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编写步骤

之前已经记录国多次的编写步骤了，无需多言。
（1）准备数据集
这里我们使用MNIST数据集，有官方下载渠道。我们直接使用torchvison里面提供的数据读取功能包就行。如果不使用这个，自己像这样子构建也一样。

# 自己构建数据读取模块
#(1) 数据读取模块
class Mydataset(Dataset):def __init__(self,filepath):xy=np.loadtxt(filepath,delimiter=',',dtype=np.float32)self.len=xy.shape[0]self.x_data=torch.from_numpy(xy[:,:-1])self.y_data=torch.from_numpy(xy[:,[-1]])#魔法方法，容许用户通过索引index得到值def __getitem__(self,index):return self.x_data[index],self.y_data[index]def __len__(self):return self.len

这里直接使用torchvison里面的工具

#准备数据集
batch_size = 64
transforms = transforms.Compose([transforms.ToTensor(),transforms.Normalize((0.1307,),(0.3081,))])trainset = torchvision.datasets.MNIST(root=r'../data/mnist',train=True,download=True,transform=transforms)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size, shuffle=True)testset = torchvision.datasets.MNIST(root=r'../data/mnist',train=False,download=True,transform=transforms)
testloader = torch.utils.data.DataLoader(testset, batch_size=batch_size, shuffle=False)

(2) 构建模型
这次我们使用不带dropout的全连接模型

# 定义模型
class Net(nn.Module):def __init__(self):super(Net, self).__init__()self.linear1 = nn.Linear(784, 100)self.linear2 = nn.Linear(100, 20)self.linear3 = nn.Linear(20, 10)def forward(self, x):x=x.view(x.size(0), -1)x = F.relu(self.linear1(x))x = F.relu(self.linear2(x))x = self.linear3(x)return x

(3) 选择损失和优化器

# 构建模型和损失
model=Net()
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=0.001, momentum=0.9)

（4）训练模型

def train(epoch):running_loss = 0.0for batch_idx, (inputs, targets) in enumerate(trainloader):optimizer.zero_grad()outputs = model(inputs)loss = criterion(outputs, targets)loss.backward()optimizer.step()#需要将张量转换为浮点数运算running_loss += loss.item()if batch_idx % 100 == 0:print('Train Epoch: {}, Loss: {:.6f}'.format(epoch, loss.item()))running_loss = 0

（5）测试模型

def test(epoch):correct = 0total = 0with torch.no_grad():for batch_idx, (inputs, targets) in enumerate(testloader):outputs = model(inputs)_, predicted = torch.max(outputs.data, 1)total += targets.size(0)correct=correct+(predicted.eq(targets).sum()*1.0)print('Accuracy of the network on the 10000 test images: %d %%' % (100*correct/total))

全部代码

import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision
import torchvision.transforms as transforms
import torch.optim as optim
#准备数据集
batch_size = 64
transforms = transforms.Compose([transforms.ToTensor(),transforms.Normalize((0.1307,),(0.3081,))])trainset = torchvision.datasets.MNIST(root=r'../data/mnist',train=True,download=True,transform=transforms)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size, shuffle=True)testset = torchvision.datasets.MNIST(root=r'../data/mnist',train=False,download=True,transform=transforms)
testloader = torch.utils.data.DataLoader(testset, batch_size=batch_size, shuffle=False)# 定义模型
class Net(nn.Module):def __init__(self):super(Net, self).__init__()self.linear1 = nn.Linear(784, 100)self.linear2 = nn.Linear(100, 20)self.linear3 = nn.Linear(20, 10)def forward(self, x):x=x.view(x.size(0), -1)x = F.relu(self.linear1(x))x = F.relu(self.linear2(x))x = self.linear3(x)return x
# 构建模型和损失
model=Net()
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=0.001, momentum=0.9)def train(epoch):running_loss = 0.0for batch_idx, (inputs, targets) in enumerate(trainloader):optimizer.zero_grad()outputs = model(inputs)loss = criterion(outputs, targets)loss.backward()optimizer.step()#需要将张量转换为浮点数运算running_loss += loss.item()if batch_idx % 100 == 0:print('Train Epoch: {}, Loss: {:.6f}'.format(epoch, loss.item()))running_loss = 0
def test(epoch):correct = 0total = 0with torch.no_grad():for batch_idx, (inputs, targets) in enumerate(testloader):outputs = model(inputs)_, predicted = torch.max(outputs.data, 1)total += targets.size(0)correct=correct+(predicted.eq(targets).sum()*1.0)print('Accuracy of the network on the 10000 test images: %d %%' % (100*correct/total))
if __name__ == '__main__':for epoch in range(10):train(epoch)test(epoch)