bug一堆

images.py

@@ -1,30 +0,0 @@
-import os 
-import requests
-import re
-import time
-
-def image_set(save_path,word,epoch):
-    q=0
-    a=0
-    while True:
-        time.sleep(1)
-        url="https://images.baidu.com/search/index?tn=baiduimage&ipn=r&ct=201326592&cl=2&lm=-1&st=-1&fm=index&fr=&hs=0&xthttps=111110&sf=1&fmq=&pv=&ic=0&nc=1&z=&se=1&showtab=0&fb=0&width=&height=&face=0&istype=2&ie=utf-8&word={}&oq={}&rsp=-1".format(word,word)
-        headers={"User-Agent":"Mozilla/5.0 (Windows NT 6.1; WOW64; Trident/7.0; SLCC2; .NET CLR 2.0.50727; .NET CLR 3.5.30729; .NET CLR 3.0.30729; .NET4.0C; .NET4.0E; rv:11.0) like Gecko"}
-        response=requests.get(url,headers)
-        html=response.text
-        urls=re.findall('"objURL":"(.*?)"',html)
-        for url in urls:
-            print(a)
-            response = requests.get(url,headers=headers)
-            image = response.content
-            with open(os.path.join(save_path,"{}.jpg".format(a))) as f:
-                f.write(image)
-            a=a+1
-        q=q+20
-        if(q/20)>=int(epoch):
-            break
-if __name__=="__main__":
-    save_path = "/kemna"
-    word=input('输入你要的图片')
-    epoch=input('下载几轮图片？')
-    image_set(save_path,word,epoch)

main.py

@@ -1,107 +1,122 @@
 #导入模块
 import torch 
-import torch.utils
-import torch.utils.data
-import torch.utils.data.dataloader
 import torchvision
-from tqdm import tqdm
+import random
-import matplotlib
+from enum import Enum
-
+import matplotlib as plt 
-#进行数据预处理
+import torch.nn.functional as F
-#初始化存储训练和测试损失与准确率的字典
-history = {
-    'Train Loss': [],
-    'Train Accuracy': [],
-    'Test Loss': [],
-    'Test Accuracy': []
-}
-#将训练设备设置为GPU
 device = 'cuda:0' if torch.cuda.is_available() else 'cpu'
 #使用torchvisiom.transform将图片转换为张量
+
 transform =torchvision.transforms.Compose([torchvision.transforms.ToTensor(),torchvision.transforms.Normalize(mean=[0.5],std=[0.5])])
 #构建数据集（使用MINIST）
 path ='./MNIST'
+
+EPOCH =10
+Batch_Size = 64
+#创建dataset
+class DataSet:
+    def __init__(self,floder,data_name,label_name,transform=None):
+        self.floder = floder
+        self.transform = transform
+#创建dataloader
+class DataLoader:
+    def __init__(self,type,batch_size,is_shuffle):
+        data_name = 'train-images-idx3-ubyte.gz' if type=='train' else 't10k-images-idx3-ubyte.gz'
+        label_name = 'train-labels-idx1-ubyte.gz' if type=='train' else 't10k-labels-idx1-ubyte.gz'
+
 #下载数据集
 #下载训练集
 trainData=torchvision.datasets.MNIST(path,train=True,transform=transform,download=True)
 #下载测试集
 testData=torchvision.datasets.MNIST(path,train=False,transform=transform,download=False)
-#使用dataloader方法开始训练
+#shuffle=true是用来打乱数据集的
-#设定batch大小
+train_Dataloader = DataLoader(train,batch_size = Batch_Size,shuffle = True)
-BATCH_SIZE=1000
+test_DataLoader = DataLoader(test,batch_size=Batch_Size,shuffle=False)
-#构建dataloader
-TrainDataLoader = torch.utils.data.DataLoader(dataset = trainData,batch_size=BATCH_SIZE)
-TestDataLoader = torch.utils.data.DataLoader(dataset=testData,batch_size=BATCH_SIZE)
-#构建神经网络
 class Net(torch.nn.Module):
     #构造函数
     def __init__(self):
         #继承父类
         super(Net,self).__init__()
-        self.model = torch.nn.Sequential(
+        self.conv1 = torch.nn.Sequential(
-            torch.nn.Conv2d(in_channels=1,out_channels=16,kernel_size=3,stride=1,padding=1),
+            torch.nn.Conv2d(1, 10, kernel_size=5),
             torch.nn.ReLU(),
-            torch.nn.MaxPool2d(kernel_size = 2,stride = 2),
+            torch.nn.MaxPool2d(kernel_size=2),
-            
-            #The size of the picture is 14x14
-            torch.nn.Conv2d(in_channels = 16,out_channels = 32,kernel_size = 3,stride = 1,padding = 1),
-            torch.nn.ReLU(),
-            torch.nn.MaxPool2d(kernel_size = 2,stride = 2),
-            
-            #The size of the picture is 7x7
-            torch.nn.Conv2d(in_channels = 32,out_channels = 64,kernel_size = 3,stride = 1,padding = 1),
-            torch.nn.ReLU(),
-            
-            torch.nn.Flatten(),
-            torch.nn.Linear(in_features = 7 * 7 * 64,out_features = 128),
-            torch.nn.ReLU(),
-            torch.nn.Linear(in_features = 128,out_features = 10),
-            torch.nn.Softmax(dim=1)
         )
+        self.conv2 = torch.nn.Sequential(
+            torch.nn.Conv2d(10, 20, kernel_size=5),
+            torch.nn.ReLU(),
+            torch.nn.MaxPool2d(kernel_size=2),
+        )
+        self.fc = torch.nn.Sequential(
+            torch.nn.Linear(320, 50),
+            torch.nn.Linear(50, 10),
+        )
+    def forward(self,x):
+        batch_size = x.size(0)
+        x = self.conv1(x)  # 一层卷积层,一层池化层,一层激活层(图是先卷积后激活再池化，差别不大)
+        x = self.conv2(x)  # 再来一次
+        x = x.view(batch_size, -1)  # flatten 变成全连接网络需要的输入 (batch, 20,4,4) ==> (batch,320), -1 此处自动算出的是320
+        x = self.fc(x)
+        return x
 
