本文整理匯總了Python中mlp.MLP.save方法的典型用法代碼示例。如果您正苦於以下問題：Python MLP.save方法的具體用法？Python MLP.save怎麽用？Python MLP.save使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類mlp.MLP的用法示例。
在下文中一共展示了MLP.save方法的1個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: __init__
# 需要導入模塊: from mlp import MLP [as 別名]
# 或者: from mlp.MLP import save [as 別名]
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()