input_2 = [Variable(t.from_numpy(var)) for var in input_2]
input_2 = [var.long() for var in input_2]
input_2 = [var.cuda() if use_cuda else var for var in input_2]
[
encoder_word_input_2, encoder_character_input_2,
decoder_word_input_2, decoder_character_input_2, target
] = input_2
''' ================================================== Forward pass ===========================================================
'''
# exit()
logits, _, kld, mu, std = rvae.forward(0.,
encoder_word_input,
encoder_character_input,
encoder_word_input_2,
encoder_character_input_2,
decoder_word_input_2,
decoder_character_input_2,
z=None)
''' ================================================================================================================================
'''
# print '============'
print(data[i])
print(data_2[i])
# print '------------------------------------'
for iteration in range(args.num_sample):
# seed = np.random.normal(size=[1, parameters.latent_variable_size])
seed = Variable(t.randn([1, parameters.latent_variable_size]))
# seed = Variable(t.from_numpy(seed).float())
initial_state = None
sentence = " ".join(
list(
map(lambda x: batch_loader.idx_to_word[x],
original_encoder_word_input[0].tolist()))[::-1])
reference = " ".join(
list(
map(lambda x: batch_loader.idx_to_word[x],
paraphrse_encoder_word_input[0].tolist()))[::-1])
for i in range(seq_len):
logits, initial_state, _ = rvae.forward(
0.,
original_encoder_word_input[0:1],
original_encoder_character_input[0:1],
paraphrse_encoder_word_input[0:1],
paraphrse_encoder_character_input[0:1],
decoder_word_input[0:1],
decoder_character_input[0:1],
z=seed,
initial_state=initial_state)
logits = logits.view(-1, rvae.params.word_vocab_size)
prediction = F.softmax(logits)
word = batch_loader.sample_word_from_distribution(
prediction.data.cpu().numpy()[-1])
if word == batch_loader.end_token:
break
result += ' ' + word
