batch_realdata = batch_realdata.view(opt.Seq_Len, opt.BATCH_SIZE, 1)
batch_realdata = Variable(batch_realdata)
# 判别器判别生成数据,真实数据
prob_fake = D(batch_gdata)
prob_real = D(batch_realdata)
d_loss = -torch.mean(torch.log(prob_real) + torch.log(1 - prob_fake))
g_loss = -torch.mean(torch.log(prob_fake))
opt_D.zero_grad()
d_loss.backward(retain_graph=True)
opt_D.step()
opt_G.zero_grad()
g_loss.backward(retain_graph=True)
opt_G.step()
print 'Epoch:{} / BATCH: {} ,d_loss:{},g_loss:{}'.format(
epoch, batch, d_loss.data.cpu().numpy(), g_loss.data.cpu().numpy())
dloss_list.append(d_loss.data.cpu().numpy())
gloss_list.append(g_loss.data.cpu().numpy())
dloss_avg = listsumavg(dloss_list)
gloss_avg = listsumavg(gloss_list)
outputWriter.writerow([epoch, dloss_avg, gloss_avg])
outputFile.close()
torch.save(G.state_dict(), 'modelsaved/generatormlp.pkl')
NUM = int(listlength / opt.Seq_Len / opt.BATCH_SIZE)
Filename = 'resultdata/rnn_auto.csv'
outputFile = open(Filename, 'w')
outputWriter = csv.writer(outputFile)
outputWriter.writerow(['Epoch', 'Loss'])
for epoch in range(500):
loss_list = []
for batch in range(NUM):
inputs = torch.randn(opt.BATCH_SIZE, opt.Seq_Len, 1)
for i in range(opt.BATCH_SIZE):
for j in range(opt.Seq_Len):
inputs[i][j][0] = pointlistx[batch * opt.Seq_Len *
opt.BATCH_SIZE + i * opt.Seq_Len +
j]
inputs = Variable(inputs.view(opt.Seq_Len, opt.BATCH_SIZE, 1).cuda())
output = net(inputs)
criterion.cuda()
loss = criterion(output, inputs)
print 'epoch:{}, batch:{}, loss:{}'.format(epoch, batch, loss)
loss_list.append(loss.data[0].item())
# 反向传播
optimizer.zero_grad()
loss.backward()
optimizer.step()
lossavg = listsumavg(loss_list)
outputWriter.writerow([epoch, lossavg])
outputFile.close()
torch.save(net.state_dict(), 'modelsaved/rnn_auto.pkl')
