主要用到了Pytorch中的Dataset和DataLoadder這兩個方法，其中Dataset是抽象類，不能實(shí)例化對象，只能繼承用于構(gòu)造數(shù)據(jù)集，DataLoader是幫助加載數(shù)據(jù)的，可以做shuffle、batch_size，能拿Mini-batch進(jìn)行訓(xùn)練。
代碼如下：

import torch import numpy as np from torch.utils.data import Dataset from torch.utils.data import DataLoaderclass 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):return self.x_data[index],self.y_data[index]def __len__(self):return self.lendataset = DiabetesDataset('E:\\tmp\\.keras\\datasets\\diabetes.csv\\diabetes.csv') train_loader = DataLoader(dataset=dataset,batch_size = 32,shuffle = True,num_workers=0 )#定義模型 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.relu = torch.nn.ReLU()self.sigmoid = torch.nn.Sigmoid()def forward(self,x):x = self.relu(self.linear1(x))x = self.relu(self.linear2(x))x = self.sigmoid(self.linear3(x))return x model = Model()#構(gòu)建優(yōu)化器和損失函數(shù) criterion = torch.nn.BCELoss(reduction='mean') optimizer = torch.optim.SGD(model.parameters(),lr=0.01) #訓(xùn)練 if __name__ == '__main__':for epoch in range(1000):for i,data in enumerate(train_loader,0):inputs,labels = datay_pred = model(inputs)loss=criterion(y_pred,labels)print(epoch,i,loss.item())optimizer.zero_grad()loss.backward()optimizer.step()

努力加油a啊

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