pytorch训练模型报错NotImplementedErrorTraceback

问题遇到的现象和发生背景

在学习pytorch训练回归模型时出现报错

问题相关代码，请勿粘贴截图

NotImplementedError                       Traceback (most recent call last)
Input In [86], in <cell line: 26>()
     29 labels = torch.from_numpy(y_train).to(device)
     31 optimizer.zero_grad()
---> 33 outputs = model(inputs)
     35 loss = criterion(outputs, labels)
     37 loss.backward()

File C:\Users\anaconda\envs\woniu\lib\site-packages\torch\nn\modules\module.py:1110, in Module._call_impl(self, *input, **kwargs)
   1106 # If we don't have any hooks, we want to skip the rest of the logic in
   1107 # this function, and just call forward.
   1108 if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
   1109         or _global_forward_hooks or _global_forward_pre_hooks):
-> 1110     return forward_call(*input, **kwargs)
   1111 # Do not call functions when jit is used
   1112 full_backward_hooks, non_full_backward_hooks = [], []

File C:\Users\anaconda\envs\woniu\lib\site-packages\torch\nn\modules\module.py:201, in _forward_unimplemented(self, *input)
    190 def _forward_unimplemented(self, *input: Any) -> None:
    191     r"""Defines the computation performed at every call.
    192 
    193     Should be overridden by all subclasses.
   (...)
    199         registered hooks while the latter silently ignores them.
    200     """
--> 201     raise NotImplementedError

NotImplementedError: 
#woniu是我组建的虚拟环境

运行结果及报错内容

import torch
import torch.nn as nn
import numpy as np

x_values = [i for i in range(11)]                
x_train = np.array(x_values , dtype = np.float32) 
x_train = x_train.reshape(-1, 1)                  
x_train.shape    

y_values = [2*i+1 for i in x_values]              
y_train = np.array(y_values , dtype = np.float32) 
y_train = y_train.reshape(-1, 1)  
y_train.shape   

class LinearRegressionModel(nn.Module):
    def __init__(self, input_dim, output_dim):
        super(LinearRegressionModel, self).__init__()
        self.Linear = nn.Linear(input_dim, output_dim)        
        
def forward(self, x):                              
    out = self.linear(x)
    return out

input_dim = 1
output_dim = 1
model = LinearRegressionModel(input_dim, output_dim)        

device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model.to(device)

criterion = nn.MSELoss()

learning_rate = 0.01
optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)

epochs = 1000 
for epoch in range(epochs):
    epoch += 1
    inputs = torch.from_numpy(x_train).to(device)
    labels = torch.from_numpy(y_train).to(device)

    optimizer.zero_grad()

    outputs = model(inputs)

    loss = criterion(outputs, labels)

    loss.backward()

    optimizer.step()
    if epoch%50 == 0:
        print('epoch{},loss{}'.format(epoch, loss.item())) 

我的解答思路和尝试过的方法

没有头绪，在学习视频中别人可以训练成功

我想要达到的结果

成功执行

缩进不对，导致你的class继承nn.moudle()类的时候forward函数没了，而这个函数是继承nn.Module的时候必须实现的接口函数，所以就会报这个错误

好像还是数据的问题

你的报错信息呢？