运行的是这一段代码，spyder老报错
RuntimeError: DataLoader worker (pid(s) 1004, 4680) exited unexpectedly
奇怪的是，同样的代码我在jupyter notebook里就能正常运行。
请问该如何解决
import torch
import torch.utils.data as Data
import torch.nn.functional as F
from torch.autograd import Variable
import matplotlib.pyplot as plt
torch.manual_seed(1) # 设定随机数种子
# 定义超参数
LR = 0.01 # 学习率
BATCH_SIZE = 32 # 批大小
EPOCH = 12 # 迭代次数
x = torch.unsqueeze(torch.linspace(-1, 1, 1000), dim=1)
y = x.pow(2) + 0.1*torch.normal(torch.zeros(*x.size()))
#plt.scatter(x.numpy(), y.numpy())
#plt.show()
# 将数据转换为torch的dataset格式
torch_dataset = Data.TensorDataset(x, y)
# 将torch_dataset置入Dataloader中
loader = Data.DataLoader(dataset=torch_dataset, batch_size=BATCH_SIZE,
shuffle=True, num_workers=2)
class Net(torch.nn.Module):
def __init__(self):
super(Net, self).__init__()
self.hidden = torch.nn.Linear(1, 20)
self.predict = torch.nn.Linear(20, 1)
def forward(self, x):
x = F.relu(self.hidden(x))
x = self.predict(x)
return x
# 为每个优化器创建一个Net
net_SGD = Net()
net_Momentum = Net()
net_RMSprop = Net()
net_Adam = Net()
nets = [net_SGD, net_Momentum, net_RMSprop, net_Adam]
# 初始化优化器
opt_SGD = torch.optim.SGD(net_SGD.parameters(), lr=LR)
opt_Momentum = torch.optim.SGD(net_Momentum.parameters(), lr=LR, momentum=0.8)
opt_RMSprop = torch.optim.RMSprop(net_RMSprop.parameters(), lr=LR, alpha=0.9)
opt_Adam = torch.optim.Adam(net_Adam.parameters(), lr=LR, betas=(0.9, 0.99))
optimizers = [opt_SGD, opt_Momentum, opt_RMSprop, opt_Adam]
# 定义损失函数
loss_function = torch.nn.MSELoss()
losses_history = [[], [], [], []] # 记录training时不同神经网络的loss值
for epoch in range(EPOCH):
print('Epoch:', epoch + 1, 'Training...')
for step, (batch_x, batch_y) in enumerate(loader):
b_x = Variable(batch_x)
b_y = Variable(batch_y)
for net, opt, l_his in zip(nets, optimizers, losses_history):
output = net(b_x)
loss = loss_function(output, b_y)
opt.zero_grad()
loss.backward()
opt.step()
l_his.append(loss.item())
labels = ['SGD', 'Momentum', 'RMSprop', 'Adam']
for i, l_his in enumerate(losses_history):
plt.plot(l_his, label=labels[i])
plt.legend(loc='best')
plt.xlabel('Steps')
plt.ylabel('Loss')
plt.ylim((0, 0.2))
plt.show()
