均方误差MSE

MSE=1N∑i=1N(xi−yi)2MSE=\frac{1}{N}\sum_{i=1}^{N}(x_i-y_i)^2

函数原型

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class torch.nn.MSELoss(size_average=None, reduce=None, reduction='mean')

属性说明

  • reduction
    • = ‘none’：no reduction will be applied.下面的实例中，在loss1中是按照原始维度输出，即对应位置的元素相减然后求平方
    • = ‘mean’（默认）：the sum of the output will be divided by the number of elements in the output.
    • = ‘sum’：the output will be summed.

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.

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a = torch.tensor([[1, 2], 
				  [3, 4]], dtype=torch.float)
				  
b = torch.tensor([[3, 5], 
				  [8, 6]], dtype=torch.float)
 
loss_fn1 = torch.nn.MSELoss(reduction='none')
loss1 = loss_fn1(a.float(), b.float())
print(loss1)   # 输出结果：tensor([[ 4.,  9.],
               #                 [25.,  4.]])
 
loss_fn2 = torch.nn.MSELoss(reduction='sum')
loss2 = loss_fn2(a.float(), b.float())
print(loss2)   # 输出结果：tensor(42.)
 
 
loss_fn3 = torch.nn.MSELoss(reduction='mean')
loss3 = loss_fn3(a.float(), b.float())
print(loss3)   # 输出结果：tensor(10.5000)