torch MSELoss

*CLASS* torch.nn.MSELoss(size_average=None, reduce=None, reduction=mean)

torch.nn.functional.mse_loss(input, target, size_average=None, reduce=None, reduction=mean) → Tensor

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参数

• size_average: 默认为True, 计算一个batch中所有loss的均值；reduce为 False时，忽略这个参数；
• reduce： 默认为True, 计算一个batch中所有loss的均值或者和；
  • reduce = False，size_average 参数失效，返回的 loss是向量，维度为 (batch_size, ) ；
  • reduce = True，size_average 参数失效，返回的 loss是标量；
    • size_average = True，返回 loss.mean();
    • size_average = False，返回 loss.sum();
• reduction ：'none' | 'mean' | 'sum'，默认均值；指定size_average 和reduce参数就不使用reduction ，与之相反。

输入

"mse_cpu" not implemented for 'Int'

• Input: (N,∗) where *∗ means, any number of additional dimensions；input.float()
• Target: (N,∗) , same shape as the input；Target.float()

例子

loss = nn.MSELoss(reduce=False, size_average=False)
input = torch.randn(3, 5)
target = torch.randn(3, 5)
output = loss(input.float(), target.float())
print(output)

tensor([[1.2459e+01, 5.8741e-02, 1.8397e-01, 4.9688e-01, 7.3362e-02],
        [8.0921e-01, 1.8580e+00, 4.5180e+00, 7.5342e-01, 4.1929e-01],
        [2.6371e-02, 1.5204e+00, 1.5778e+00, 1.1634e+00, 9.5338e-03]])

loss = nn.MSELoss(reduce = True, size_average=True)
input = torch.randn(3, 5)
target = torch.randn(3, 5)
output = loss(input, target)
print(output)

tensor(1.2368)

参考

TORCH.NN
torch.nn.functional