Questions for the note book Predictive_Maintenance_SiameseNet

[kcg2015]

Hi, Marco, really enjoy this notebook!!! I am working on a similar problem([https://github.com/kcg2015/fiber_cuts_prediction)]. In your SeameseNet architecture, you add a Dropout layer after the difference calculation of two encoders

L1_layer = Lambda(lambda tensor: K.abs(tensor[0] - tensor[1])) L1_distance = L1_layer([encoded_l, encoded_r]) drop = Dropout(0.2)(L1_distance) Could you let me know what is the rationale behind this? More importantly, would adding this dropout layer significantly reduce the overfitting? Thanks, Kyle

[cerlymarco]

Hi, thanks for your interest! I don't remember if this is the case, but its role is exactly what you underlined... nonetheless, the best way to answer is to try and see the difference with and without dropout or other regularization techniques with your data. All the best