I want to adjust weight decay and number of hidden units for nnet by a loop like for(decay) { for(number of unit) { for(#run) {model<-nnet() test.error<-.... } } } for example: I set decay=0.1, size=3, maxit=200, for this set I run 10 times, and calculate test error after that I want to get a matrix like this decay size maxit #run test_error 0.1 3 200 1 1.2 0.1 3 200 2 1.1 0.1 3 200 3 1.0 0.1 3 200 4 3.4 0.1 3 200 5 .. 0.1 3 200 6 .. 0.1 3 200 7 .. 0.1 3 200 8 .. 0.1 3 200 9 .. 0.1 3 200 10 .. 0.2 3 200 1 1.2 0.2 3 200 2 1.1 0.2 3 200 3 1.0 0.2 3 200 4 3.4 0.2 3 200 5 .. 0.2 3 200 6 .. 0.2 3 200 7 .. 0.2 3 200 8 .. 0.2 3 200 9 .. 0.2 3 200 10 .. I am not sure if this is correct way to do this? Does anyone tune these parameters like this before? thanks, Aimin
AM, I have a pieice of junk on my blog. Here it is. ################################################# # USE CROSS-VALIDATION TO DO A GRID-SEARCH FOR # # THE OPTIMAL SETTINGS (WEIGHT DECAY AND NUMBER # # OF HIDDEN UNITS) OF NEURAL NETS # ################################################# library(nnet); library(MASS); data(Boston); X <- I(as.matrix(Boston[-14])); # STANDARDIZE PREDICTORS st.X <- scale(X); Y <- I(as.matrix(Boston[14])); boston <- data.frame(X = st.X, Y); # DIVIDE DATA INTO TESTING AND TRAINING SETS set.seed(2005); test.rows <- sample(1:nrow(boston), 100); test.set <- boston[test.rows, ]; train.set <- boston[-test.rows, ]; # INITIATE A NULL TABLE sse.table <- NULL; # SEARCH FOR OPTIMAL WEIGHT DECAY # RANGE OF WEIGHT DECAYS SUGGESTED BY B. RIPLEY for (w in c(0.0001, 0.001, 0.01)) { # SEARCH FOR OPTIMAL NUMBER OF HIDDEN UNITS for (n in 1:10) { # UNITIATE A NULL VECTOR sse <- NULL; # FOR EACH SETTING, RUN NEURAL NET MULTIPLE TIMES for (i in 1:10) { # INITIATE THE RANDOM STATE FOR EACH NET set.seed(i); # TRAIN NEURAL NETS net <- nnet(Y~X, size = n, data = train.set, rang = 0.00001, linout = TRUE, maxit = 10000, decay = w, skip = FALSE, trace = FALSE); # CALCULATE SSE FOR TESTING SET test.sse <- sum((test.set$Y - predict(net, test.set))^2); # APPEND EACH SSE TO A VECTOR if (i == 1) sse <- test.sse else sse <- rbind(sse, test.sse); } # APPEND AVERAGED SSE WITH RELATED PARAMETERS TO A TABLE sse.table <- rbind(sse.table, c(WT = w, UNIT = n, SSE = mean(sse))); } } # PRINT OUT THE RESULT print(sse.table);http://statcompute.spaces.live.com/Blog/cns!39C8032DBD1321B7!290.entry On 3/9/07, Aimin Yan <aiminy at iastate.edu> wrote:> I want to adjust weight decay and number of hidden units for nnet by > a loop like > for(decay) > { > for(number of unit) > { > for(#run) > {model<-nnet() > test.error<-.... > } > } > } > > for example: > I set decay=0.1, size=3, maxit=200, for this set I run 10 times, and > calculate test error > > after that I want to get a matrix like this > > decay size maxit #run test_error > 0.1 3 200 1 1.2 > 0.1 3 200 2 1.1 > 0.1 3 200 3 1.0 > 0.1 3 200 4 3.4 > 0.1 3 200 5 .. > 0.1 3 200 6 .. > 0.1 3 200 7 .. > 0.1 3 200 8 .. > 0.1 3 200 9 .. > 0.1 3 200 10 .. > 0.2 3 200 1 1.2 > 0.2 3 200 2 1.1 > 0.2 3 200 3 1.0 > 0.2 3 200 4 3.4 > 0.2 3 200 5 .. > 0.2 3 200 6 .. > 0.2 3 200 7 .. > 0.2 3 200 8 .. > 0.2 3 200 9 .. > 0.2 3 200 10 .. > > I am not sure if this is correct way to do this? > Does anyone tune these parameters like this before? > thanks, > > Aimin > > ______________________________________________ > R-help at stat.math.ethz.ch mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. >-- WenSui Liu A lousy statistician who happens to know a little programming (http://spaces.msn.com/statcompute/blog)
