此博客并不是教程，只是一个练习总结 代码汇总放在文末

1.首先导入所需要的库

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 import pandas as pd import numpy as np from Util import plot_image,pd_one_hot #辅助函数，在博客末尾附上

2.数据集

此数据集总共包含70K张图片，其中60K作为训练集，10K作为测试集。 更多消息可以查看官网官网链接：官网

3.加载数据

batch_size设置一次处理多少图片，此处设置为512张图片，这样并行处理可以cpu，gpu加快处理速度

batch_size = 512

加载训练集，测试集图片

train_loader = torch.utils.data.DataLoader( torchvision.datasets.MNIST('mnist_data' #数据集文件夹名 ,train=True ,download=True #当电脑没此数据的时候会自动下载数据集 ,transform=torchvision.transforms.Compose([ torchvision.transforms.ToTensor() #矩阵转化为张量 ,torchvision.transforms.Normalize((0.1307,), (0.3081,)) ]) ) ,batch_size=batch_size ,shuffle=True # 设置随机打散 ) 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)

4.数据可视化

只查看9张图片，可在辅助函数内修改为其它值

x, y = next(iter(train_loader)) print(x.shape, y.shape) # 查看数据集大小 plot_image(x, y, 'image sample') torch.Size([512, 1, 28, 28]) torch.Size([512])

注： 512, 1, 28, 28:四维矩阵，512张图片，1个通道，大小为28*28 1个通道的意思为单色，若改为3则是RGB彩色

5.定义神经网络

class Net(nn.Module): def __init__(self): super(Net , self).__init__() self.fc1 = nn.Linear(28*28 , 256) # 输入和输出的维度，根据经验自己设置 self.fc2 = nn.Linear(256 , 64) # 输入维度要等于上层的输出维度 self.fc3 = nn.Linear(64 , 10) # 数字结果为0~9，所以最后输出值为10个维度 def forward(self , x): x = F.relu(self.fc1(x)) # relu 激活函数 x = F.relu(self.fc2(x)) x = self.fc3(x) return x

6.训练测试集

1.初始化网络

net = Net()

2.设置学习率

optimizer = optim.SGD(net.parameters() , lr = 0.01 , momentum= 0.9)

3.迭代

此处没有调用GPU处理数据

loss_s = [ ] # 存储损失值 for each in range(3): # 迭代三次 for location , (x,y) in enumerate(train_loader): x = x.view(x.size(0) , 28*28) #将图片矩阵打平 out = net(x) y_onehot = pd_one_hot(y) loss = F.mse_loss(out , torch.from_numpy(y_onehot).float()) # 清零梯度 optimizer.zero_grad() # 计算梯度 loss.backward() # 更新梯度 optimizer.step() if(location % 5 == 0): # 每处理5*512张图片记录一次损失函数值 loss_s.append(loss.item()) # .item的意思为只输出值 print('第' , each+1 , '次迭代完成') 第 1 次迭代完成 第 2 次迭代完成 第 3 次迭代完成

4.查看损失值

plt.plot(range(len(loss_s)) , loss_s , 'y') plt.show()

损失值递减且趋于稳定，训练过程正确

7.预测训练集

# 存储预测正确图片的数量 total_correct = 0 for x,y in test_loader: x = x.view(x.size(0), 28*28) out = net(x) pred = out.argmax(dim=1) # 取最大值概率所在的位置 correct = pred.eq(y).sum().float().item() total_correct += correct print('正确率：' , total_correct/len(test_loader.dataset)) 正确率： 0.8903

8.查看测试集图片

x , y = next(iter(test_loader)) plot_image(x , y , 'test')

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Util.py代码

import numpy as np import matplotlib.pyplot as plt import pandas as pd def plot_image(img, label, name): fig = plt.figure() for i in range(9): plt.subplot(3, 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 pd_one_hot(y): y = y.reshape(-1 , 1) y = pd.Series(y) # 使用pandas的one-hot处理 y= y.astype(str) y = pd.get_dummies(y) return y.values

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正文代码汇总

项目github链接: github.com/2979083263/mnist

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 import pandas as pd import numpy as np from Util import plot_curve,plot_image,one_hot,pd_one_hot 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() #矩阵转化为张量 ,torchvision.transforms.Normalize((0.1307,), (0.3081,)) ]) ) ,batch_size=batch_size ,shuffle=True # 设置随机打散 ) 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)) #暂时看作迭代 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__() self.fc1 = nn.Linear(28*28 , 256) #输入和输出的维度 self.fc2 = nn.Linear(256 , 64) self.fc3 = nn.Linear(64 , 10) def forward(self , x): x = F.relu(self.fc1(x)) x = F.relu(self.fc2(x)) x = self.fc3(x) return x net = Net() optimizer = optim.SGD(net.parameters() , lr = 0.01 , momentum= 0.9) loss_s = [ ] for each in range(3): for location , (x,y) in enumerate(train_loader): x = x.view(x.size(0) , 28*28) out = net(x) y_onehot = pd_one_hot(y) loss = F.mse_loss(out , torch.from_numpy(y_onehot).float()) # 清零梯度 optimizer.zero_grad() # 计算梯度 loss.backward() # 更新梯度 optimizer.step() if(location % 5 == 0): loss_s.append(loss.item()) print('第' , each+1 , '次迭代完成') plt.plot(range(len(loss_s)) , loss_s , 'y') plt.show() # 存储正确的数量 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 print('正确率：' , total_correct/len(test_loader.dataset)) x , y = next(iter(test_loader)) plot_image(x , y , 'test')