R version 4.4.0 beta (2024-04-10 r86396 ucrt) -- "Puppy Cup"
Copyright (C) 2024 The R Foundation for Statistical Computing
Platform: x86_64-w64-mingw32/x64

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(deepNN)
> 
> N <- 1000
> d <- matrix(rnorm(5*N),ncol=5)
> 
> fun <- function(x){
+     lp <- 2*x[2]
+     pr <- exp(lp) / (1 + exp(lp))
+     ret <- c(0,0)
+     ret[1+rbinom(1,1,pr)] <- 1
+     return(ret)
+ }
> 
> d <- lapply(1:N,function(i){return(d[i,])})
> 
> truth <- lapply(d,fun)
> 
> net <- network( dims = c(5,10,2),
+                 activ=list(ReLU(),softmax()))
> 
> netwts <- train( dat=d,
+                  truth=truth,
+                  net=net,
+                  eps=0.01,
+                  tol=100,            # run for 100 iterations
+                  batchsize=10,       # note this is not enough
+                  loss=multinomial(), # for convergence
+                  stopping="maxit")
[1] 6.943888 0.000000
[1] 6.933191 0.000000
[1] 7.032016 0.000000
[1] 6.989746 0.000000
[1] 6.94473 0.00000
[1] 6.93126 0.00000
[1] 6.924207 0.000000
[1] 6.878339 0.000000
[1] 6.804929 0.000000
[1] 6.842238 0.000000
[1] 6.948685 0.000000
[1] 6.947105 0.000000
[1] 7.157745 0.000000
[1] 6.997445 0.000000
[1] 6.970845 0.000000
[1] 6.946958 0.000000
[1] 6.936468 0.000000
[1] 6.962108 0.000000
[1] 6.930813 0.000000
[1] 6.907072 0.000000
[1] 6.976217 0.504552
[1] 6.9525927 0.5043667
[1] 6.9310563 0.5047823
[1] 6.9111286 0.5054169
[1] 6.9326443 0.5055459
[1] 6.8952349 0.5052593
[1] 6.9357291 0.5058216
[1] 7.0389860 0.5052832
[1] 6.9310304 0.5057209
[1] 6.9404746 0.5048241
[1] 6.9085857 0.5034731
[1] 6.9844696 0.5014907
[1] 6.9034801 0.5031606
[1] 7.0253702 0.5024427
[1] 6.9272416 0.5016678
[1] 6.9077345 0.5014947
[1] 7.0190759 0.5008033
[1] 6.9300916 0.5008765
[1] 6.8915674 0.5005828
[1] 6.9919556 0.4996046
[1] 6.9328957 0.4997447
[1] 6.8297960 0.4996346
[1] 6.8499712 0.5009858
[1] 6.9436685 0.5022507
[1] 7.146680 0.500801
[1] 6.933249 0.499650
[1] 6.8257340 0.5007447
[1] 6.8489394 0.5011794
[1] 6.9426278 0.5030873
[1] 6.9401977 0.5017766
[1] 6.7487812 0.5007416
[1] 6.9483056 0.5024404
[1] 6.698150 0.504517
[1] 6.6430786 0.5060727
[1] 7.3546492 0.5077867
[1] 7.0959394 0.5095324
[1] 6.8311074 0.5106092
[1] 7.0746581 0.5126448
[1] 7.0436260 0.5114198
[1] 6.9363123 0.5110042
[1] 7.0054073 0.5108349
[1] 6.8794410 0.5098413
[1] 6.9316004 0.5102436
[1] 6.8640991 0.5084315
[1] 6.7815058 0.5062398
[1] 6.710277 0.506078
[1] 6.9482787 0.5085365
[1] 7.0935504 0.5061651
[1] 6.8276493 0.5037181
[1] 6.8115175 0.5040053
[1] 6.8046144 0.5069212
[1] 6.7900560 0.5062854
[1] 6.6263585 0.5067395
[1] 6.9644205 0.5028933
[1] 6.7810564 0.5032769
[1] 6.9696115 0.5034135
[1] 7.1565737 0.4995546
[1] 7.271969 0.496418
[1] 7.0505095 0.4962658
[1] 7.0926441 0.4985442
[1] 6.9217549 0.4989787
[1] 6.9212817 0.5003502
[1] 6.9192657 0.5004377
[1] 6.9490857 0.4992437
[1] 6.9240124 0.5001761
[1] 6.9404202 0.5010556
[1] 6.8551292 0.5028274
[1] 7.0580443 0.5004227
[1] 7.0226837 0.5025313
[1] 7.0921951 0.5032965
[1] 6.8928034 0.5018612
[1] 6.8957730 0.5000211
[1] 6.8766057 0.5020528
[1] 6.8638582 0.5006472
[1] 6.8295882 0.5037857
[1] 6.9946041 0.5025404
[1] 6.941964 0.500780
[1] 6.9112299 0.5034342
[1] 6.8076352 0.5054724
> 
> pred <- NNpredict(  net=net,
+                     param=netwts$opt,
+                     newdata=d,
+                     newtruth=truth,
+                     record=TRUE,
+                     plot=TRUE)
1000 
> 
> proc.time()
   user  system elapsed 
   2.09    0.31    2.39