args = parser.parse_args()
batch_loader = BatchLoader('')
parameters = Parameters(batch_loader.max_word_len,
batch_loader.max_seq_len,
batch_loader.words_vocab_size,
batch_loader.chars_vocab_size)
rvae = RVAE(parameters)
if args.use_trained:
rvae.load_state_dict(t.load('trained_RVAE'))
if args.use_cuda:
rvae = rvae.cuda()
optimizer = Adam(rvae.learnable_parameters(), args.learning_rate)
train_step = rvae.trainer(optimizer, batch_loader)
validate = rvae.validater(batch_loader)
ce_result = []
kld_result = []
for iteration in range(args.num_iterations):
cross_entropy, kld, coef = train_step(iteration, args.batch_size, args.use_cuda, args.dropout)
if iteration % 5 == 0:
print('\n')
print('------------TRAIN-------------')
print('----------ITERATION-----------')
data = 0
with open('train.txt', 'r') as f:
data = f.readlines()
preprocess = Preprocess(embedding_model)
input = preprocess.to_sequence(data)
# embedding=preprocess.embedding()
# np.save('embedding',embedding)
batch_loader = Batch(input, 0.7)
params=Parameter(word_embed_size=300,encode_rnn_size=600,latent_variable_size=1400,\
decode_rnn_size=600,vocab_size=preprocess.vocab_size,embedding_path='embedding.npy')
model = RVAE(params)
model = model.cuda()
optimizer = Adam(model.learnable_parameters(), 1e-3)
train_step = model.trainer(optimizer)
use_cuda = t.cuda.is_available()
ce_list = []
kld_list = []
coef_list = []
test_batch = batch_loader.test_next_batch(1)
for i, batch in enumerate(batch_loader.train_next_batch(1)):
# if i%20==0:
# sample=next(test_batch)
# sentence=model.sample(10,sample,use_cuda)
# sentence=[preprocess.index_to_word(i) for i in sentence]
# print(' '.join(sentence))
# break