torch_feed_mynet

àì夳堔傛蜴生んèń 2022-09-10 10:23 94阅读 0赞

### 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')
 *  ![image-20210907141411679.png][]
 *

[image-20210907141411679.png]: https://gitee.com/coconut_milk_candy/image-res/raw/master/blog/image-20210907141411679.png