# Confused output from MSE loss

When using the following tensors:

``````prediction = torch.randn(3, 5)
# tensor([[ 1.0732,  1.0451,  0.6778,  1.9992,  0.2216],
#         [-0.1783,  0.7814,  0.3577, -0.8458, -0.1871],
#         [ 0.7581,  1.2827,  0.2930, -1.0155, -1.0184]])

target = torch.randn(3, 5)
# tensor([[-1.1133,  0.7325, -0.8611,  0.3197, -0.0146],
#        [-0.5205,  1.2527,  0.9034,  0.1704, -1.1623],
#        [-0.7035,  2.2432,  1.2100, -1.9930, -0.9532]])
``````

to calculate the MSE loss without reduction I getting as a result:

``````mse_loss = torch.nn.MSELoss(reduction="none")
loss = mse_loss(input, target)
# tensor([[4.7804e+00, 9.7749e-02, 2.3682e+00, 2.8209e+00, 5.5801e-02],
#         [1.1709e-01, 2.2212e-01, 2.9780e-01, 1.0326e+00, 9.5105e-01],
#         [2.1364e+00, 9.2253e-01, 8.4085e-01, 9.5541e-01, 4.2508e-03]])
``````

But in that case, shouldn’t it be a tensor with only three values? Since the the number of elements (N) in target and prediction is equal to 3?

No when you set reduction to none MSELoss only finds the squared difference between each of the targets and predictions. It does not compute the mean so the shape is the same as the shape of the target and predictions.

However, the documentation states:

where N is the batch size and x and y are tensors of arbitrary shapes. Then, with N=3, L should be:
`L={L1, L2, L3}`