K x = L x − L 0 L 0 (2) with L 0 being the loss (here the mean squared error for regressions and the categorical cross entropy for the classification) for the full test data set, whereas L x is the loss for the test data set when setting the xth input to zero. When drop...
In this case, the R-squared (R2) reached is greater than 0.999. These parameters determine the signal named "Fitted signal" or "Best-fit". It is important to highlight the fact that this fitting operation cannot be performed in real-time. Despite being not appropriate for real system ...
The mean squared error is utilized as loss function for regressions and the categorical cross entropy for classifications [142]. For the training of classification, 𝑧topztop and 𝑟effreff retrievals, all samples are weighted equally (with 1) when calculating the total loss of a batch of ...
The mean squared error is utilized as loss function for regressions and the categorical cross entropy for classifications [142]. For the training of classification, 𝑧topztop and 𝑟effreff retrievals, all samples are weighted equally (with 1) when calculating the total loss of a batch of ...