I am trying to create a denoising auto-encoder that imputes missing data. I only have a small number of data points ~100 each with ~200 features of which up to 20% maybe be missing for a given data point.
My intention is to train an auto-encoder using SGD (batch size = 1) to impute the missing data for subsequent analysis.
Within each batch (i.e. a single data point) I would like to do dropout in the first hidden layer along the where I drop the columns where features are missing. Is this something that is achievable within the pytorch framework?