pytorch训练模型时，因为broadcast机制踩的坑：loss无法收敛

在训练一个非常简单的拟合曲线的模型时，遇到下面的报错：

C:/Users/user/Desktop/test/test.py:58: UserWarning: Using a target size (torch.Size([30000])) that is different to the input size (torch.Size([30000, 1])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.
  loss = F.mse_loss(out, yt)

mse_loss的两个输入参数input和target维度不一样，一个是torch.Size([30000, 1]，一个是torch.Size([30000]，这两个维度在进行计算时都会将维度广播成torch.Size([30000, 30000]，这个时候就出问题了：

mse_loss函数最终输出值是(target-input)每个元素数字平方和除以width x height，也就是在batch和特征维度上都做了平均。但这两个维度在进行广播之后tensor会变成下面这样：

# 原tensor：yt
yt tensor([ 96.6252, -83.4613,  -1.6751,  ...,   1.8656, -15.8007, -30.5789]) torch.Size([30000])

# 广播之后：yt
yt tensor([[ 96.6252, -83.4613,  -1.6751,  ...,   1.8656, -15.8007, -30.5789],
        [ 96.6252, -83.4613,  -1.6751,  ...,   1.8656, -15.8007, -30.5789],
        [ 96.6252, -83.4613,  -1.6751,  ...,   1.8656, -15.8007, -30.5789],
        ...,
        [ 96.6252, -83.4613,  -1.6751,  ...,   1.8656, -15.8007, -30.5789],
        [ 96.6252, -83.4613,  -1.6751,  ...,   1.8656, -15.8007, -30.5789],
        [ 96.6252, -83.4613,  -1.6751,  ...,   1.8656, -15.8007, -30.5789]])

###############################################################################

# 原tensor：out
out tensor([[62.2171],
        [34.9442],
        [92.2927],
        ...,
        [16.6877],
        [35.8723],
        [60.5973]], grad_fn=<MmBackward>) torch.Size([30000, 1])

# 广播之后：out
out tensor([[62.2171, 62.2171, 62.2171,  ..., 62.2171, 62.2171, 62.2171],
        [34.9442, 34.9442, 34.9442,  ..., 34.9442, 34.9442, 34.9442],
        [92.2927, 92.2927, 92.2927,  ..., 92.2927, 92.2927, 92.2927],
        ...,
        [16.6877, 16.6877, 16.6877,  ..., 16.6877, 16.6877, 16.6877],
        [35.8723, 35.8723, 35.8723,  ..., 35.8723, 35.8723, 35.8723],
        [60.5973, 60.5973, 60.5973,  ..., 60.5973, 60.5973, 60.5973]],
       grad_fn=<ExpandBackward>)

这样在进行逐元素计算时，结果就和预想的不一样了，计算出来的loss就发生了变化

标签：loss,60.5973,30000,...,torch,broadcast,pytorch,16.6877,96.6252
来源： https://blog.csdn.net/weixin_42149550/article/details/117373799