print(("Step %5d, Training accuracy: %4f, Test accuracy: %4f" %
(step, train_accuracy, test_accuracy)))
if step % 100 == 0: # save the model every 100 step
save_model(step)
if test_accuracy > 0.999 or step-origin_step > 2000:
print('you can re-format dataset and give a smaller alpha '
'to continue training')
save_model(step)
break
print("Test accuracy: %g" %
session.run(
accuracy,
feed_dict={
x: test_dataset,
y: test_labels,
keep_prob: 1.0
})
)