刘老师的《Pytorch深度学习实践》第八讲：加载数据集 代码

import numpy as np
import torch
from torch.utils.data import Dataset
from torch.utils.data import DataLoader#DataLoader需要获取DataSet提供的索引[i]和len

class DiabetesDataset(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]])

    def __getitem__(self,index):#支持下标索引
        return self.x_data[index],self.y_data[index]

    def __len__(self):
        return self.len

dataset=DiabetesDataset('diabetes.csv.gz')
train_loader=DataLoader(dataset=dataset,batch_size=32,shuffle=True,num_workers=2)#num_workers=并行的数量

class Model(torch.nn.Module):
    def __init__(self):
        super(Model,self).__init__()
        self.linear1=torch.nn.Linear(8,6)
        self.linear2=torch.nn.Linear(6,4)
        self.linear3=torch.nn.Linear(4,1)
        self.sigmoid=torch.nn.Sigmoid()

    def forward(self,x):
        x=self.sigmoid(self.linear1(x))
        x=self.sigmoid(self.linear2(x))
        x=self.sigmoid(self.linear3(x))
        return x

model=Model()#实例化

criterion=torch.nn.BCELoss(reduction='mean')
optimizer=torch.optim.SGD(model.parameters(),lr=0.01)

if __name__=='__main__':
    #training cycle
    for epoch in range(100):
        #loop over all batches
        for i,data in enumerate(train_loader,0):
            inputs,labels=data#inputs为x,labels为y
            y_pred=model(inputs)
            loss=criterion(y_pred,labels)
            print(epoch,i,loss.item())

            optimizer.zero_grad()
            loss.backward()
            
            optimizer.step()

作业数据链接：Titanic - Machine Learning from Disaster | Kaggle