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PyTorch CNN实战之MNIST手写数字识别示例
2022-01-14 21人围观 0条评论
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    简介

    卷积神经网络(Convolutional Neural Network, CNN)是深度学习技术中极具代表的网络结构之一,在图像处理领域取得了很大的成功,在国际标准的ImageNet数据集上,许多成功的模型都是基于CNN的。

    卷积神经网络CNN的结构一般包含这几个层:

    1. 输入层:用于数据的输入
    2. 卷积层:使用卷积核进行特征提取和特征映射
    3. 激励层:由于卷积也是一种线性运算,因此需要增加非线性映射
    4. 池化层:进行下采样,对特征图稀疏处理,减少数据运算量。
    5. 全连接层:通常在CNN的尾部进行重新拟合,减少特征信息的损失
    6. 输出层:用于输出结果

    PyTorch实战

    本文选用上篇的数据集MNIST手写数字识别实践CNN。

    import torch
    import torch.nn as nn
    import torch.nn.functional as F
    import torch.optim as optim
    from torchvision import datasets, transforms
    from torch.autograd import Variable
    
    # Training settings
    batch_size = 64
    
    # MNIST Dataset
    train_dataset = datasets.MNIST(root='./data/',
                    train=True,
                    transform=transforms.ToTensor(),
                    download=True)
    
    test_dataset = datasets.MNIST(root='./data/',
                   train=False,
                   transform=transforms.ToTensor())
    
    # Data Loader (Input Pipeline)
    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)
    
    
    class Net(nn.Module):
      def __init__(self):
        super(Net, self).__init__()
        # 输入1通道,输出10通道,kernel 5*5
        self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
        self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
        self.mp = nn.MaxPool2d(2)
        # fully connect
        self.fc = nn.Linear(320, 10)
    
      def forward(self, x):
        # in_size = 64
        in_size = x.size(0) # one batch
        # x: 64*10*12*12
        x = F.relu(self.mp(self.conv1(x)))
        # x: 64*20*4*4
        x = F.relu(self.mp(self.conv2(x)))
        # x: 64*320
        x = x.view(in_size, -1) # flatten the tensor
        # x: 64*10
        x = self.fc(x)
        return F.log_softmax(x)
    
    
    model = Net()
    
    optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.5)
    
    def train(epoch):
      for batch_idx, (data, target) in enumerate(train_loader):
        data, target = Variable(data), Variable(target)
        optimizer.zero_grad()
        output = model(data)
        loss = F.nll_loss(output, target)
        loss.backward()
        optimizer.step()
        if batch_idx % 200 == 0:
          print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
            epoch, batch_idx * len(data), len(train_loader.dataset),
            100. * batch_idx / len(train_loader), loss.data[0]))
    
    
    def test():
      test_loss = 0
      correct = 0
      for data, target in test_loader:
        data, target = Variable(data, volatile=True), Variable(target)
        output = model(data)
        # sum up batch loss
        test_loss += F.nll_loss(output, target, size_average=False).data[0]
        # get the index of the max log-probability
        pred = output.data.max(1, keepdim=True)[1]
        correct += pred.eq(target.data.view_as(pred)).cpu().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)))
    
    
    for epoch in range(1, 10):
      train(epoch)
      test()
    
    

    输出结果:

    Train Epoch: 1 [0/60000 (0%)]   Loss: 2.315724
    Train Epoch: 1 [12800/60000 (21%)]  Loss: 1.931551
    Train Epoch: 1 [25600/60000 (43%)]  Loss: 0.733935
    Train Epoch: 1 [38400/60000 (64%)]  Loss: 0.165043
    Train Epoch: 1 [51200/60000 (85%)]  Loss: 0.235188

    Test set: Average loss: 0.1935, Accuracy: 9421/10000 (94%)

    Train Epoch: 2 [0/60000 (0%)]   Loss: 0.333513
    Train Epoch: 2 [12800/60000 (21%)]  Loss: 0.163156
    Train Epoch: 2 [25600/60000 (43%)]  Loss: 0.213840
    Train Epoch: 2 [38400/60000 (64%)]  Loss: 0.141114
    Train Epoch: 2 [51200/60000 (85%)]  Loss: 0.128191

