手写数字识别

先给代码，如下：

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

from utils import plot_image, plot_curve, one_hot

# step.1 Load Dataset
batch_size = 512
train_loader = torch.utils.data.DataLoader(
    torchvision.datasets.MNIST('mnist_data', train=True, download=True,
                               transform=torchvision.transforms.Compose([
                                   torchvision.transforms.ToTensor(),  # 将灰度图片像素值（0~255）转为Tensor（0~1），方便后续处理
                                   torchvision.transforms.Normalize(
                                       (0.1307,), (0.3081,))  # 归一化，均值0，方差1
                               ])),
    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(),  # 将灰度图片像素值（0~255）转为Tensor（0~1），方便后续处理
                                   torchvision.transforms.Normalize(
                                       (0.1307,), (0.3081,))  # 归一化，均值0，方差1
                               ])),
    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')

# step.2 Build Module
class Net(nn.Module):

    def __init__(self):
        super(Net, self).__init__()  # python的父类初始化
        # wx + 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(w1x + 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


# step.3 train module
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)

        # 梯度计算
        optimizer.zero_grad()   # 清零梯度
        loss.backward()         # 反向传播
        # w' = w - lr*grad
        optimizer.step()

        train_loss.append(loss.item())
        if batch_idx % 10 == 0:
            print(epoch, batch_idx, loss.item())

torch.save(net, 'Number_recognition.pt')
# get optimal [w1, b1, w2, b2, w3, b3]

# step.4 Test
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()
    total_correct += correct

total_num = len(test_loader.dataset)
acc = total_correct / total_num
print("test acc:", acc)
plot_curve(train_loss)

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')

结果：

接下来代码讲解

step.1 Load Dataset

我们使用的是非常经典的 mnist_data 手写数字数据集

这里配好环境之后，如何没有mnist_data数据集，会从网上自行下载，不用担心

torch.utils.data.DataLoader是一个数据加载器，该做的都给我们做完了

step.2 Build Module

这里我们继承nn.Module这个模型类来实现自己的模型

forward函数会在 "call" 里面被调用，这些都是python类的知识，如下：

forward方法是必须要重写的，它是实现模型的功能，实现各个层之间的连接关系的核心

只要在nn.Module的子类中定义了forward函数，backward函数就会被自动实现(利用Autograd)

自动调用 forward 函数原因分析：

利用Python的语言特性，y = model(x)是调用了对象model的*call**方法，而nn.Module把*_*call*_方法实现为类对象的forward函数，所以任意继承了nn.Module的类对象都可以这样简写来调用forward函数

关于模型的搭建都是通式，模板是固定的，只要写过一次，就可以改别人的网络了

# step.2 Build Module
class Net(nn.Module):

    def __init__(self):
        super(Net, self).__init__()  # python的父类初始化
        # wx + 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(w1x + 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

step.3 train module

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)

        # 梯度计算
        optimizer.zero_grad()   # 清零梯度
        loss.backward()         # 反向传播
        # w' = w - lr*grad
        optimizer.step()

        train_loss.append(loss.item())
        if batch_idx % 10 == 0:
            print(epoch, batch_idx, loss.item())

torch.save(net, 'Number_recognition.pt')

这里的每一步都是框架预先实现了的，我们直接调函数就行了

我们的这个卷积神经网络非常简单，就3个线性层

有不懂的函数就直接上网查就可以查到了

step.4 Test

# step.4 Test
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()
    total_correct += correct

total_num = len(test_loader.dataset)
acc = total_correct / total_num
print("test acc:", acc)
plot_curve(train_loss)

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')

这里就是对我们训练出来的模型进行测试了，从 test_loader 测试集下载器里面把测试数据拿出来

向前传播预测出结果，与测试集进行比对，最后展示预测结果