def train(self,
t_x,
t_y,
v_x,
v_y,
lrv,
char2idx,
sess,
epochs,
batch_size=10,
reset=True):
idx2char = {k: v for v, k in char2idx.items()}
v_y_g = [np.trim_zeros(v_y_t) for v_y_t in v_y]
gold_out = [
toolbox.generate_trans_out(v_y_t, idx2char) for v_y_t in v_y_g
]
best_score = 0
if reset or not os.path.isfile(self.trained + '_weights.index'):
for epoch in range(epochs):
Batch.train_seq2seq(sess,
model=self.en_vec + self.trans_labels,
decoding=self.feed_previous,
batch_size=batch_size,
config=self.trans_train,
lr=self.trans_l_rate,
lrv=lrv,
data=[t_x] + [t_y])
pred = Batch.predict_seq2seq(sess,
model=self.en_vec + self.de_vec +
self.trans_output,
decoding=self.feed_previous,
decode_len=self.decode_step,
data=[v_x],
argmax=True,
batch_size=100)
pred_out = [
toolbox.generate_trans_out(pre_t, idx2char)
for pre_t in pred
]
c_scores = evaluation.trans_evaluator(gold_out, pred_out)
print('epoch: %d' % (epoch + 1))
print('ACC: %f' % c_scores[0])
print('Token F score: %f' % c_scores[1])
sys.stdout.flush()
if c_scores[1] > best_score:
best_score = c_scores[1]
self.saver.save(sess,
self.trained + '_weights',
write_meta_graph=False)
if best_score > 0 or not reset:
self.saver.restore(sess, self.trained + '_weights')
def tag(self, t_x, char2idx, sess, batch_size=100):
t_x = [t_x_t[:self.encode_step] for t_x_t in t_x]
t_x = toolbox.pad_zeros(t_x, self.encode_step)
idx2char = {k: v for v, k in char2idx.items()}
pred = Batch.predict_seq2seq(sess,
model=self.en_vec + self.de_vec +
self.trans_output,
decoding=self.feed_previous,
decode_len=self.decode_step,
data=[t_x],
argmax=True,
batch_size=batch_size)
pred_out = [
toolbox.generate_trans_out(pre_t, idx2char) for pre_t in pred
]
return pred_out