Pytorch基于MNIST数据集简单实现手写数字识别

"""
模型训练代码
"""
import torch
import torchvision.datasets
from torch import nn
from torchvision import transforms
from torch.utils.data import DataLoader
import cv2
 
# 这里我们使用LeNet定义我们的模型
net = nn.Sequential(
    nn.Conv2d(1, 6, kernel_size=5, padding=2), nn.Sigmoid(),
    nn.AvgPool2d(kernel_size=2, stride=2),
    nn.Conv2d(6, 16, kernel_size=5), nn.Sigmoid(),
    nn.AvgPool2d(kernel_size=2, stride=2),
    nn.Flatten(),
    nn.Linear(16 * 5 * 5, 120), nn.Sigmoid(),
    nn.Linear(120, 84), nn.Sigmoid(),
    nn.Linear(84, 10)
)
 
 
train_data = torchvision.datasets.MNIST(root='MNIST',
                                        train=True,
                                        transform=torchvision.transforms.ToTensor(),
                                        download=True
                                        )
 
test_data = torchvision.datasets.MNIST(root='MNIST',
                                       train=False,
                                       transform=torchvision.transforms.ToTensor(),
                                       download=True
                                       )
 
train_loader = DataLoader(train_data, batch_size=100, shuffle=True)
test_loader = DataLoader(test_data, batch_size=100, shuffle=True)
 
 
# images, lables = next(iter(train_loader))
# img = torchvision.utils.make_grid(images, nrow=10)  # 把若干图像拼接成一张图像
# img = img.numpy().transpose(1, 2, 0)
# cv2.imshow('img', img)
# cv2.waitKey(0)
 
 
# for data in train_loader:
#     imgs, target = data
#     # print(imgs.shape)
#     # print(target.shape)
#     print(target)
#     # print(data[0].shape)  # (100 , 1, 28, 28) 100个皮偏高 1个通道 28 * 28 的图像
#     break
 
 
loss = nn.CrossEntropyLoss()  # 损失函数
 
optim = torch.optim.Adam(net.parameters(), lr=0.001)  # 优化器
 
num_epochs = 20
 
 
for epoch in range(num_epochs):
    sum_loss = 0.0
    for data in train_loader:
        imgs, targets = data
        outputs = net(imgs)
        result_loss = loss(outputs, targets)
        optim.zero_grad()  # 梯度清零
        result_loss.backward()
        optim.step()  # 进行优化
        sum_loss = sum_loss + result_loss
    print(f'epoch:{epoch + 1}，训练误差 :{sum_loss/len(train_data)}')
 
 
# 测试
net.eval()
test_acc = 0
for data in test_loader:
    imgs, targets = data
    outputs = net(imgs)
    _, id = torch.max(outputs.data, 1)  # 1表示维度 返回概率最大的索引
    test_acc += torch.sum(id == targets.data)
print("测试误差:%.3f" %((test_acc * 100) / len(test_data)))
 
# 模型的保存
torch.save(net.state_dict(), "net_parameters.pth")
 

"""
简易可视化
"""
import torch
import torchvision.datasets
from torch import nn
from d2l import torch as d2l
from torchvision import transforms
from torch.utils.data import DataLoader
import cv2
 
net = nn.Sequential(
    nn.Conv2d(1, 6, kernel_size=5, padding=2),nn.Sigmoid(),
    nn.AvgPool2d(kernel_size=2, stride=2),
    nn.Conv2d(6, 16, kernel_size=5), nn.Sigmoid(),
    nn.AvgPool2d(kernel_size=2, stride=2),
    nn.Flatten(),
    nn.Linear(16 * 5 * 5, 120), nn.Sigmoid(),
    nn.Linear(120, 84), nn.Sigmoid(),
    nn.Linear(84, 10)
)
 
net.load_state_dict(torch.load(r"D:\PycharmProjects\pytorch_study\easy_test\net_parameters.pth"))
 
test_data = torchvision.datasets.MNIST(root='MNIST',
                                       train=False,
                                       transform=torchvision.transforms.ToTensor(),
                                       download=True
                                       )
 
test_loader = DataLoader(test_data, batch_size=1, shuffle=True)
 
"""
下面这里只是一个简单的可视化，读者可自行优化
"""
for data in test_loader:
    imgs, targets = data
    output = net(imgs)
    print(torch.topk(output, 1)[1].squeeze(0))
    img = imgs.numpy().reshape((28, 28))
    cv2.imshow('img', img)
    cv2.waitKey(0)
    break