RuntimeError: DataLoader worker (pid(s) 5868) exited unexpectedly

RuntimeError: DataLoader worker (pid(s) 5868) exited unexpectedly

报错代码：

import torch
import numpy as np
from torch.utils.data import Dataset
from torch.utils.data import DataLoader

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]])
        # 数据都加载到内存中

    def __getitem__(self, index):
        return self.x_data[index], self.y_data[index]

    def __len__(self):
        return self.len


dataset = DiabetesDataset('iris.csv')
train_loader = DataLoader(dataset=dataset, batch_size=10, shuffle=True, num_workers=1)



class Model(torch.nn.Module):
    def __init__(self):
        super(Model, self).__init__()
        self.linear1 = torch.nn.Linear(4, 2)
        self.linear2 = torch.nn.Linear(2, 1)
        self.sigmoid = torch.nn.Sigmoid()

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

model = Model()


criterion = torch.nn.BCELoss(size_average=True)
optimizer = torch.optim.SGD(model.parameters(), lr=0.1)



for epoch in range(100):
    for i, data in enumerate(train_loader, 0):
        inputs, labels = data
        y_pred = model(inputs)
        loss = criterion(y_pred, labels)

        print(epoch, i, loss.item())

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

修改之后

import torch
import numpy as np
from torch.utils.data import Dataset
from torch.utils.data import DataLoader

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]])
        # 数据都加载到内存中

    def __getitem__(self, index):
        return self.x_data[index], self.y_data[index]

    def __len__(self):
        return self.len


dataset = DiabetesDataset('iris.csv')
train_loader = DataLoader(dataset=dataset, batch_size=10, shuffle=True, num_workers=1)



class Model(torch.nn.Module):
    def __init__(self):
        super(Model, self).__init__()
        self.linear1 = torch.nn.Linear(4, 2)
        self.linear2 = torch.nn.Linear(2, 1)
        self.sigmoid = torch.nn.Sigmoid()

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

model = Model()


criterion = torch.nn.BCELoss(size_average=True)
optimizer = torch.optim.SGD(model.parameters(), lr=0.1)

# 这里变动了一下
if __name__ == '__main__':
    for epoch in range(100):
        for i, data in enumerate(train_loader, 0):
            inputs, labels = data
            y_pred = model(inputs)
            loss = criterion(y_pred, labels)
    
            print(epoch, i, loss.item())
    
            optimizer.zero_grad()
            loss.backward()
            optimizer.step()