pytorch笔记5-mnist全连接网络模型

mnist数据集简介：
在torchvision中包含，可通过下列代码下载：

from torchvision import datasets
train_dataset = datasets.MNIST(root='./dataset/mnist/', train=True, download=True)
# test_dataset = datasets.MNIST(root='./dataset/mnist/', train=False, download=True)

上述代码会将数据下载到指定路径下，后面不需要再次下载，包含训练集（60000）、测试集（10000）的图片和标签共四个文件，
查看数据集：

from torchvision import transforms
from torchvision import datasets
mean,sqr=0.1307,0.3081 #minist数据集的均值和方差,transforms.ToTensor()会将数据转换至0-1，compose是将操作组合
transform=transforms.Compose([transforms.ToTensor(), transforms.Normalize((mean,), (sqr,))])
traindata=datasets.MNIST(root='./datasets/mnist',download=False,train=True,transform=transform)
testdata=datasets.MNIST(root='./datasets/mnist',download=False,train=False,transform=transform)
print(traindata.data.size())
print(traindata.targets.size())
print(testdata.data.size())
print(testdata.targets.size())

得到结果：

torch.Size([60000, 28, 28])
torch.Size([60000])
torch.Size([10000, 28, 28])
torch.Size([10000])

模型经过16轮训练在测试集可以达到98.14%的准确率：

import torch
import matplotlib.pyplot as plt
from torchvision import transforms
from torchvision import datasets
from torch.utils.data import DataLoader
batchsize=32
lr=0.01
mean,sqr=0.1307,0.3081 #minist数据集的均值和方差,transforms.ToTensor()会将数据转换至0-1，compose是将操作组合
transform=transforms.Compose([transforms.ToTensor(), transforms.Normalize((mean,), (sqr,))])
traindata=datasets.MNIST(root='./datasets/mnist',download=False,train=True,transform=transform)
testdata=datasets.MNIST(root='./datasets/mnist',download=False,train=False,transform=transform)
trainloard=DataLoader(dataset=traindata,batch_size=batchsize,shuffle=True,num_workers=0,drop_last=True)
testloard=DataLoader(dataset=testdata,batch_size=batchsize,shuffle=False,num_workers=0,drop_last=True)

class Model(torch.nn.Module):
    def __init__(self):
        super(Model,self).__init__()
        self.l1=torch.nn.Linear(784,256)
        self.l2=torch.nn.Linear(256,64)
        self.l3=torch.nn.Linear(64,32)
        self.l4=torch.nn.Linear(32,10)
        self.relu=torch.nn.ReLU()
    def forward(self,x):
        x=x.view(-1,28*28*1)
        x=self.relu(self.l1(x))
        x=self.relu(self.l2(x))
        x=self.relu(self.l3(x))
        x=self.l4(x)
        return x
        
model=Model()
criterion=torch.nn.CrossEntropyLoss()
optimizer=torch.optim.SGD(model.parameters(),lr=lr,momentum=0.5)

def train(allepoch):
    lepoch=[]
    llost=[]
    lacc=[]
    for epoch in range(allepoch):
        lost=0
        count=0
        for num,(x,y) in enumerate(trainloard,1):
            y_h=model(x)
            loss=criterion(y_h,y)
            optimizer.zero_grad()
            loss.backward()
            optimizer.step()
            lost+=loss.item()
            count+=batchsize
        print('epoch:',epoch+1,'loss:',lost/count,end=' ')
        lepoch.append(epoch+1)
        llost.append(lost/count)
        acc=test()
        lacc.append(acc)
        print('acc:',acc)
    plt.plot(lepoch,llost,label='loss')
    plt.plot(lepoch,lacc,label='acc')
    plt.legend()
    plt.show()
    
def test():
    with torch.no_grad():
        acc=0
        count=0
        for num, (x, y) in enumerate(testloard, 1):
            y_h = model(x)
            _,y_h=torch.max(y_h.data,dim=1)
            acc+= (y_h==y).sum().item()
            count+=x.size(0)
        return acc/count

if __name__=='__main__':
    train(30)

结果如下：