def _classify(self, x_data, gen_limit=50, *args, **kwargs):
xp = self._xp
batch_size = len(x_data)
src_len = len(x_data[0])
EOL = self._model._trg_voc.eos_id()
self._model.reset_state(batch_size)
output = np.zeros((batch_size, gen_limit), dtype=np.float32)
# For each word
for j in range(min(gen_limit, src_len)):
words = Variable(xp.array([x_data[i][j] for i in range(batch_size)], dtype=np.int32))
collect_output(j, output, words.data)
y = self._model(words, is_train=False)
start_gen = j
for j in range(start_gen, gen_limit - src_len):
next_word = UF.argmax(y.data)
collect_output(j, output, next_word)
y = self._model(Variable(xp.array(next_word, dtype=np.int32)), is_train=False)
if all(word == EOL for word in next_word):
break
return output
def collect_output(src_col, output, out):
y = UF.argmax(out.data)
for i in range(len(y)):
output[i][src_col] = y[i]