-    def forward(self,input):
+
-        output = self.model(input)
+model = Net().to(device)
-        return output
+#使用交叉墒损失做损失函数
-net = Net().to(device)
+sunshi = torch.nn.CrossEntropyLoss()
-#构建迭代器与损失函数
+#优化器：随机梯度下降
-#对于简单的多分类任务，我们可以使用交叉熵损失来作为损失函数；而对于迭代器而言，我们可以使用Adam迭代器
+#lr=学习率，momentum = 冲量
-lossF = torch.nn.CrossEntropyLoss()
+optimizer = torch.optim.SGD(model.parameters(),lr=0.25,momentum=0.25)
-optimizer = torch.optim.Adam(net.parameters())
+#训练
-#循环训练
+def train(epoch):
-Epochs=100
+    running_loss = 0.0
-for epochs in range(0,Epochs):
+    running_total=0
-    #训练内容
+    running_correct = 0
-    processBar = tqdm(TrainDataLoader,unit = 'step')
+    for batch_idx,data in enumerate(train_Dataloader,0):
-    net.train(True)
+        inputs, target = data
-    for step,(trainImgs,labels) in enumerate(processBar):
+        inputs = inputs.to(device)
-        trainImgs = trainImgs.to(device)
+        target = target.to(device)
-        labels = labels.to(device)
+        #梯度归零
-        net.zero_grad()
+        optimizer.zero_grad()
-        outputs = net(trainImgs)
+
-        loss = lossF(outputs,labels)
+        outputs = model(inputs)
-        predictions = torch.argmax(outputs, dim = 1)
+        loss = sunshi(outputs,target)
-        accuracy = torch.sum(predictions == labels)/labels.shape[0]
+        #反向传播
         loss.backward()
         optimizer.step()
-        processBar.set_description("[%d/%d] Loss: %.4f, Acc: %.4f" % 
-                                   (epochs,Epochs,loss.item(),accuracy.item()))
 
-        if step == len(processBar)-1:
+        running_loss += loss.item()
-            correct,totalLoss = 0,0
+        #准确率
-            net.train(False)
+        _,predicted = torch.max(outputs,dim=1)
-            for testImgs,labels in TestDataLoader:
+        running_total+=inputs.shape[0]
-                testImgs = testImgs.to(device)
+        running_correct += (predicted == target).sum().item()
-                labels = labels.to(device)
+        print('[%d,%5d]:loss:%.3f,acc:%.2f',epoch+1,batch_idx+1,running_loss,running_correct/running_total)
-                outputs = net(testImgs)
+#测试
-                loss = lossF(outputs,labels)
+def test():
-                predictions = torch.argmax(outputs,dim = 1)
+    correct =0
+    total = 0
+    with torch.no_grad():
+        for data in test_DataLoader:
+            images,labels = data
+            outputs = model(images)
+            predicted = torch.max(outputs.data,dim=1)
+            total += labels.size(0)
+            correct +=(predicted == labels).sum().item()
+    accuracy = correct/total
+    print('[%d/%d]Accuracy: %.lf %%', epoch+1,EPOCH,accuracy)
+    return accuracy
 
