R version 4.4.0 beta (2024-04-10 r86396 ucrt) -- "Puppy Cup"
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Platform: x86_64-w64-mingw32/x64

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> 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] 6.933189 0.000000
[1] 6.950091 0.000000
[1] 6.933873 0.000000
[1] 6.984492 0.000000
[1] 6.976321 0.000000
[1] 6.888918 0.000000
[1] 6.995339 0.000000
[1] 7.00649 0.00000
[1] 6.930438 0.000000
[1] 6.922481 0.000000
[1] 6.800637 0.000000
[1] 6.84142 0.00000
[1] 6.713437 0.000000
[1] 7.110403 0.000000
[1] 7.073489 0.000000
[1] 6.74711 0.00000
[1] 6.68745 0.00000
[1] 6.631985 0.000000
[1] 7.176194 0.000000
[1] 6.797863 0.000000
[1] 6.9699723 0.4955718
[1] 7.1380309 0.4935322
[1] 6.9542747 0.4950035
[1] 6.8176846 0.4965759
[1] 6.9573765 0.4981195
[1] 6.6732691 0.5003049
[1] 6.9676917 0.5021173
[1] 6.9626113 0.4994666
[1] 6.959308 0.497169
[1] 7.2526867 0.4953377
[1] 7.1437037 0.4965008
[1] 6.8207583 0.4963722
[1] 6.939622 0.496273
[1] 6.6717971 0.4987209
[1] 6.9547467 0.4988035
[1] 6.5387581 0.4957091
[1] 6.5923843 0.4923142
[1] 6.9887972 0.4912649
[1] 7.4002361 0.4868058
[1] 7.1158639 0.4872817
[1] 6.8193798 0.4876907
[1] 7.2385988 0.4915599
[1] 6.9361789 0.4924323
[1] 7.0281705 0.4918687
[1] 6.9996900 0.4911113
[1] 7.0986332 0.4888449
[1] 6.9264027 0.4867518
[1] 6.965489 0.485120
[1] 6.8935829 0.4872431
[1] 7.0342436 0.4884427
[1] 6.9116607 0.4900835
[1] 7.0205733 0.4885343
[1] 6.926643 0.489608
[1] 6.8403491 0.4889002
[1] 6.8409103 0.4889269
[1] 6.8384497 0.4894158
[1] 7.175683 0.489580
[1] 7.0671985 0.4926905
[1] 7.0138696 0.4957161
[1] 6.9867887 0.4986344
[1] 6.945640 0.500668
[1] 6.9428492 0.5018749
[1] 6.9230618 0.5002584
[1] 6.9252184 0.5012439
[1] 6.9364720 0.5003335
[1] 6.9183771 0.4994338
[1] 6.9138117 0.5000993
[1] 6.9191407 0.5002349
[1] 6.9396867 0.5001856
[1] 6.9097019 0.4998821
[1] 6.8976883 0.5007975
[1] 6.898398 0.500845
[1] 6.8886410 0.5008191
[1] 6.9011233 0.4997902
[1] 6.8961749 0.5000684
[1] 6.8621097 0.4985641
[1] 7.0825069 0.5003416
[1] 6.9066894 0.5018991
[1] 6.9154334 0.5011912
[1] 7.0773187 0.5016796
[1] 6.8955970 0.5012926
[1] 6.8500984 0.5008123
[1] 6.7915808 0.5004836
[1] 6.9950463 0.4994323
[1] 6.7824946 0.5003918
[1] 6.8924339 0.5014063
[1] 6.5772161 0.5027217
[1] 6.8720804 0.5008134
[1] 7.0365131 0.4992847
[1] 6.7594735 0.5004454
[1] 7.175000 0.498809
[1] 6.6396905 0.4980598
[1] 6.5886326 0.5003198
[1] 7.1326897 0.5029167
[1] 6.687077 0.505217
[1] 6.5698490 0.5068865
[1] 6.7265203 0.5101548
[1] 6.9405391 0.5074227
[1] 6.2294394 0.5045163
> 
> pred <- NNpredict(  net=net,
+                     param=netwts$opt,
+                     newdata=d,
+                     newtruth=truth,
+                     record=TRUE,
+                     plot=TRUE)
1000 
> 
> proc.time()
   user  system elapsed 
   1.93    0.31    2.20