hidden=c(6,6,6) as shown below.

Question:

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)

]]>I'm trying to carry out a classification model on data set with malignant and benign tumors. The first time, I was able to select classification model. I lost the model because of an internet bug. Thus, I carried out a second tryied but this time, regression model is selected and I cannot select the classification option. How can I solve this problem ?

Thank you so much for your answer !

]]>Kindly use this discussion to discuss the PRs that you are working on or raising for the study group to help with coordination.

]]>I am running a deep learning model (binary classification) with following grid search code

hyper_params <- list(

activation = c("Rectifier", "Maxout", "Tanh", "RectifierWithDropout", "MaxoutWithDropout", "TanhWithDropout"),

hidden = list(c(5, 5, 5, 5, 5), c(10, 10, 10, 10), c(50, 50, 50), c(100, 100, 100)),

epochs = c(50, 100, 200),

l1 = c(0, 0.00001, 0.0001),

l2 = c(0, 0.00001, 0.0001),

rate = c(0, 01, 0.005, 0.001),

rate_annealing = c(1e-8, 1e-7, 1e-6),

rho = c(0.9, 0.95, 0.99, 0.999),

epsilon = c(1e-10, 1e-8, 1e-6, 1e-4),

momentum_start = c(0, 0.5),

momentum_stable = c(0.99, 0.5, 0),

input_dropout_ratio = c(0, 0.1, 0.2),

max_w2 = c(10, 100, 1000, 3.4028235e+38)

)

search_criteria <- list(strategy = "RandomDiscrete",

max_models = 100,

max_runtime_secs = 12000,

stopping_tolerance = 0.001,

stopping_rounds = 15,

seed = 42)

dl_grid <- h2o.grid(algorithm = "deeplearning",

x = x,

y = y,

grid_id = "dl_grid",

training_frame = df,

validation_frame = tst,

nfolds = 25,

fold_assignment = "Stratified",

hyper_params = hyper_params,

search_criteria = search_criteria,

seed = 42

)

**However I am getting the error "na .h2o.doSafeREST(h2oRestApiVersion = h2oRestApiVersion, urlSuffix = urlSuffix, : Unexpected CURL error: Timeout was reached: [localhost:54321] Resolving timed out after 10180 milliseconds [1] "Job request failed Unexpected CURL error: Timeout was reached: [localhost:54321] Resolving timed out after 10180 milliseconds, will retry after 3s."**

R version details are

platform x86_64-w64-mingw32

arch x86_64

os mingw32

crt ucrt

system x86_64, mingw32

status

major 4

minor 2.0

year 2022

month 04

day 22

svn rev 82229

language R

version.string R version 4.2.0 (2022-04-22 ucrt)

nickname Vigorous Calisthenics

Kindly help me

Thank you