-                totalLoss += loss
+
-                correct += torch.sum(predictions == labels)
+
-            testAccuracy = correct/(BATCH_SIZE * len(TestDataLoader))
+if __name__ =='__main__':
-            testLoss = totalLoss/len(TestDataLoader)
+    acc_list_test =[]
-            history['Test Loss'].append(testLoss.item())
+    for epoch in range(EPOCH):
-            history['Test Accuracy'].append(testAccuracy.item())
+        train(epoch)
-            processBar.set_description("[%d/%d] Loss: %.4f, Acc: %.4f, Test Loss: %.4f, Test Acc: %.4f" % 
+        #每训练10轮测试一次
-                                   (epochs,Epochs,loss.item(),accuracy.item(),testLoss.item(),testAccuracy.item()))
+        if epoch % 10 ==9:
-    processBar.close()
+            acc_test = test()
+            acc_list_test.append(acc_test)
+    plt.plot(acc_list_test)
+    plt.xlabel('Epoch')
+    plt.ylabel('Accuracy On TestSet')
+    plt.show()

test.py

@@ -1,103 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import torch.optim as optim
-from torchvision import datasets, transforms
-from torch.utils.data import DataLoader
-
-# 定义超参数
-BATCH_SIZE =64  # 每批处理的数据
-DEVICE = torch.device('cpu')  # 用 cpu 还是 gpu
-EPOCHS = 300  # 训练次数
-
-# 构建 pipeline，对图像做处理
-pipeline = transforms.Compose([
-    transforms.ToTensor(),  # 将图片转化成 tensor
-    transforms.Normalize((0.1307,), (0.3081,))  # 正则化，降低模型复杂度
-])
-
-# 下载数据集
-train_set = datasets.MNIST('data', train=True, download=True, transform=pipeline)
-test_set = datasets.MNIST('data', train=False, download=False, transform=pipeline)
-# 加载数据
-train_loader = DataLoader(train_set, batch_size=BATCH_SIZE, shuffle=True)
-test_loader = DataLoader(test_set, batch_size=BATCH_SIZE, shuffle=True)
-
-# 构建模型
-class Dight(nn.Module):
-    def __init__(self):
-        super().__init__()
-        self.conv1 = nn.Conv2d(1, 10, 5)
-        self.conv2 = nn.Conv2d(10, 20, 3)
-        self.fc1 = nn.Linear(20 * 10 * 10, 500)
-        self.fc2 = nn.Linear(500, 10)
-
-    def forward(self, x):
-        input_size = x.size(0)  # batch_size   ×1×28×28
-        x = self.conv1(x)  # s\输出 batch*1*28*28,输出×10×24×24(28-5+1=24)
-        x = F.relu(x)  # 保持 shape 不变
-        x = F.max_pool2d(x, 2, 2)  # 输入：10×24×24  输出：batch×10×12×12
-        x = self.conv2(x)  # 10*12*12  20*10*10
-        x = F.relu(x)
-        x = x.view(input_size, -1)  # 拉平，自动计算维度 20×10×10=2000
-        x = self.fc1(x)  # 2000 500
-        x = F.relu(x)
-        x = self.fc2(x)
-        output = F.log_softmax(x, dim=1)  # 数字概率找最大的
-        return output
-
-# 定义优化器
-model = Dight().to(DEVICE)
-optimizer = optim.Adam(model.parameters())
-
-# 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 GPU
-        data = data.to(device)
-        target = target.to(device)
-        # 梯度初始化为 0
-        optimizer.zero_grad()
-        # 预测结果
-        output = model(data)
-        # 计算损失
-        loss = F.cross_entropy(output, target)
-        # 损失函数
-        loss.backward()
-        # 反向传播
-        optimizer.step()
-        # 参数优化
-        optimizer.step()
-        if batch_index % 3000 == 0:
-            print('Traib Epoch : {} \t Loss : {:.6f}'.format(epoch,loss.item()))
-# 定义测试方法
-def test_model(model, device, test_loader):
-    # 模型验证
-    model.eval()
-    # 正确率
-    correct = 0.0
-    # 测试损失
-    test_loss = 0.0
-    with torch.no_grad():
-        for data, target in test_loader:
-            # 部署到 device 上
-            data = data.to(device)
-            target = target.to(device)
-            # 测试数据
-            output = model(data)
-            # 计算测试损失
-            test_loss += F.cross_entropy(output, target).item()
-            # 获取预测结果
-            _, pred = output.max(1, keepdim=True)
-            # 累计正确率
-            correct += (pred == target.view_as(pred)).sum().item()
-    test_loss /= len(test_loader.dataset)
-    print('Test - Average loss : (:.4f),Accuracy:{:.3f}\n'.format(
-        test_loss, 100.0 * correct / len(test_loader.dataset)))
-
-# 调用，输出
-for epoch in range(1, EPOCHS + 1):
-    train_model(model, DEVICE, train_loader, optimizer, epoch)
-    test_model(model, DEVICE, test_loader)