Pytorch实战——MNIST数据集手写数字识别

#1 加载必要的库
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets,transforms

#2 定义超参数
BATCH_SIZE= 64 #每次训练的数据的个数
DEVICE=torch.device("cuda"if torch.cuda.is_available()else"cpu")#做一个判断，如果有gpu就用gpu，如果没有的话就用cpu
EPOCHS=20 #训练轮数

#3 构建pipeline，对图像做处理
pipeline=transforms.Compose([
    transforms.ToTensor(),#将图片转换成tensor类型
    transforms.Normalize((0.1307,),(0.3081,))#正则化，模型过拟合时，降低模型复杂度
])

#下载数据集
train_set=datasets.MNIST("data",train=True,download=True,transform=pipeline)#文件夹，需要训练，需要下载，要转换成tensor

test_set=datasets.MNIST("data",train=False,download=True,transform=pipeline)
#加载数据
train_loader=DataLoader(train_set,batch_size=BATCH_SIZE,shuffle=True)#加载训练集，数据个数为64，需要打乱

test_loader=DataLoader(test_set,batch_size=BATCH_SIZE,shuffle=True)

with open("MNIST的绝对路径","rb") as f:
    file=f.read()

image1=[int(str(item).encode('ascii'),10)for item in file[16: 16+784]]
print(image1)

import cv2
import numpy as np

image1_np=np.array(image1,dtype=np.uint8).reshape(28,28,1)

print(image1_np.shape)
cv2.imwrite("digit.jpg",image1_np)#保存图片

#5 构建网络模型
class Digit(nn.Module):
    def __init__(self):
        super().__init__()
        self.conv1= nn.Conv2d(1,10,kernel_size=5)
        self.conv2=nn.Conv2d(10,20,kernel_size=3)
        self.fc1=nn.Linear(20*10*10,500)
        self.fc2=nn.Linear(500,10)

    def forward(self,x):
       input_size=x.size(0)
       x=self.conv1(x)#输入batch_size 1*28*28 ，输出batch 10*24*24(28-5+1=24)
       x=F.relu(x) #激活函数,保持shape不变
       x=F.max_pool2d(x,2,2)#2是池化步长的大小,输出batch20*12*12

       x=self.conv2(x)#输入batch20*12*12 输出batch 20*10*10 (12-3+1=10)
       x=F.relu(x)

       x=x.view(input_size,-1)#拉平,-1自动计算维度 20*10*10=2000

       x=self.fc1(x)#输入batch 2000 输出batch 500
       x=F.relu(x)
       x=self.fc2(x)#输出batch 500 输出batch 10

       ouput=F.log_softmax(x,dim=1)#计算分类后每个数字概率
       return ouput

model =Digit().to(device)

optimizer=optim.Adam(model.parameters())##选择adam优化器

#7 定义训练方法
def train_model(model,device,train_loader,optimizer,epoch):
    #模型训练
    model.train()
    for batch_index,(data,target) in enumerate(train_loader):
        #部署到DEVICE上去
        data,target=data.to(device), target.to(device)
        #梯度初始化为0
        optimizer.zero_grad()
        #预测,训练后结果
        output=model(data)
        #计算损失
        loss = F.cross_entropy(output,target)#多分类用交叉验证
        #反向传播
        loss.backward()
        #参数优化
        optimizer.step()
        if batch_index%3000==0:
            print("Train Epoch :{}  \t Loss :{:.6f}".format(epoch,loss.item()))

#8 定义测试方法
def test_model(model,device,test_loader):
    #模型验证
    model.eval()
    #正确率
    corrcet=0.0
    #测试损失
    test_loss=0.0
    with torch.no_grad():   #不会计算梯度，也不会进行反向传播
            for data,target in test_loader:
                #部署到device上
                data,target=data.to(device),target.to(device)
                #测试数据
                output=model(data)
                #计算测试损失
                test_loss+=F.cross_entropy(output,target).item()
                #找到概率最大的下标
                pred=output.argmax(1)
                #累计正确的值
                corrcet+=pred.eq(target.view_as(pred)).sum().item()
            test_loss/=len(test_loader.dataset)
            print("Test--Average Loss:{:.4f},Accuarcy:{:.3f}\n".format(test_loss,100.0 * corrcet / len(test_loader.dataset)))

#9 调用方法
for epoch in range(1,EPOCHS+1):
    train_model(model,DEVICE,train_loader,optimizer,epoch)
    test_model(model,DEVICE,test_loader)

Train Epoch :1  	 Loss :2.296158
Train Epoch :1  	 Loss :0.023645
Test--Average Loss:0.0027,Accuarcy:98.690

Train Epoch :2  	 Loss :0.035262
Train Epoch :2  	 Loss :0.002957
Test--Average Loss:0.0027,Accuarcy:98.750

Train Epoch :3  	 Loss :0.029884
Train Epoch :3  	 Loss :0.000642
Test--Average Loss:0.0032,Accuarcy:98.460

Train Epoch :4  	 Loss :0.002866
Train Epoch :4  	 Loss :0.003708
Test--Average Loss:0.0033,Accuarcy:98.720

Train Epoch :5  	 Loss :0.000039
Train Epoch :5  	 Loss :0.000145
Test--Average Loss:0.0026,Accuarcy:98.840

Train Epoch :6  	 Loss :0.000124
Train Epoch :6  	 Loss :0.035326
Test--Average Loss:0.0054,Accuarcy:98.450

Train Epoch :7  	 Loss :0.000014
Train Epoch :7  	 Loss :0.000001
Test--Average Loss:0.0044,Accuarcy:98.510

Train Epoch :8  	 Loss :0.001491
Train Epoch :8  	 Loss :0.000045
Test--Average Loss:0.0031,Accuarcy:99.140

Train Epoch :9  	 Loss :0.000428
Train Epoch :9  	 Loss :0.000000
Test--Average Loss:0.0056,Accuarcy:98.500

Train Epoch :10  	 Loss :0.000001
Train Epoch :10  	 Loss :0.000377
Test--Average Loss:0.0042,Accuarcy:98.930

#5 构建网络模型
class Digit(nn.Module):
    def __init__(self):
        super().__init__()
        self.conv1= nn.Conv2d(1,10,5)
        self.conv2=nn.Conv2d(10,20,5)
        self.fc1=nn.Linear(20*4*4,10)

    def forward(self,x):
       input_size=x.size(0)
       x=self.conv1(x)#输入batch_size 1*28*28 ，输出batch 10*24*24(28-5+1=24)
       x=F.relu(x) #激活函数,保持shape不变
       x=F.max_pool2d(x,2,2)#2是池化步长的大小,输出batch20*12*12

       x=self.conv2(x)#输入batch20*12*12 输出batch 20*10*10 (12-3+1=10)
       x=F.relu(x)
       x=F.max_pool2d(x,2,2)

       x=x.view(input_size,-1)#拉平,-1自动计算维度 20*10*10=2000

       x=self.fc1(x)#输入batch 2000 输出batch 500
       x=F.relu(x)


       ouput=F.log_softmax(x,dim=1)#计算分类后每个数字概率
       return ouput

Train Epoch :17  	 Loss :0.014693
Train Epoch :17  	 Loss :0.000051
Test--Average Loss:0.0026,Accuarcy:99.010