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I have 2 tensors with .size of torch.Size([2272, 161]). I want to get mean-squared-error between them. However, I want it along each of the 161 channels, so that my error tensor has a .size of torch.Size([161]). How can I accomplish this?
It seems that torch.nn.MSELoss doesn't let me specify a dimension.
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Mean squared error is computed as the mean of the squared differences between the input and target (predicted and actual) values. To compute the mean squared error in PyTorch, we apply the MSELoss() function provided by the torch. nn module. It creates a criterion that measures the mean squared error.
Mse stands for mean square error which is the most commonly used loss function for regression. The loss is the mean supervised data square difference between true and predicted values. PyTorch MSELoss is a process that measures the average of the square difference between actual value and predicted value.
Mean squared error (MSE) is the most commonly used loss function for regression. The loss is the mean overseen data of the squared differences between true and predicted values, or writing it as a formula.
For the nn.MSELoss you can specify the option reduction='none'. This then gives you back the squared error for each entry position of both of your tensors. Then you can apply torch.sum/torch.mean.
a = torch.randn(2272,161)
b = torch.randn(2272,161)
loss = nn.MSELoss(reduction='none')
loss_result = torch.sum(loss(a,b),dim=0)
I don't think there is a direct way to specify at the initialisation of the loss to which dimension to apply mean/sum. Hope that helps!
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answered Oct 14 '22 01:10
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