PyTorch 卷积神经网络介绍

卷积神经网络是从零开始构建CNN模型的一个过程。网络架构将包含以下步骤的组合：

Conv2d
MaxPool2d
整流线性单元（Rectified Linear Unit）
View
线性层（Linear Layer）

训练模型

训练模型的过程与图像分类问题相同。下面的代码片段完成了在提供的数据集上训练模型的过程：

def fit(epoch,model,data_loader,phase 
= 'training',volatile = False):
   if phase == 'training':
      model.train()
   if phase == 'training':
      model.train()
   if phase == 'validation':
      model.eval()
   volatile=True
   running_loss = 0.0
   running_correct = 0
   for batch_idx , (data,target) in enumerate(data_loader):
      if is_cuda:
         data,target = data.cuda(),target.cuda()
         data , target = Variable(data,volatile),Variable(target)
      if phase == 'training':
         optimizer.zero_grad()
         output = model(data)
         loss = F.nll_loss(output,target)
         running_loss + = 
         F.nll_loss(output,target,size_average = 
         False).data[0]
         preds = output.data.max(dim = 1,keepdim = True)[1]
         running_correct + = 
         preds.eq(target.data.view_as(preds)).cpu().sum()
         if phase == 'training':
            loss.backward()
            optimizer.step()
   loss = running_loss/len(data_loader.dataset)
   accuracy = 100. * running_correct/len(data_loader.dataset)
   print(f'{phase} loss is {loss:{5}.{2}} and {phase} accuracy is {running_correct}/{len(data_loader.dataset)}{accuracy:{return loss,accuracy}})

该方法包括不同的逻辑用于训练和验证。使用不同的模式有两个主要原因−