def train_discriminator(dis, gen, criterion, optimizer, epochs,
dis_adversarial_train_loss, dis_adversarial_train_acc,
args):
"""
Train discriminator
"""
generate_samples(gen, args.batch_size, args.n_samples, NEGATIVE_FILE)
data_iter = DisDataIter(POSITIVE_FILE, NEGATIVE_FILE, args.batch_size)
for epoch in range(epochs):
correct = 0
total_loss = 0.
for data, target in data_iter:
if args.cuda:
data, target = data.cuda(), target.cuda()
target = target.contiguous().view(-1)
output = dis(data)
pred = output.data.max(1)[1]
correct += pred.eq(target.data).cpu().sum()
loss = criterion(output, target)
total_loss += loss.item()
optimizer.zero_grad()
loss.backward()
optimizer.step()
data_iter.reset()
avg_loss = total_loss / len(data_iter)
acc = correct.item() / data_iter.data_num
print("Epoch {}, train loss: {:.5f}, train acc: {:.3f}".format(
epoch, avg_loss, acc))
dis_adversarial_train_loss.append(avg_loss)
dis_adversarial_train_acc.append(acc)
total_words = 0.
n = 0
for (data, target) in dis_data_iter:
n+=1
data = Variable(data)
target = Variable(target)
if opt.cuda:
data, target = data.cuda(), target.cuda()
target = target.contiguous().view(-1)
pred = discriminator.forward(data)
loss = dis_criterion(pred, target) # negative log likelihood loss
total_loss += loss.item()
total_words += data.size(0) * data.size(1)
dis_optimizer.zero_grad()
loss.backward()
dis_optimizer.step()
dis_data_iter.reset()
f_loss = math.exp(total_loss/ total_words)
d_save_path = os.path.join(m_save_path, c_cat)
if not os.path.exists(d_save_path):
os.mkdir(d_save_path)
d_save_path = os.path.join(d_save_path, 'discriminator'+str(total_batch)+'.pkl')
# torch.save(discriminator.state_dict(), d_save_path)
print('total_d_loss ', total_loss)
print('f_d_loss ',f_loss)
