N_CLASS = 4 # B: 0, M: 1, E: 2, S: 3
N_EPOCH = 1
sample = '我来到大学读书，希望学到知识'
py = int(sys.version[0])
def to_seq(*args):
data = []
for x in args:
x = x[:(len(x) - len(x) % SEQ_LEN)]
data.append(np.reshape(x, [-1, SEQ_LEN]))
return data
if __name__ == '__main__':
x_train, y_train, x_test, y_test, vocab_size, char2idx, idx2char = chseg.load_data(
)
X_train, X_test, Y_train, Y_test = to_seq(x_train, x_test, y_train, y_test)
print('Vocab size: %d' % vocab_size)
clf = BiRNN_CRF(SEQ_LEN, vocab_size, N_CLASS)
clf.fit(X_train, Y_train, val_data=(X_test, Y_test), n_epoch=N_EPOCH)
chars = list(sample) if py == 3 else list(sample.decode('utf-8'))
labels = clf.infer([char2idx[c] for c in chars])
res = ''
for i, l in enumerate(labels):
c = sample[i] if py == 3 else sample.decode('utf-8')[i]
if l == 2 or l == 3:
c += ' '
res += c
print(res)
N_CLASS = 4 # B: 0, M: 1, E: 2, S: 3
N_EPOCH = 1
sample = '我来到大学读书，希望学到知识'
py = int(sys.version[0])
def to_seq(*args):
data = []
for x in args:
x = x[: (len(x) - len(x) % SEQ_LEN)]
data.append(np.reshape(x, [-1, SEQ_LEN]))
return data
if __name__ == '__main__':
x_train, y_train, x_test, y_test, vocab_size, char2idx, idx2char = chseg.load_data()
X_train, X_test, Y_train, Y_test = to_seq(x_train, x_test, y_train, y_test)
print('Vocab size: %d' % vocab_size)
clf = BiRNN(SEQ_LEN, vocab_size, N_CLASS, n_layer=2)
clf.fit(X_train, Y_train, val_data=(X_test, Y_test), n_epoch=N_EPOCH)
chars = list(sample) if py == 3 else list(sample.decode('utf-8'))
labels = clf.infer([char2idx[c] for c in chars])
res = ''
for i, l in enumerate(labels):
c = sample[i] if py == 3 else sample.decode('utf-8')[i]
if l == 2 or l == 3:
c += ' '
res += c
print(res)
def to_test_seq(*args):
data = []
for x in args:
x = x[:(len(x) - len(x) % SEQ_LEN)]
data.append(np.reshape(x, [-1, SEQ_LEN]))
return data
def iter_seq(x, text_iter_step=3):
return np.array(
[x[i:i + SEQ_LEN] for i in range(0,
len(x) - SEQ_LEN, text_iter_step)])
if __name__ == '__main__':
x_train, y_train, x_test, y_test, vocab_size, word2idx, idx2word = chseg.load_data(
)
X_train, Y_train = to_train_seq(x_train, y_train)
X_test, Y_test = to_test_seq(x_test, y_test)
print('Vocab size: %d' % vocab_size)
clf = Tagger(vocab_size, N_CLASS, SEQ_LEN, num_blocks=3)
clf.fit(X_train,
Y_train,
val_data=(X_test, Y_test),
n_epoch=N_EPOCH,
batch_size=BATCH_SIZE)
chars = list(sample) if py == 3 else list(sample.decode('utf-8'))
_test = [word2idx[w] for w in sample] + [0] * (SEQ_LEN - len(sample))
labels = clf.infer(_test, len(sample))
labels = labels[:len(sample)]