Solved – Does it make sense to use dropout in last layer of regression neural network?

I have a neural network that I constructed in keras that goes from a LSTM recurrent layer > dropout > flattened > dense layer of 1 unit.

Does this make sense to have dropout regularization at this stage? Would this be creating sparsity in the penultimate layer or would it be evening out the connections to the final prediction?

bidirectional_1 (Bidirection (None, 60, 512)           1181696    _________________________________________________________________ dropout_2 (Dropout)          (None, 60, 512)           0          _________________________________________________________________ flatten_1 (Flatten)          (None, 30720)             0          _________________________________________________________________ dense_1 (Dense)              (None, 1)                 30721      ================================================================= 

This is perfectly fine. Check out this paper.

Hope this helps!

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