#训练过程函数

def fit(epoch,model,trainloader,testloader):
    correct = 0
    total = 0
    running_loss =0
    model.train()  #指明这是train模式需要bn和drop
    for x,y in trainloader:
        if torch.cuda.is_available():
            x,y =x.to('cuda'),y.to('cuda')
        y_pred =model(x)
        loss = loss_fn(y_pred,y)
        optim.zero_grad()
        loss.backward()
        optim.step()
        with torch.no_grad():
            y_pred = torch.argmax(y_pred,dim=1)
            correct +=(y_pred==y).sum().item()
            total += y.size(0)
            running_loss += loss.item()
    epoch_loss = running_loss/len(trainloader)
    epoch_acc =correct/total

    test_correct = 0
    test_total = 0
    test_running_loss =0

    model.eval()
    with torch.no_grad():
        for x,y in testloader:
            if torch.cuda.is_available():
                x,y = x.to('cuda'),y.to('cuda')
            y_pred =model(x)
            loss = loss_fn(y_pred,y)
            y_pred = torch.argmax(y_pred,dim=1)
            test_correct +=(y_pred==y).sum().item()
            test_total +=y.size(0)
            test_running_loss +=loss.item()
    epoch_tst_loss =test_running_loss/len(testloader)
    epoch_tst_acc = test_correct/test_total

    print('epoch',epoch,'loss',round(epoch_loss,3),
          'acc:',round(epoch_acc,3),
          'test_loss:',round(epoch_tst_loss,3),
          'test_acc:',round(epoch_tst_acc,3))
    return epoch_loss ,epoch_acc,epoch_tst_loss,epoch_tst_acc


#打印训练epoch

epochs =30
train_loss =[]
train_acc =[]
test_loss =[]
test_acc=[]

for epoch in range(epochs):
    epoch_loss,epoch_acc,epoch_tst_loss,epoch_tst_acc =fit(epoch,model,train_dl,test_dl)
    train_loss.append(epoch_loss)
    train_acc.append(epoch_acc)
    test_loss.append(epoch_tst_loss)
    test_acc.append(epoch_tst_acc)


#绘制图像：
plt.plot(range(1,epochs+1),train_loss,label='train_loss')
plt.plot(range(1,epochs+1),test_loss,label = 'test_loss' )
plt.legend()
plt.show()