type=bool,
default=True,
metavar='CUDA',
help='use cuda (default: True)')
parser.add_argument('--num-sample',
type=int,
default=100,
metavar='NS',
help='num samplings (default: 10)')
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)
rvae.load_state_dict(t.load('trained_RVAE_code'))
if args.use_cuda:
rvae = rvae.cuda()
with open("code_sampling_100.txt", 'w') as cs:
for iteration in range(args.num_sample):
seed = np.random.normal(size=[1, parameters.latent_variable_size])
result = rvae.sample(batch_loader, 50, seed, args.use_cuda)
# print(result)
# print()
cs.write(result + '\n')
#load data
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))