Question

我正在Pytorch中使用CNN学习图像分类。在我开始构建CNN体系结构时，我遇到了2个问题：

运行代码时，我得到的警告是-warnings.warn("train_labels has been renamed targets")
虽然我尝试使用print函数打印损失和准确性，但没有打印任何内容。

为了您的更好理解，我在这里发布了我的代码。先谢谢了。感谢您在这些问题上提供的帮助和提示。

 def forward(self, x):
     out = self.layer1(x)
     out = self.layer2(out)
     out = out.reshape(out.size(0), -1)
     out = self.drop_out(out)
     out = self.fc1(out)
     out = self.fc2(out)
     return out
     from torch import optim
     model = ConvNet()
     criterion = nn.CrossEntropyLoss()
     optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
     total_step = len(train_loader)
     loss_list = []
     acc_list = []
     for epoch in range(num_epochs):
        for i, (images, labels) in enumerate(train_loader):

          outputs = model(images)
          loss = criterion(outputs, labels)
          loss_list.append(loss.item())
          optimizer.zero_grad()
          loss.backward()
          optimizer.step()

          total = labels.size(0)
          _, predicted = torch.max(outputs.data, 1)
          correct = (predicted == labels).sum().item()
          acc_list.append(correct / total)
          if (i + 1) % 100 == 0:
           print("Epoch [{}\{}], Step [{}\{}], Loss: {:.4f}, Accuracy: {:.2f}%"
                    .format(epoch + 1, num_epochs, i + 1, total_step, loss.item(),
                            correct / total) * 100)

        # Test the model
          model.eval()
          with torch.no_grad():
            correct = 0
            total = 0
            for images, labels in test_loader:
                outputs = model(images)
                _, predicted = torch.max(outputs.data, 1)
                total += labels.size(0)
                correct += (predicted == labels).sum().item()

            print("Test Accuracy of the model on the 10000 test images: {} %".format((correct / total) * 100))

在pytorch中构建CNN架构时，打印功能不会打印任何内容

0 个答案: