用PyTorch完成手写数字识别

数据集为MNIST

import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms
import torchvision
from torch.autograd import Variable
from torch.utils.data import DataLoader
import cv2# 下载训练集
train_dataset = datasets.MNIST(root='./num/',train=True,transform=transforms.ToTensor(),download=True)
# 下载测试集
test_dataset = datasets.MNIST(root='./num/',train=False,transform=transforms.ToTensor(),download=True)# dataset 参数用于指定我们载入的数据集名称
# batch_size参数设置了每个包中的图片数据个数
# 在装载的过程会将数据随机打乱顺序并进打包# 装载训练集
train_loader = torch.utils.data.DataLoader(dataset=train_dataset,batch_size=batch_size,shuffle=True)
# 装载测试集
test_loader = torch.utils.data.DataLoader(dataset=test_dataset,batch_size=batch_size,shuffle=True)images, labels = next(iter(data_loader_train))
img = torchvision.utils.make_grid(images)img = img.numpy().transpose(1, 2, 0)
std = [0.5, 0.5, 0.5]
mean = [0.5, 0.5, 0.5]
img = img * std + mean
print(labels)
cv2.imshow('win', img)
key_pressed = cv2.waitKey(0)# 卷积层使用 torch.nn.Conv2d
# 激活层使用 torch.nn.ReLU
# 池化层使用 torch.nn.MaxPool2d
# 全连接层使用 torch.nn.Linearclass LeNet(nn.Module):def __init__(self):super(LeNet, self).__init__()self.conv1 = nn.Sequential(nn.Conv2d(1, 6, 3, 1, 2), nn.ReLU(),nn.MaxPool2d(2, 2))self.conv2 = nn.Sequential(nn.Conv2d(6, 16, 5), nn.ReLU(),nn.MaxPool2d(2, 2))self.fc1 = nn.Sequential(nn.Linear(16 * 5 * 5, 120),nn.BatchNorm1d(120), nn.ReLU())self.fc2 = nn.Sequential(nn.Linear(120, 84),nn.BatchNorm1d(84),nn.ReLU(),nn.Linear(84, 10))# 最后的结果一定要变为 10，因为数字的选项是 0 ~ 9def forward(self, x):x = self.conv1(x)x = self.conv2(x)x = x.view(x.size()[0], -1)x = self.fc1(x)x = self.fc2(x)x = self.fc3(x)return xdevice = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
batch_size = 64
LR = 0.001net = LeNet().to(device)
# 损失函数使用交叉熵
criterion = nn.CrossEntropyLoss()
# 优化函数使用 Adam 自适应优化算法
optimizer = optim.Adam(net.parameters(),lr=LR,
)epoch = 1
if __name__ == '__main__':for epoch in range(epoch):sum_loss = 0.0for i, data in enumerate(train_loader):inputs, labels = datainputs, labels = Variable(inputs).cuda(), Variable(labels).cuda()optimizer.zero_grad()  #将梯度归零outputs = net(inputs)  #将数据传入网络进行前向运算loss = criterion(outputs, labels)  #得到损失函数loss.backward()  #反向传播optimizer.step()  #通过梯度做一步参数更新# print(loss)sum_loss += loss.item()if i % 100 == 99:print('[%d,%d] loss:%.03f' %(epoch + 1, i + 1, sum_loss / 100))sum_loss = 0.0net.eval()  #将模型变换为测试模式correct = 0total = 0for data_test in test_loader:images, labels = data_testimages, labels = Variable(images).cuda(), Variable(labels).cuda()output_test = net(images)_, predicted = torch.max(output_test, 1)total += labels.size(0)correct += (predicted == labels).sum()print("correct1: ", correct)print("Test acc: {0}".format(correct.item() /len(test_dataset)))