05,pytorch_手写数字案例

本是古典何须时尚 2023-01-09 03:58 144阅读 0赞

本节内容来自网易云课堂课程，主讲人为：龙良曲

# 1、关于MNIST #

*  每个数字都有7000张图片（each number owns 7000 images）
 *  训练集/测试集 数量比例：60k vs 10k (train/test splitting: 60k vs 10k)  
    ![在这里插入图片描述][20210120230832447.png_pic_center]

# 2、NO deep learning, just function mapping #

![在这里插入图片描述][2021012023105145.png_pic_center]

# 3 Loss ? #

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3RvdG8xMjk3NDg4NTA0_size_16_color_FFFFFF_t_70_pic_center]

# 4、In a nutshell #

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3RvdG8xMjk3NDg4NTA0_size_16_color_FFFFFF_t_70_pic_center 1]

# 5、Non-linear Factor #

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3RvdG8xMjk3NDg4NTA0_size_16_color_FFFFFF_t_70_pic_center 2]

# 6、Gradient Descent #

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3RvdG8xMjk3NDg4NTA0_size_16_color_FFFFFF_t_70_pic_center 3]

# 7、Inference #

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3RvdG8xMjk3NDg4NTA0_size_16_color_FFFFFF_t_70_pic_center 4]

# 8.手写数字问题 #

案例代码：

# -*- coding: UTF-8 -*-
    
    import torch
    from torch import nn
    from torch.nn import functional as F
    from torch import optim
    
    import torchvision
    from matplotlib import pyplot as plt
    
    
    def plot_curve(data):
        fig = plt.figure()
        plt.plot(range(len(data)), data, color='blue')
        plt.legend(['value'], loc='upper right')
        plt.xlabel('step')
        plt.ylabel('value')
        plt.show()
    
    
    def plot_image(img, label, name):
        fig = plt.figure()
        for i in range(6):
            plt.subplot(2, 3, i + 1)
            plt.tight_layout()
            plt.imshow(img[i][0] * 0.3081 + 0.1307, cmap='gray', interpolation='none')
            plt.title("{}: {}".format(name, label[i].item()))
            plt.xticks([])
            plt.yticks([])
        plt.show()
    
    
    def one_hot(label, depth=10):
        out = torch.zeros(label.size(0), depth)
        idx = torch.LongTensor(label).view(-1, 1)
        out.scatter_(dim=1, index=idx, value=1)
        return out
    
    
    batch_size = 512
    
    # step1. load dataset
    train_loader = torch.utils.data.DataLoader(
        torchvision.datasets.MNIST('mnist_data', train=True, download=True,
                                   transform=torchvision.transforms.Compose([
                                       torchvision.transforms.ToTensor(),
                                       torchvision.transforms.Normalize(
                                           (0.1307,), (0.3081,))
                                   ])),
        batch_size=batch_size, shuffle=True)
    
    test_loader = torch.utils.data.DataLoader(
        torchvision.datasets.MNIST('mnist_data/', train=False, download=True,
                                   transform=torchvision.transforms.Compose([
                                       torchvision.transforms.ToTensor(),
                                       torchvision.transforms.Normalize(
                                           (0.1307,), (0.3081,))
                                   ])),
        batch_size=batch_size, shuffle=False)
    
    
    x, y = next(iter(train_loader))
    print(x.shape, y.shape, x.min(), x.max())
    plot_image(x, y, 'image sample')
    
    
    class Net(nn.Module):
        def __init__(self):
            super(Net, self).__init__()
    
            # xw+b
            self.fc1 = nn.Linear(28 * 28, 256)
            self.fc2 = nn.Linear(256, 64)
            self.fc3 = nn.Linear(64, 10)
    
    
        def forward(self, x):
            # x: [b, 1, 28, 28]
            # h1 = relu(xw1+b1)
            x = F.relu(self.fc1(x))
            # h2 = relu(h1w2+b2)
            x = F.relu(self.fc2(x))
            # h3 = h2w3+b3
            x = self.fc3(x)
    
            return x
    
    
    net = Net()
    # [w1, b1, w2, b2, w3, b3]
    optimizer = optim.SGD(net.parameters(), lr=0.01, momentum=0.9)
    
    train_loss = []
    
    for epoch in range(3):
        for batch_idx, (x, y) in enumerate(train_loader):
    
            # x: [b, 1, 28, 28], y: [512]
            # [b, 1, 28, 28] => [b, 784]
            x = x.view(x.size(0), 28 * 28)
            # => [b, 10]
            out = net(x)
            # [b, 10]
            y_onehot = one_hot(y)
            # loss = mse(out, y_onehot)
            loss = F.mse_loss(out, y_onehot)
    
            optimizer.zero_grad()
            loss.backward()
            # w' = w - lr*grad
            optimizer.step()
    
            train_loss.append(loss.item())
    
            if batch_idx % 10 == 0:
                print(epoch, batch_idx, loss.item())
    
    plot_curve(train_loss)
    # we get optimal [w1, b1, w2, b2, w3, b3]
    
    
    total_correct = 0
    for x, y in test_loader:
        x = x.view(x.size(0), 28 * 28)
        out = net(x)
        # out: [b, 10] => pred: [b]
        pred = out.argmax(dim=1)
        correct = pred.eq(y).sum().float().item()
        total_correct += correct
    
    
    total_num = len(test_loader.dataset)
    acc = total_correct / total_num
    print('test acc:', acc)
    
    x, y = next(iter(test_loader))
    out = net(x.view(x.size(0), 28 * 28))
    pred = out.argmax(dim=1)
    plot_image(x, pred, 'test')

[20210120230832447.png_pic_center]: /images/20221119/5bff9f7bb3f24b3a8839b346c8895fb9.png
[2021012023105145.png_pic_center]: /images/20221119/d5902b3993f24e7686435e90a9ddf541.png
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[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3RvdG8xMjk3NDg4NTA0_size_16_color_FFFFFF_t_70_pic_center 1]: https://img-blog.csdnimg.cn/20210120231642806.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3RvdG8xMjk3NDg4NTA0,size_16,color_FFFFFF,t_70#pic_center
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