code_torch_feed_mynet

• 这里更改了自己的neural network

• import torch.nn as nn
  
  
  class MyNetwork(nn.Module):
  
      def __init__(self,input_size=28,hidden_size=100 ,num_classes=10):
          pass
          super(MyNetwork,self).__init__()
          # 这里用的是两层的FC网络，中间加个ReLU激活层
          self.fc1 = nn.Linear(in_features=input_size, out_features=hidden_size)
          self.relu = nn.ReLU()
          self.fc2 = nn.Linear(in_features=hidden_size, out_features=num_classes)
  
      def forward(self,x):
          # 在forward中定义前推的流程
          out1 = self.fc1(x)
          out_relu1 = self.relu(out1)
          out2 = self.fc2(out_relu1)
          return out2
  
  

• 然后基于logistic的代码吧model换成MyNetwork

• import torch
  import torch.nn as nn
  import torchvision
  import torchvision.transforms as transforms
  from MyNetwork import MyNetwork
  
  ##############################################################################
  
  # 此份代码是根据torch_logistic_regression修改model部分
  
  ##############################################################################
  # Hyper-parameters
  input_size = 28 * 28
  num_classes = 10
  num_epochs = 20
  batch_size = 100
  learning_rate = 0.001
  
  
  # Mnist dataset
  train_dataset = torchvision.datasets.MNIST(root='../../data',
                                             train=True,
                                             transform=transforms.ToTensor(),
                                             download=True)
  test_dataset = torchvision.datasets.MNIST(root='../../data',
                                            train=False,
                                            transform=transforms.ToTensor())
  
  
  train_loader = torch.utils.data.DataLoader(dataset=train_dataset,
                                             batch_size=batch_size,
                                             shuffle=True)
  test_loader = torch.utils.data.DataLoader(dataset=test_dataset,
                                            batch_size=batch_size,
                                            shuffle=False)
  
  
  # model = nn.Linear(input_size, num_classes)
  model = MyNetwork(input_size=input_size, hidden_size=100,num_classes=num_classes)
  
  # Loss and optimizer
  # nn.CrossEntropyLoss() computes softmax internally
  criterion = nn.CrossEntropyLoss()
  # 这里优化器试了下Adam，效果还不错，在这个人物下比SGD好点
  optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
  
  # Train the model
  total_step = len(train_loader)
  for epoch in range(num_epochs):
      for i ,(images,lables) in enumerate(train_loader):
          # Reshape images to (batch_size,input_size)
          images = images.reshape(-1,input_size)
  
          # Forward pass
          outputs = model(images)
          loss = criterion(outputs, lables)
  
          # backward and optimize
          optimizer.zero_grad()
          loss.backward()
          optimizer.step()
          if (i + 1) % 100 == 0:
              print('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'.format(epoch + 1, num_epochs, i + 1, total_step, loss.item()))
  
  
  with torch.no_grad():
      correct = 0
      total = 0
      for images, labels in test_loader:
          images = images.reshape(-1, input_size)
          outputs = model(images)
          # 拿到最大的那个当作预测结果
          _, predicted = torch.max(outputs.data,1)
          total += labels.size(0)
          correct += (predicted == labels).sum()
      # 计算精确度
      print('Accuracy of the model on the 10000 test images: {} %'.format(100 * correct / total))
  
  
  
  # Save the model checkpoint
  torch.save(model.state_dict(), 'model.ckpt')
  
  

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