    Test set: Average loss: 0.1180, Accuracy: 9645/10000 (96%)

    Train Epoch: 3 [0/60000 (0%)]   Loss: 0.206469
    Train Epoch: 3 [12800/60000 (21%)]  Loss: 0.234443
    Train Epoch: 3 [25600/60000 (43%)]  Loss: 0.061048
    Train Epoch: 3 [38400/60000 (64%)]  Loss: 0.192217
    Train Epoch: 3 [51200/60000 (85%)]  Loss: 0.089190

    Test set: Average loss: 0.0938, Accuracy: 9723/10000 (97%)

    Train Epoch: 4 [0/60000 (0%)]   Loss: 0.086325
    Train Epoch: 4 [12800/60000 (21%)]  Loss: 0.117741
    Train Epoch: 4 [25600/60000 (43%)]  Loss: 0.188178
    Train Epoch: 4 [38400/60000 (64%)]  Loss: 0.049807
    Train Epoch: 4 [51200/60000 (85%)]  Loss: 0.174097

    Test set: Average loss: 0.0743, Accuracy: 9767/10000 (98%)

    Train Epoch: 5 [0/60000 (0%)]   Loss: 0.063171
    Train Epoch: 5 [12800/60000 (21%)]  Loss: 0.061265
    Train Epoch: 5 [25600/60000 (43%)]  Loss: 0.103549
    Train Epoch: 5 [38400/60000 (64%)]  Loss: 0.019137
    Train Epoch: 5 [51200/60000 (85%)]  Loss: 0.067103

    Test set: Average loss: 0.0720, Accuracy: 9781/10000 (98%)

    Train Epoch: 6 [0/60000 (0%)]   Loss: 0.069251
    Train Epoch: 6 [12800/60000 (21%)]  Loss: 0.075502
    Train Epoch: 6 [25600/60000 (43%)]  Loss: 0.052337
    Train Epoch: 6 [38400/60000 (64%)]  Loss: 0.015375
    Train Epoch: 6 [51200/60000 (85%)]  Loss: 0.028996

    Test set: Average loss: 0.0694, Accuracy: 9783/10000 (98%)

    Train Epoch: 7 [0/60000 (0%)]   Loss: 0.171613
    Train Epoch: 7 [12800/60000 (21%)]  Loss: 0.078520
    Train Epoch: 7 [25600/60000 (43%)]  Loss: 0.149186
    Train Epoch: 7 [38400/60000 (64%)]  Loss: 0.026692
    Train Epoch: 7 [51200/60000 (85%)]  Loss: 0.108824

    Test set: Average loss: 0.0672, Accuracy: 9793/10000 (98%)

    Train Epoch: 8 [0/60000 (0%)]   Loss: 0.029188
    Train Epoch: 8 [12800/60000 (21%)]  Loss: 0.031202
    Train Epoch: 8 [25600/60000 (43%)]  Loss: 0.194858
    Train Epoch: 8 [38400/60000 (64%)]  Loss: 0.051497
    Train Epoch: 8 [51200/60000 (85%)]  Loss: 0.024832

    Test set: Average loss: 0.0535, Accuracy: 9837/10000 (98%)

    Train Epoch: 9 [0/60000 (0%)]   Loss: 0.026706
    Train Epoch: 9 [12800/60000 (21%)]  Loss: 0.057807
    Train Epoch: 9 [25600/60000 (43%)]  Loss: 0.065225
    Train Epoch: 9 [38400/60000 (64%)]  Loss: 0.037004
    Train Epoch: 9 [51200/60000 (85%)]  Loss: 0.057822

    Test set: Average loss: 0.0538, Accuracy: 9829/10000 (98%)

    Process finished with exit code 0

    参考:https://github.com/hunkim/PyTorchZeroToAll

    以上就是本文的全部内容,希望对大家的学习有所帮助,也希望大家多多支持脚本之家。

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