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    多分类问题线性层和训练部分代码的构建

    语音识别与语义处理领域
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    • 155****7220
      155****7220 last edited by

      如下图网络是一个十个输出(十分类问题)

      首先建立三个线性层

      import torch
      import torch.nn.functional as F
      
      # 先建立三个线性层 784=>200=>200=>10
      
      w1, b1 = torch.randn(200, 784, requires_grad=True), \
               torch.randn(200, requires_grad=True)
      # randn内的参数分别为(ch_out, ch_cn),784=28*28,适用于常用的mnist数据集
      w2, b2 = torch.randn(200, 200, requires_grad=True), \
               torch.randn(200, requires_grad=True)
      w3, b3 = torch.randn(10, 200, requires_grad=True), \
               torch.randn(10, requires_grad=True)
      # 第二层虽然维度和第一层一样,都是200,但是并不是没有作用,而是经历了特征变换
      
      def forward(x):
          # 经过第一层
          x = x@w1.t() + b1
          x = F.relu(x)
          
          # 经过第二层
          x = x@w2.t() + b2
          x = F.relu(x)
          
          # 经过最后一层
          x = x@w3.t() + b3
          x = F.relu(x)
          
          return x # 这里返回的x没有经过sigmoid和softmax
      

      上面完成了tensor和forward的建立,下面介绍train的部分

      # 训练过程首先要建立一个优化器,引入相关工具包
      import torch.optim as optim
      import torch.nn as nn
      
      lr = 1e-3 # learning_rate
      
      # 优化器优化的目标是三个全连接层的变量
      optimizer = optim.SGD([w1, b1, w2, b2, w3, b3], lr=lr)
      criteon = nn.CrossEntropyLoss() # 自带softmax,log,CrossEntropy
      
      for epoch in range(epochs):
          for batch_idx, (data, target) in enumerate(train_loader):
              data = data.view(-1, 28*28)
              
              logits = forward(data)
              loss = criteon(logits, target)
              
              optimizer.zero_gradr()
              loss.backward()
              optimizer.step()
      

      这里先要求掌握以上代码的书写,后续需会讲解数据读取、结果验证等其他部分代码

      下面给出全部代码

      import  torch
      import  torch.nn as nn
      import  torch.nn.functional as F
      import  torch.optim as optim
      from torchvision import datasets, transforms
      
      
      batch_size=200
      learning_rate=0.01
      epochs=10
      
      train_loader = torch.utils.data.DataLoader(
          datasets.MNIST('../data', train=True, download=True,
                         transform=transforms.Compose([
                             transforms.ToTensor(),
                             transforms.Normalize((0.1307,), (0.3081,))
                         ])),
          batch_size=batch_size, shuffle=True)
      test_loader = torch.utils.data.DataLoader(
          datasets.MNIST('../data', train=False, transform=transforms.Compose([
              transforms.ToTensor(),
              transforms.Normalize((0.1307,), (0.3081,))
          ])),
          batch_size=batch_size, shuffle=True)
      
      
      
      w1, b1 = torch.randn(200, 784, requires_grad=True),\
               torch.zeros(200, requires_grad=True)
      w2, b2 = torch.randn(200, 200, requires_grad=True),\
               torch.zeros(200, requires_grad=True)
      w3, b3 = torch.randn(10, 200, requires_grad=True),\
               torch.zeros(10, requires_grad=True)
      
      torch.nn.init.kaiming_normal_(w1)
      torch.nn.init.kaiming_normal_(w2)
      torch.nn.init.kaiming_normal_(w3)
      
      
      def forward(x):
          x = x@w1.t() + b1
          x = F.relu(x)
          x = x@w2.t() + b2
          x = F.relu(x)
          x = x@w3.t() + b3
          x = F.relu(x)
          return x
      
      
      
      optimizer = optim.SGD([w1, b1, w2, b2, w3, b3], lr=learning_rate)
      criteon = nn.CrossEntropyLoss()
      
      for epoch in range(epochs):
      
          for batch_idx, (data, target) in enumerate(train_loader):
              data = data.view(-1, 28*28)
      
              logits = forward(data)
              loss = criteon(logits, target)
      
              optimizer.zero_grad()
              loss.backward()
              # print(w1.grad.norm(), w2.grad.norm())
              optimizer.step()
      
              if batch_idx % 100 == 0:
                  print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
                      epoch, batch_idx * len(data), len(train_loader.dataset),
                             100. * batch_idx / len(train_loader), loss.item()))
      
      
          test_loss = 0
          correct = 0
          for data, target in test_loader:
              data = data.view(-1, 28 * 28)
              logits = forward(data)
              test_loss += criteon(logits, target).item()
      
              pred = logits.data.max(1)[1]
              correct += pred.eq(target.data).sum()
      
          test_loss /= len(test_loader.dataset)
          print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
              test_loss, correct, len(test_loader.dataset),
              100. * correct / len(test_loader.dataset)))
      
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