Pytorch 简明教程

PyTorch - Introduction to Convents

卷积网络主要关于从头开始构建 CNN 模型。网络架构将包含以下步骤的组合 -

  1. Conv2d

  2. MaxPool2d

  3. Rectified Linear Unit

  4. View

  5. Linear Layer

Training the Model

训练模型的过程与图像分类问题相同。以下代码片段完成了在给定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}})

该方法包括用于训练和验证的不同逻辑。使用不同模式的主要原因有两个 -

  1. 在训练模式下，丢弃会删除一定百分比的值，而这在验证或测试阶段不应该发生。

  2. 对于训练模式，我们要计算梯度并更改模型参数的值，但在测试或验证阶段不需要反向传播。