在 Torch 中进行线性回归结果产生 NaN 错误

我是一个 Torch 的新手。最近，我尝试使用 Torch 进行多元线性回归。但是错误总是会产生无穷大和 NaN。

对于前两个错误，显然是增加的。 以下是我的代码。

dataset=
124.0000   81.6900   64.5000  118.0000
 150.0000  103.8400   73.3000  143.0000
   ...
 137.0000   94.9600   67.0000  191.0000
 110.0000   99.7900   75.5000  192.0000
   ...
  94.0000   89.4000   64.5000  139.0000
  74.0000   93.0000   74.0000  148.0000
  89.0000   93.5900   75.5000  179.0000
linLayer = nn.Linear(3,1)
model = nn.Sequential()
model:add(linLayer)
criterion = nn.MSECriterion()

feval = function(x_new)
    if x ~= x_new then
      x:copy(x_new)
   end
   _nidx_ = (_nidx_ or 0) + 1
   if _nidx_ > (#dataset_inputs)[1] then _nidx_ = 1 end

   local sample = dataset[_nidx_]
   local inputs = sample[{ {2,4} }]
   local target = sample[{ {1} }]

   dl_dx:zero()

   local loss_x = criterion:forward(model:forward(inputs),target)
   model:backward(inputs, criterion:backward(model.output,target))

   -- 返回损失（x）和 dloss/dx
   return loss_x, dl_dx
end

sgd_params = {
   learningRate = 1e-3,
   learningRateDecay = 1e-4,
   weightDecay = 0,
   momentum = 0
}
epochs = 100

  for i = 1,epochs do
        current_loss = 0
        for i = 1,(#dataset_inputs)[1] do

            _,fs = optim.sgd(feval,x,sgd_params)

            current_loss = current_loss + fs[1]
        end
        current_loss = current_loss / (#dataset_inputs)[1]
        print('epoch = ' .. i ..
         ' of ' .. epochs ..
         ' current loss = ' .. current_loss)
    end

结果如下：
epoch = 1 of 100 current loss = 8.1958765768632e+138
epoch = 2 of 100 current loss = 5.0759297005752e+278
epoch = 3 of 100 current loss = inf
epoch = 4 of 100 current loss = inf
epoch = 5 of 100 current loss = nan
... ...
epoch = 97 of 100 current loss = nan
epoch = 98 of 100 current loss = nan
epoch = 99 of 100 current loss = nan
epoch = 100 of 100 current loss = nan

这个问题应该怎么解决？我用同样的方法训练逻辑回归。结果似乎比这个好。但还是不够好。 有什么问题吗？非常感谢。