Pytorch（三）

版权声明：本文为博主原创文章，遵循 CC 4.0 BY-SA 版权协议，转载请附上原文出处链接和本声明。

本文链接： https://blog.csdn.net/qq_43475173/article/details/100937376

计算准确率

model = Classifier()
criterion = nn.NLLLoss()
optimizer = optim.Adam(model.parameters(), lr=0.003)

epochs = 5
steps = 0

train_losses, test_losses = [], []
for e in range(epochs):
    running_loss = 0
    for images, labels in trainloader:
        
        optimizer.zero_grad()
        
        log_ps = model(images)
        loss = criterion(log_ps, labels)
        loss.backward()
        optimizer.step()
        
        running_loss += loss.item()
        
    else:
        test_loss = 0
        accuracy = 0
        
        # Turn off gradients for validation, saves memory and computations
        with torch.no_grad():
            for images, labels in testloader:
                log_ps = model(images)
                test_loss += criterion(log_ps, labels)
                
                ps = torch.exp(log_ps)
                top_p, top_class = ps.topk(1, dim=1)
                equals = top_class == labels.view(*top_class.shape)
                accuracy += torch.mean(equals.type(torch.FloatTensor))
                
        train_losses.append(running_loss/len(trainloader))
        test_losses.append(test_loss/len(testloader))

        print("Epoch: {}/{}.. ".format(e+1, epochs),
              "Training Loss: {:.3f}.. ".format(running_loss/len(trainloader)),
              "Test Loss: {:.3f}.. ".format(test_loss/len(testloader)),
              "Test Accuracy: {:.3f}".format(accuracy/len(testloader)))

丢弃法（DropOut）

丢弃法的两处改动：


未使用丢弃法前：

使用丢弃法后：

效果很明显是不是？

保存，读取模型

网络完全一样：

torch.save(model.state_dict(), 'checkpoint.pth')
state_dict = torch.load('checkpoint.pth')
model.load_state_dict(state_dict)

网络不一样：