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()
from rnn import RNN
from tokens import tokenize
f = open('input.txt', 'r')
sentences = list(filter(None, f.read().split('\n\n')))
f.close()
# print((sentences))
tokens = []
X = []
Y = []
# word_dim = 58
for i in range(len(sentences)):
tokens.append([])
for j in range(len(sentences[i])):
if (tokenize(sentences[i][j]) != "UNKNOWN"):
tokens[i].append(tokenize(sentences[i][j]))
X.append(tokens[i][:-1])
Y.append(tokens[i][1:])
model = RNN(61, 200, 10)
model.load('uwv.pkl')
model.train(X, Y, 0.1, 10, 1)
labels=None,
output_shape=2)
#generator = BatchGenerator(sonar_text, batch_size, num_unrollings, labels=sonar_labels, output_shape=2)
##########################################
# Create RNN
##########################################
summary_frequency = 10
num_nodes = 128
num_layers = 1
model_path = './model/checkpoint.ckpt'
RNN = RNN(summary_frequency,
num_nodes,
num_layers,
generator,
labeled=False,
output_shape=2)
RNN.train(100)
RNN.plot()
RNN.save(model_path, full_model=False)
RNN.load(model_path, full_model=False)
#RNN.sample_sentence('japan is al geruime tijd een natie van de erkende naties inderdaad d', 100)
#RNN.sample_sentence(' ' * (num_unrollings+1), 1000)
#x,y=generator._next()
#pred = RNN.predict(x)
