CLASS torch.nn.MSELoss(size_average=None, reduce=None, reduction='mean')
Creates a criterion that measures the mean squared error (squared L2 norm) between each element in the input x and target yy.
創建一個准則測量輸入x和目標y每個元素間的平方二范數。
x和y可以是任意形狀的張量，每個張量總的元素個數為n。
Note: size_average and reduce are in the process of being deprecated, and in the meantime, specifying either of those two args will override reduction. Default: 'mean'
size_average和reduce正在被棄用，因此我們只看reduction參數。
reduction='none'
The unreduced (i.e. with reduction set to 'none') loss can be described as:
返回每個元素的平方二范數，shape與輸入相同。
reduction='sum'和reduction='mean'
對所有對應元素的平方二范數求和或求平均（sum / n）。
1 a = torch.tensor([[[1., 2.]], [[1, 2]]]) # [2, 1, 2] 2 b = torch.tensor([[[2., 4.]], [[2, 4.]]]) # [2, 1, 2] 3 4 C = torch.nn.MSELoss(reduction='none') 5 6 loss = C(a, b) 7 8 print(a) 9 print(b) 10 print(loss) 11 12 >>> 13 14 tensor([[[1., 2.]], 15 [[1., 2.]]]) 16 17 tensor([[[2., 4.]], 18 [[2., 4.]]]) 19 20 tensor([[[1., 4.]], 21 [[1., 4.]]]) # 分別計算對應元素的平方二范數 22 23 C = torch.nn.MSELoss(reduction='sum') 24 25 loss = C(a, b) 26 print(loss) 27 28 >>> tensor(10.) # 所有元素平方二范數之和 29 30 C = torch.nn.MSELoss(reduction='mean') 31 32 loss = C(a, b) 33 print(loss) 34 >>> tensor(2.5000) # 所有元素平方二范數的均值