def control(input_msg):
tagger = Komoran()
dataset = Dataset('nsmc/ratings.txt',
tagger,
max_length=MAX_LENGTH,
batch_size=BATCH_SIZE)
Z_DIM = 40
H_DIM = 300
C_DIM = 2
model = RNN_VAE(dataset.num_words,
H_DIM,
Z_DIM,
C_DIM,
freeze_embeddings=False,
gpu=USE_CUDA,
gpu_id=GPU_ID)
test_data = torch.LongTensor(
dataset.sentence2idxs(tagger.morphs(input_msg))).unsqueeze(1)
model.load_state_dict(torch.load('models/vae_epoch_300_400.bin'))
results = model.controlSentence(test_data, t=0.5)
return (dataset.idxs2sentence(results[0], no_pad=True),
dataset.idxs2sentence(results[1], no_pad=True))
#
# 또한 표준 입력을 받아서 컨트롤된 문장의 결과를 출력합니다.
model = RNN_VAE(dataset.num_words,
H_DIM,
Z_DIM,
C_DIM,
freeze_embeddings=False,
gpu=USE_CUDA,
gpu_id=GPU_ID)
test_set = dataset.getTestData(100)
model.load_state_dict(torch.load('models/vae_epoch_300_400.bin'))
for test in test_set:
results = model.controlSentence(test[0].unsqueeze(1), t=0.5)
print('Original : ', dataset.idxs2sentence(test[0], no_pad=True))
print('Positive : ', dataset.idxs2sentence(results[0], no_pad=True))
print('Negative : ', dataset.idxs2sentence(results[1], no_pad=True))
print()
tagger = Komoran()
while True:
sentence = tagger.morphs(input())
if len(sentence) == 0:
break
sentence = dataset.sentence2idxs(sentence).unsqueeze(dim=1)
