class CWS:
def __init__(self, s):
self.rnn = RNN(s['ne'], s['de'], s['win'], s['nh'], s['nc'], 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
be = 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()
for i in xrange(n_train):
cwords = contextwin(train_lex[i], s['win'])
words = map(lambda x: np.asarray(x).astype('int32'), minibatch(cwords, s['bs']))
labels = train_label[i]
for word_batch, label_last_word in zip(words, labels):
self.rnn.fit(word_batch, label_last_word, s['clr'])
self.rnn.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()
pred_y = self.predict(valid_lex)
p, r, f = evaluate(pred_y, valid_label)
print '[learning] epoch %i >> P: %2.2f%% R: %2.2f%% F: %2.2f%%' % (e+1, p*100., r*100., f*100.), '<< %s used' % time_format(time.time() - tic)
if f > best_f:
best_f = f
be = e
self.save()
if s['decay'] and e - be >= 5: s['clr'] *= 0.5
if s['clr'] < 1e-5: break
def predict(self, lex):
s = self.s
y = [self.rnn.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.rnn.save()
def load(self):
self.rnn.load()
cs :: word window context size
'''
rnn = RNN(nh=s['nhidden'],
nc=len(languages()),
ne=s['vocab_size'],
de=s['emb_dimension'],
cs=s['win'])
best_f1 = -numpy.inf
s['clr'] = s['lr']
for e in range(s['nepochs']):
# shuffle
shuffle([train_lex, train_ne, train_y], s['seed'])
s['ce'] = e
tic = time.time()
for i in xrange(nsentences):
cwords = contextwin(train_lex[i], s['win'])
words = map(lambda x: numpy.asarray(x).astype('int32'),\
minibatch(cwords, s['bs']))
labels = train_y[i]
for word_batch, label_last_word in zip(words, labels):
rnn.train(word_batch, label_last_word, s['clr'])
rnn.normalize()
if s['verbose']:
print(
'[learning] epoch %i >> %2.2f%%' %
(e, (i + 1) * 100. / nsentences),
'completed in %.2f (sec) <<\r' % (time.time() - tic))
sys.stdout.flush()
#cross_validation(all_train_x, all_train_y, test_x, test_y)
nc :: number of classes
ne :: number of word embeddings in the vocabulary
de :: dimension of the word embeddings
cs :: word window context size
'''
rnn = RNN( nh = s['nhidden'],
nc = len(languages()),
ne = s['vocab_size'],
de = s['emb_dimension'],
cs = s['win'])
best_f1 = -numpy.inf
s['clr'] = s['lr']
for e in range(s['nepochs']):
# shuffle
shuffle([train_lex, train_ne, train_y], s['seed'])
s['ce'] = e
tic = time.time()
for i in xrange(nsentences):
cwords = contextwin(train_lex[i], s['win'])
words = map(lambda x: numpy.asarray(x).astype('int32'),\
minibatch(cwords, s['bs']))
labels = train_y[i]
for word_batch , label_last_word in zip(words, labels):
rnn.train(word_batch, label_last_word, s['clr'])
rnn.normalize()
if s['verbose']:
print('[learning] epoch %i >> %2.2f%%'%(e,(i+1)*100./nsentences),'completed in %.2f (sec) <<\r'%(time.time()-tic))
sys.stdout.flush()
#cross_validation(all_train_x, all_train_y, test_x, test_y)