From 4420fe3ff2d145cc75aa72aba214c12d084cd811 Mon Sep 17 00:00:00 2001 From: chengzhiwei Date: Wed, 12 Mar 2025 17:25:13 +0800 Subject: [PATCH] =?UTF-8?q?=E5=88=A0=E9=99=A4=2024ALexNet=5F02.py?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- 24ALexNet_02.py | 115 ------------------------------------------------ 1 file changed, 115 deletions(-) delete mode 100644 24ALexNet_02.py diff --git a/24ALexNet_02.py b/24ALexNet_02.py deleted file mode 100644 index f22d437..0000000 --- a/24ALexNet_02.py +++ /dev/null @@ -1,115 +0,0 @@ -import torch -import torchvision.datasets -from matplotlib import pyplot as plt -from torch import nn -from torch.utils.data import DataLoader - -batch_size=10 -train_data=torchvision.datasets.MNIST(root="./data",train=True,download=True,transform=torchvision.transforms.ToTensor()) -test_data=torchvision.datasets.MNIST(root="./data",train=False,download=True,transform=torchvision.transforms.ToTensor()) - - -train_dataloader=DataLoader(dataset=train_data,batch_size=batch_size) -test_dataloader=DataLoader(dataset=test_data,batch_size=batch_size) - - -# #输出数据集中的第一个图片 -# plt.imshow(train_data.data[0].numpy(),cmap='gray') -# plt.title('%i' % train_data.targets[0]) -# plt.show() - - -class AlexNet(nn.Module): - def __init__(self): - super().__init__() - self.conv1=nn.Sequential( - nn.Conv2d(in_channels=1,out_channels=96,kernel_size=3,stride=1,padding=1),#28*28 - nn.ReLU(), - #2.改 3通道 - #3. 写net 传函数 和 新模型写类区别 - #4.用batch - nn.MaxPool2d(kernel_size=3,stride=1)#26*26 - ) - self.conv2=nn.Sequential( - nn.Conv2d(in_channels=96,out_channels=256,kernel_size=5,padding=2),#26*26 - nn.ReLU(), - nn.MaxPool2d(kernel_size=3,stride=1)#24*24 - ) - self.conv3=nn.Sequential( - nn.Conv2d(in_channels=256,out_channels=384,kernel_size=3,padding=1), - nn.ReLU(), - nn.Conv2d(in_channels=384,out_channels=384,kernel_size=3,padding=1), - nn.ReLU(), - nn.Conv2d(in_channels=384,out_channels=256,kernel_size=3,padding=1), - nn.ReLU(), - nn.MaxPool2d(kernel_size=3,stride=1)#22*22 - ) - self.flatten=nn.Flatten() - self.end=nn.Sequential( - nn.Linear(in_features=256*22*22,out_features=256), - nn.ReLU(), - nn.Linear(in_features=256,out_features=10) - ) - def forward(self,x): - x=self.conv1(x) - x=self.conv2(x) - x=self.conv3(x) - x=self.flatten(x) - x=self.end(x) - return x - - -net=AlexNet() -loss_cross=nn.CrossEntropyLoss() - -optim=torch.optim.Adam(net.parameters(),lr=0.001) - -def train(net,train_data): - for data,targets in train_data: - #print("targets:",targets) - #print("data.shape:",data.shape) - #print("targets.shape:",targets.shape) - #print("targets.dty24ALexNet.pype",targets.dtype) - #data=data.reshape(10,1,28,28) #1,28,28->1,1,28,28 ? - data=net(data) - - #计算每次训练的准确率 - acc=(data.argmax(1)==targets).sum() #用sum() 和 sum(0)都可以 || 加()的是函数 不加()的是属性 - #print(acc.dtype) - accury=acc/batch_size - #print("正确数：",acc,"正确率",accury) - print("正确数:{},正确率:{}".format(acc,accury))#跑的时候，除10 有 0.0000几的误差???? - loss= loss_cross(data,targets) - #print("loss:",loss) #loss值代表？?? - print("loss:{}".format(loss)) - loss.backward() - optim.step() - optim.zero_grad() - return net - -def yanzheng(net,test_data): - net.eval() - acc=0 - sum=batch_size - with torch.no_grad(): - for data,targets in test_data: - data=net(data) - acc=(data.argmax(1)==targets).sum() - accury = acc / batch_size - print("正确数：",acc," ","正确率",accury) - -#训练数据 -#net=train(net,train_dataloader) #不返回也没事 训练之后的参数会保留,(只要是在这一次程序中运行的) - -#保存模型 此方法需要保证可以找到自己定义的class AlexNet模型 （直接加载模型应该也可以吧 eg:VGG） ||我认为小土堆的第二种方法 也需要保证运行的程序中有模型，无论是加载别人定义好的 还是 自己引入（from P26_model_save import *）/加上代码 class 模型 -#torch.save(net,"ALexNet_02.path") - -#加载模型 -net_loading=torch.load("ALexNet_02.path") - -#测试数据 -yanzheng(net_loading,test_dataloader) - - - -