Well, the wording is pretty unclear, but my guess is that he wants you to encode the protein sequence into DNA codons and decode again into a protein and look at the similarity
Admittedly, it's a very weird use case for autoencoder since there is a fixed mapping between codons and amino acids, and no real noise to clean I can think of (it would make more ...
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 - ...
From the Keras documentation:
Data on which to evaluate the loss and any model metrics at the end of each epoch. The model will not be trained on this data. Thus, note the fact that the validation loss of data provided using validation_split or validation_data is not affected by regularization layers like noise and dropout. validation_data ...
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 ...