本文整理汇总了Python中mlp.MLP.load方法的典型用法代码示例。如果您正苦于以下问题：Python MLP.load方法的具体用法？Python MLP.load怎么用？Python MLP.load使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类mlp.MLP的用法示例。
在下文中一共展示了MLP.load方法的2个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: __init__
# 需要导入模块: from mlp import MLP [as 别名]
# 或者: from mlp.MLP import load [as 别名]
class CWS:
def __init__(self, s):
self.mlp = MLP(s['ne'], s['de'], s['win'], s['nh'], 4, s['L2_reg'], np.random.RandomState(s['seed']))
self.s = s
def fit(self, lex, label):
s = self.s
n_sentences = len(lex)
n_train = int(n_sentences * (1. - s['valid_size']))
s['clr'] = s['lr']
best_f = 0
for e in xrange(s['n_epochs']):
shuffle([lex, label], s['seed'])
train_lex, valid_lex = lex[:n_train], lex[n_train:]
train_label, valid_label = label[:n_train], label[n_train:]
tic = time.time()
cost = 0
for i in xrange(n_train):
if len(train_lex[i]) == 2: continue
words = np.asarray(contextwin(train_lex[i], s['win']), dtype = 'int32')
labels = [0] + train_label[i] + [0]
y_pred = self.mlp.predict(words)
cost += self.mlp.fit(words, [0]+y_pred, [0]+labels, s['clr'])
self.mlp.normalize()
if s['verbose']:
print '[learning] epoch %i >> %2.2f%%' % (e+1, (i+1)*100./n_train), 'completed in %s << \r' % time_format(time.time() - tic),
sys.stdout.flush()
print '[learning] epoch %i >> cost = %f' % (e+1, cost / n_train), ', %s used' % time_format(time.time() - tic)
pred_y = self.predict(valid_lex)
p, r, f = evaluate(pred_y, valid_label)
print ' P: %2.2f%% R: %2.2f%% F: %2.2f%%' % (p*100., r*100., f*100.)
'''
if f > best_f:
best_f = f
self.save()
'''
def predict(self, lex):
s = self.s
y = [self.mlp.predict(np.asarray(contextwin(x, s['win'])).astype('int32'))[1:-1] for x in lex]
return y
def save(self):
if not os.path.exists('params'): os.mkdir('params')
self.mlp.save()
def load(self):
self.mlp.load()示例2: convert
# 需要导入模块: from mlp import MLP [as 别名]
# 或者: from mlp.MLP import load [as 别名]
def convert(image_file, text_file=None):
img = ip.get_image(image_file)
lines = []
for line in ip.get_lines(img):
words = []
for word in ip.get_words(img, line):
chars = []
for char in ip.get_chars(img, word):
c = convert_char(img, char)
chars.append(c)
words.append(''.join(chars))
lines.append(' '.join(words))
if text_file:
f = open(text_file, 'w')
f.write('\n'.join(lines))
f.close()
else:
print '\n'.join(lines)
def convert_char(img, char):
c = ip.process_char(img, char)
return decode(network.activate(c))
network = MLP.load('lower2.dmp')
if __name__ == '__main__':
convert('./samples/otra_prueba.png')