pytorch-08. Load dataset

import torchfrom torch.utils.data import Dataset # Dataset abstract class, cannot be instantiated, can only be inheritedfrom torch.utils.data import DataLoader # DataLoader can be instantiatedimport numpy as npclass 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 the length of the datasetreturn self.lendataset = DiabetesDataset('diabetes.csv.gz')train_loader = DataLoader(dataset=dataset,batch_size=32,shuffle=True,num_workers=2) #num_workers read data in parallelclass 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()self.activate = torch.nn.ReLU()def forward(self,x):x = self.activate(self.linear1(x))x = self.activate(self.linear2(x))x = self.sigmoid(self.linear3(x)) #RELU, the y value when x is less than 0 is 0, and ln0 may appear when calculating the lossreturn xmodel = Model()criterion = torch.nn.BCELoss(size_average=True)optimizer = torch.optim.SGD(model.parameters(),lr=0.1)'''Enumerate is mostly used to get the count in the for loop. It can be used to obtain the index and value at the same time, that is, when the index and value values ​​are needed, enumerate can be used'''if __name__ == '__main__':for epoch in range(100):for i,data in enumerate(train_loader,0): #0 means start from the specified index 0print('train_loader:',train_loader)# 1 Prepare datainputs,labels = data# 2 Forwardy_pred = model(inputs)loss = criterion(y_pred, labels)print(epoch,i,loss.item())#3 Backwardoptimizer.zero_grad()loss.backward()#4 Updateoptimizer.step()