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-----------')
print(iteration)
print('--------CROSS-ENTROPY---------')
print(cross_entropy.data.cpu().numpy())
if args.use_trained:
rvae.load_state_dict(
t.load('saved_models/trained_RVAE_' + args.model_name))
ce_result = list(
np.load('saved_models/ce_result_{}.npy'.format(args.model_name)))
kld_result = list(
np.load('saved_models/kld_result_npy_{}.npy'.format(
args.model_name)))
if args.use_cuda:
rvae = rvae.cuda()
optimizer = Adam(rvae.learnable_parameters(), args.learning_rate)
train_step = rvae.trainer(optimizer, batch_loader)
validate, validation_sample = rvae.validater(batch_loader)
for iteration in range(args.num_iterations):
cross_entropy, kld, coef = train_step(iteration, args.batch_size,
args.use_cuda, args.dropout)
if iteration % 100 == 0:
print('\n')
print('------------TRAIN-------------')
print('----------ITERATION-----------')
print(iteration)
print('--------CROSS-ENTROPY---------')
print(cross_entropy.data.cpu().numpy()[0])
print('-------------KLD--------------')
print(kld.data.cpu().numpy()[0])
