def train_torch_lstm(conf, args=None):
pdata = PoemData()
pdata.read_data(conf)
pdata.get_vocab()
if conf.use_gpu:
device = torch.device('cuda')
else:
device = torch.device('cpu')
model = PoetryModel(pdata.vocab_size, conf, device)
train_data = pdata.train_data
test_data = pdata.test_data
train_data = torch.from_numpy(np.array(train_data['pad_words']))
dev_data = torch.from_numpy(np.array(test_data['pad_words']))
dataloader = DataLoader(train_data,
batch_size=conf.batch_size,
shuffle=True,
num_workers=conf.num_workers)
devloader = DataLoader(dev_data,
batch_size=conf.batch_size,
shuffle=True,
num_workers=conf.num_workers)
optimizer = Adam(model.parameters(), lr=conf.learning_rate)
criterion = nn.CrossEntropyLoss()
loss_meter = meter.AverageValueMeter()
if conf.load_best_model:
model.load_state_dict(torch.load(conf.beat_model_path))
if conf.use_gpu:
model.cuda()
criterion.cuda()
step = 0
bestppl = 1e9
early_stop_controller = 0
for epoch in range(conf.n_epochs):
losses = []
loss_meter.reset()
model.train()
for i, data in enumerate(dataloader):
data = data.long().transpose(1, 0).contiguous()
if conf.use_gpu:
data = data.cuda()
input, target = data[:-1, :], data[1:, :]
optimizer.zero_grad()
output, _ = model(input)
loss = criterion(output, target.contiguous().view(-1))
loss.backward()
optimizer.step()
losses.append(loss.item())
loss_meter.add(loss.item())
step += 1
if step % 100 == 0:
print("epoch_%d_step_%d_loss:%0.4f" %
(epoch + 1, step, loss.item()))
train_loss = float(loss_meter.value()[0])
model.eval()
for i, data in enumerate(devloader):
data = data.long().transpose(1, 0).contiguous()
if conf.use_gpu:
data = data.cuda()
input, target = data[:-1, :], data[1:, :]
output, _ = model(input)
loss = criterion(output, target.view(-1))
loss_meter.add(loss.item())
ppl = math.exp(loss_meter.value()[0])
print("epoch_%d_loss:%0.4f , ppl:%0.4f" % (epoch + 1, train_loss, ppl))
if epoch % conf.save_every == 0:
torch.save(model.state_dict(),
"{0}_{1}".format(conf.model_prefix, epoch))
fout = open("{0}out_{1}".format(conf.out_path, epoch),
'w',
encoding='utf-8')
for word in list('日红山夜湖海月'):
gen_poetry = generate_poet(model, word, pdata.vocab, conf)
fout.write("".join(gen_poetry) + '\n\n')
fout.close()
if ppl < bestppl:
bestppl = ppl
early_stop_controller = 0
torch.save(model.state_dict(),
"{0}_{1}".format(conf.best_model_path, "best_model"))
else:
early_stop_controller += 1
if early_stop_controller > conf.patience:
print("early stop.")
break
loss = 0
counts = 0
for case in range(v * batch, min((v + 1) * batch, TRAINSIZE)):
s = data[case]
hidden = model.initHidden()
t, o = makeForOneCase(s, one_hot_var_target)
output, hidden = model(t.cuda(), hidden)
loss += criterion(output, o.cuda())
counts += 1
loss = loss / counts
print "=====", loss.data[0]
print "start training"
for epoch in range(epochNum):
model.train()
for batchIndex in range(int(TRAINSIZE / batch)):
model.zero_grad()
loss = 0
counts = 0
for case in range(batchIndex * batch,
min((batchIndex + 1) * batch, TRAINSIZE)):
s = data[case]
hidden = model.initHidden()
t, o = makeForOneCase(s, one_hot_var_target)
output, hidden = model(t.cuda(), hidden)
loss += criterion(output, o.cuda())
counts += 1
loss = loss / counts
loss.backward()
print epoch, loss.data[0]
