how to dynamically scale autoencoder with different input features
I am trying to use one code and create autoencoder with following configuration
hidden=c(6,6,6) as shown below.
1) How can I use the same code for multiple input dataframe such that one can have 20 features and other can have 400 and 1000 and so on. Goal is to change the hidden vector
2) What does hidden=c(6,6,6) mean ? I see that there are 3 hidden layers with 6 neurons each but where is the input, hidden, latent dimension and output, How many neurons? Do we just provide the encoding section and H2O will mimic the same for decoding ?
3) To make it dynamic can we do hidden = c(Num_input_feats, int(Num_input_feats/2), int(Num_input_feats/3), int(Num_input_feats/2), Num_input_feats)
Eg: If the data had 30 features then the complete autoencoder would be
hidden = c(30, 15, 8, 15, 30) ? or should it be
hidden = c(30,15,8)
var.dl = h2o.deeplearning(x=parms, training_frame=h2o.dat, autoencoder=TRUE, reproducible=T, seed=1234, activation="TanhWithDropout", hidden=c(6,6,6),epochs=50)