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I don't see why not- it's the loss you wish to minimize.
I'm using the following as my loss function and it works well when sMAPE is my metric for prediction accuracy.
import tensorflow.keras.backend as K
def smape_loss(y_true, y_pred):
epsilon = 0.1
summ = K.maximum(K.abs(y_true) + K.abs(y_pred) + epsilon, 0.5 + epsilon)
smape = K.abs(y_pred - ...
1
Monte-Carlo Dropout is the use of dropout at inference time in order to add stochasticity to a network that can be used to generate a cohort of predictors/predictions that you can perform statistical analysis on. This is commonly used for bootstrapping confidence intervals.
Where you perform dropout in your sequential model is therefore important. If you add ...
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