PyTorch搭建卷积神经网络实现手写数字识别

1.卷积神经网络介绍

卷积神经网络（Convolutional Neural Networks, CNN）是一类包含卷积计算且具有深度结构的前馈神经网络（Feedforward Neural Networks），是深度学习（deep learning）的代表算法之一 。卷积神经网络具有表征学习（representation learning）能力，能够按其阶层结构对输入信息进行平移不变分类（shift-invariant classification），因此也被称为“平移不变人工神经网络（Shift-Invariant Artificial Neural Networks, SIANN）”

2.卷积神经网络架构

卷积神经网络主要包括卷积层，采样层（一般做最大池化）和全连接层（FC层）。

3.Pytorch实现卷积神经网络

• 卷积层：nn.Conv2d()

其参数如下：

• 池化层：nn.MaxPool2d()

其参数如下：

4.实现MINST手写数字识别

一共定义了五层，其中两层卷积层，两层池化层，最后一层为FC层进行分类输出。其网络结构如下：

具体图片尺寸计算如下：

5.代码实现

import torch
from torchvision import transforms  # 是一个常用的图片变换类
from torchvision import datasets
from torch.utils.data import DataLoader
import torch.nn.functional as F

batch_size = 64
transform = transforms.Compose(
    [
        transforms.ToTensor(),  # 把数据转换成张量
        transforms.Normalize((0.1307,), (0.3081,))  # 0.1307是均值，0.3081是标准差
    ]
)
train_dataset = datasets.MNIST(root='../dataset/mnist',
                               train=True,
                               download=True,
                               transform=transform)
train_loader = DataLoader(train_dataset,
                          shuffle=True,
                          batch_size=batch_size)
test_dataset = datasets.MNIST(root='../dataset/mnist',
                              train=False,
                              download=True,
                              transform=transform)
test_loader = DataLoader(test_dataset,
                         shuffle=True,
                         batch_size=batch_size)


class CNN(torch.nn.Module):
    def __init__(self):
        super(CNN, self).__init__()
        self.layer1 = torch.nn.Sequential(
            torch.nn.Conv2d(1, 25, kernel_size=3),
            torch.nn.BatchNorm2d(25),
            torch.nn.ReLU(inplace=True)
        )

        self.layer2 = torch.nn.Sequential(
            torch.nn.MaxPool2d(kernel_size=2, stride=2)
        )

        self.layer3 = torch.nn.Sequential(
            torch.nn.Conv2d(25, 50, kernel_size=3),
            torch.nn.BatchNorm2d(50),
            torch.nn.ReLU(inplace=True)
        )

        self.layer4 = torch.nn.Sequential(
            torch.nn.MaxPool2d(kernel_size=2, stride=2)
        )

        self.fc = torch.nn.Sequential(
            torch.nn.Linear(50 * 5 * 5, 1024),
            torch.nn.ReLU(inplace=True),
            torch.nn.Linear(1024, 128),
            torch.nn.ReLU(inplace=True),
            torch.nn.Linear(128, 10)
        )

    def forward(self, x):
        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)
        x = x.view(x.size(0), -1)  # 在进入全连接层之前需要把数据拉直Flatten
        x = self.fc(x)
        return x


model = CNN()
# 下面两行代码主要是如果有GPU那么就使用GPU跑代码，否则就使用cpu。cuda:0表示第1块显卡
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")  # 将数据放在GPU上跑所需要的代码
model.to(device)  # 将数据放在GPU上跑所需要的代码
criterion = torch.nn.CrossEntropyLoss()  # 使用交叉熵损失
optimizer = torch.optim.SGD(model.parameters(), lr=0.1, momentum=0.5)  # momentum表示冲量，冲出局部最小


def train(epochs):
    running_loss = 0.0
    for batch_idx, data in enumerate(train_loader, 0):
        inputs, target = data
        inputs, target = inputs.to(device), target.to(device)  # 将数据放在GPU上跑所需要的代码
        optimizer.zero_grad()
        # 前馈+反馈+更新
        outputs = model(inputs)
        loss = criterion(outputs, target)
        loss.backward()
        optimizer.step()

        running_loss += loss.item()
        if batch_idx % 300 == 299:  # 不让他每一次小的迭代就输出，而是300次小迭代再输出一次
            print('[%d,%5d] loss:%.3f' % (epoch + 1, batch_idx + 1, running_loss / 300))
            running_loss = 0.0
    torch.save(model, 'model_{}.pth'.format(epochs))


def test():
    correct = 0
    total = 0
    with torch.no_grad():  # 下面的代码就不会再计算梯度
        for data in test_loader:
            inputs, target = data
            inputs, target = inputs.to(device), target.to(device)  # 将数据放在GPU上跑所需要的代码
            outputs = model(inputs)
            _, predicted = torch.max(outputs.data, dim=1)  # _为每一行的最大值，predicted表示每一行最大值的下标
            total += target.size(0)
            correct += (predicted == target).sum().item()
    print('Accuracy on test set:%d %%' % (100 * correct / total))


if __name__ == '__main__':
    for epoch in range(10):
        train(epoch)
        test()

6.结果