机器学习之卷积神经网络Lenet5训练模型

Lenet5训练模型

下载数据集

可以提前下载也可以在线下载

train_data = torchvision.datasets.MNIST(root='./',download=True,train=True,transform=transform)test_data = torchvision.datasets.MNIST(root='./',download=True,train=False,transform=transform)

训练模型

import torchimport torchvisionclass Lenet5(torch.nn.Module):    def __init__(self):        super(Lenet5, self).__init__()        self.model = torch.nn.Sequential(            torch.nn.Conv2d(in_channels=1,out_channels=6,kernel_size=5), # 1*32*32 # 6*28*28            torch.nn.ReLU(),            torch.nn.MaxPool2d(kernel_size=2,  # 6*14*14                                stride=2),            torch.nn.Conv2d(in_channels=6,                                    out_channels=16,                                    kernel_size=5),  # 16 *10*10            torch.nn.ReLU(),            torch.nn.MaxPool2d(kernel_size=2,                                stride=2),  # 16*5*5            torch.nn.Conv2d(in_channels=16,                                    out_channels=120,                                    kernel_size=5),  # 120*1*1            torch.nn.ReLU(),            torch.nn.Flatten(),  # 展平 成120*1维            torch.nn.Linear(120, 84),            torch.nn.Linear(84, 10)        )    def forward(self,x):        x = self.model(x)        return xtransform = torchvision.transforms.Compose(    [torchvision.transforms.Resize(32),     torchvision.transforms.ToTensor()])train_data = torchvision.datasets.MNIST(root='./',download=True,train=True,transform=transform)test_data = torchvision.datasets.MNIST(root='./',download=True,train=False,transform=transform)#分批次加载数据 64 128train_loader =torch.utils.data.DataLoader(train_data,batch_size=64,shuffle=True)test_loader =torch.utils.data.DataLoader(test_data,batch_size=64,shuffle=True)#gpudevice = torch.device('cuda' if torch.cuda.is_available() else 'cpu')net = Lenet5().to(device)loss_func = torch.nn.CrossEntropyLoss().to(device)optim = torch.optim.Adam(net.parameters(),lr=0.001)net.train()for epoch in range(10):    for step,(x,y) in enumerate(train_loader):        x = x.to(device)        y = y.to(device)        ouput = net(x)        loss = loss_func(ouput,y)  #计算损失        optim.zero_grad()        loss.backward()        optim.step()    print('epoch:',epoch,"loss:",loss)torch.save(net,'net.pkl')

import torchimport torchvisionclass Lenet5(torch.nn.Module):    def __init__(self):        super(Lenet5, self).__init__()        self.model = torch.nn.Sequential(            torch.nn.Conv2d(in_channels=1,out_channels=6,kernel_size=5), # 1*32*32 # 6*28*28            torch.nn.ReLU(),            torch.nn.MaxPool2d(kernel_size=2,  # 6*14*14                                stride=2),            torch.nn.Conv2d(in_channels=6,                                    out_channels=16,                                    kernel_size=5),  # 16 *10*10            torch.nn.ReLU(),            torch.nn.MaxPool2d(kernel_size=2,                                stride=2),  # 16*5*5            torch.nn.Conv2d(in_channels=16,                                    out_channels=120,                                    kernel_size=5),  # 120*1*1            torch.nn.ReLU(),            torch.nn.Flatten(),  # 展平 成120*1维            torch.nn.Linear(120, 84),            torch.nn.Linear(84, 10)        )    def forward(self,x):        x = self.model(x)        return xtransform = torchvision.transforms.Compose(    [torchvision.transforms.Resize(32),     torchvision.transforms.ToTensor()])test_data = torchvision.datasets.MNIST(root='./',download=False,train=False,transform=transform)test_loader =torch.utils.data.DataLoader(test_data,batch_size=64,shuffle=True)device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')net = torch.load('net.pkl')net.eval()  #表明进行推理with torch.no_grad():    for step,(x,y) in enumerate(test_loader):        x,y = x.to(device),y.to(device)        pre = net(x)        print(pre)        pre_y = torch.max(pre.cpu(),1)[1].numpy()        print(pre_y)        y = y.cpu().numpy()        acc = (pre_y == y).sum()/len(y)    print("accu:",acc)