def main():
# parse command line options from neural.py
args = neural.user_options()
# import training data and labels
train_set, train_labels = neural.read_data(path="digits_train_scaled.csv",
rows=args.num_train)
print("Successfully loaded training data with %i training images." %
train_set.shape[0])
# initialize neural network object with specified command line arguments
neuralNet = neural.neural(num_features = train_set.shape[1], num_hidden = 100, num_classes = 10, \
learn_rate = 0.001, reg_strength = 0.1, drop_prop = 0.1)
# run k-fold cross-validation
accuracies = cross_validate(num_iters = args.num_iters, train_data = train_set, batch_size = args.batch_size, \
train_labels = train_labels, neuralNet = neuralNet, crossEnt = args.cross_entropy, L2Reg = args.L2_reg,
dropout = args.dropout, XavierHe = args.XavierHe_init, K = args.K)
print("Average %i-fold cross-validation training accuracy: %.4f" %
(args.K, np.mean(accuracies)))
# import test data and labels
test_set, test_labels = neural.read_data(path="digits_test_scaled.csv",
rows=args.num_test)
# compute accuracy on test data
test_accuracy = neuralNet.compute_accuracy(data=test_set.transpose(),
labels=test_labels)
print("Accuracy on %i test images: %.4f" %
(test_set.shape[0], test_accuracy))
def nocr(filename):
print("nocr:" + filename)
# bounding box, find and log a box around the characters of interest
bounded_image = bounding_box(filename)
# separate characters, placing each in the log_images directory
(individual_images,
space_locations) = separate_chars(filename, bounded_image)
# return predicted string
nospace_string = neural(individual_images)
print(nospace_string)
final_string = ''
offset = 0
for index in range(0, len(nospace_string)):
if (index + offset) in space_locations:
final_string = final_string + ' '
offset = offset + 1
final_string = final_string + nospace_string[index]
return final_string
import numpy as np import neural as nn #Sample with AND logic gate: s = [2, 3, 1] in_set = np.array([[0., 0.], [0., 1.], [1., 0.], [1., 1.]]) out_set = np.array([[0.], [0.], [0.], [1.]]) n = nn.neural(s) n.train(in_set, out_set, 3000) n.forward(in_set) print n.o
import neural
j = 0
k = 10
errors = 1
b = neural.neural()
c = b.point(-2, 2)
g = b.etalon(c)
print(g)
while errors != 0:
errors = 0
while j < 10 and k < 20:
v = b.window(c, j, k)
m = b.check_error(v, g[k])
if m:
errors += 1
b.hoff(c, m, j, k)
j += 1
k += 1
print b.get_w()
def reply1(message):
Brand = db.getBrand(message.text)
if Brand is not None:
sent = bot.send_message(message.chat.id, "Окей, у вас %s" % Brand)
db.updateBrand(message.chat.id, Brand)
bot.register_next_step_handler(sent, reply2)
else:
sent = bot.send_message("Не удалось распознать марку автомобиля")
bot.register_next_step_handler(sent, reply1)
def reply2(message):
check = neur.check_cat(message.text)
sent = bot.send_message(message.chat.id,
'{name}. Заканчивай.'.format(name=message.text))
bot.register_next_step_handler(sent, start)
if __name__ == '__main__':
createdb.createtables()
db = DB.DBLayer(config.database)
neur = neural.neural()
neur.data_init()
while True:
try:
bot.polling(none_stop=True, interval=5)
except Exception as e:
print('Error occurred:')
print(sys.stderr, str(e))
print "file read error"
raise
def fill_data(self):
for line in self.ftrain:
intval = map(int,line.strip().split(" "))
self.train_data.append(intval)
#print intval
for line in self.ftrail:
intval = map(int,line.strip().split(" "))
self.test_data.append(intval)
for line in self.forgtrain:
intval = map(int,line.strip().split(" "))
self.train_org.append(intval)
print len(self.train_org)
for line in self.forgtrail:
intval = map(int,line.strip().split(" "))
self.test_org.append(intval)
#print intval
print len(self.test_org)
if __name__ == "__main__":
data = Data()
data.fill_data()
#nn = neural.neural(len(data.train_data[0])-1,10,0,data.train_data,data.test_data,11,1000)
#nn.train()
nn = neural.neural(len(data.train_org[0])-1,10,64,data.train_org,data.test_org,21,100)
nn.train()
