commit 6306a90ec028ace87d2db795e910b3738b626a56
Author: chengzhiwei <chengzw@qibebt.ac.cn>
Date:   Wed Mar 12 16:49:11 2025 +0800

    上传文件至 /
    
    AlexNet网络
    
    Signed-off-by: chengzhiwei <chengzw@qibebt.ac.cn>

diff --git a/24ALexNet_02.py b/24ALexNet_02.py
new file mode 100644
index 0000000..f22d437
--- /dev/null
+++ b/24ALexNet_02.py
@@ -0,0 +1,115 @@
+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)
+
+
+
+