AM, Sorry. please ignore the top box in the code. It is not actually a cv validation but just a simple split-sample validation. sorry for confusion. On 3/9/07, Wensui Liu <liuwensui at gmail.com> wrote:> AM, > I have a pieice of junk on my blog. Here it is. > ################################################# > # USE CROSS-VALIDATION TO DO A GRID-SEARCH FOR # > # THE OPTIMAL SETTINGS (WEIGHT DECAY AND NUMBER # > # OF HIDDEN UNITS) OF NEURAL NETS # > ################################################# > > library(nnet); > library(MASS); > data(Boston); > X <- I(as.matrix(Boston[-14])); > # STANDARDIZE PREDICTORS > st.X <- scale(X); > Y <- I(as.matrix(Boston[14])); > boston <- data.frame(X = st.X, Y); > > # DIVIDE DATA INTO TESTING AND TRAINING SETS > set.seed(2005); > test.rows <- sample(1:nrow(boston), 100); > test.set <- boston[test.rows, ]; > train.set <- boston[-test.rows, ]; > > # INITIATE A NULL TABLE > sse.table <- NULL; > > # SEARCH FOR OPTIMAL WEIGHT DECAY > # RANGE OF WEIGHT DECAYS SUGGESTED BY B. RIPLEY > for (w in c(0.0001, 0.001, 0.01)) > { > # SEARCH FOR OPTIMAL NUMBER OF HIDDEN UNITS > for (n in 1:10) > { > # UNITIATE A NULL VECTOR > sse <- NULL; > # FOR EACH SETTING, RUN NEURAL NET MULTIPLE TIMES > for (i in 1:10) > { > # INITIATE THE RANDOM STATE FOR EACH NET > set.seed(i); > # TRAIN NEURAL NETS > net <- nnet(Y~X, size = n, data = train.set, rang = 0.00001, > linout = TRUE, maxit = 10000, decay = w, > skip = FALSE, trace = FALSE); > # CALCULATE SSE FOR TESTING SET > test.sse <- sum((test.set$Y - predict(net, test.set))^2); > # APPEND EACH SSE TO A VECTOR > if (i == 1) sse <- test.sse else sse <- rbind(sse, test.sse); > } > # APPEND AVERAGED SSE WITH RELATED PARAMETERS TO A TABLE > sse.table <- rbind(sse.table, c(WT = w, UNIT = n, SSE = mean(sse))); > } > } > # PRINT OUT THE RESULT > print(sse.table);http://statcompute.spaces.live.com/Blog/cns!39C8032DBD1321B7!290.entry > > > On 3/9/07, Aimin Yan <aiminy at iastate.edu> wrote: > > I want to adjust weight decay and number of hidden units for nnet by > > a loop like > > for(decay) > > { > > for(number of unit) > > { > > for(#run) > > {model<-nnet() > > test.error<-.... > > } > > } > > } > > > > for example: > > I set decay=0.1, size=3, maxit=200, for this set I run 10 times, and > > calculate test error > > > > after that I want to get a matrix like this > > > > decay size maxit #run test_error > > 0.1 3 200 1 1.2 > > 0.1 3 200 2 1.1 > > 0.1 3 200 3 1.0 > > 0.1 3 200 4 3.4 > > 0.1 3 200 5 .. > > 0.1 3 200 6 .. > > 0.1 3 200 7 .. > > 0.1 3 200 8 .. > > 0.1 3 200 9 .. > > 0.1 3 200 10 .. > > 0.2 3 200 1 1.2 > > 0.2 3 200 2 1.1 > > 0.2 3 200 3 1.0 > > 0.2 3 200 4 3.4 > > 0.2 3 200 5 .. > > 0.2 3 200 6 .. > > 0.2 3 200 7 .. > > 0.2 3 200 8 .. > > 0.2 3 200 9 .. > > 0.2 3 200 10 .. > > > > I am not sure if this is correct way to do this? > > Does anyone tune these parameters like this before? > > thanks, > > > > Aimin > > > > ______________________________________________ > > R-help at stat.math.ethz.ch mailing list > > https://stat.ethz.ch/mailman/listinfo/r-help > > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > > and provide commented, minimal, self-contained, reproducible code. > > > > > -- > WenSui Liu > A lousy statistician who happens to know a little programming > (http://spaces.msn.com/statcompute/blog) >-- WenSui Liu A lousy statistician who happens to know a little programming (http://spaces.msn.com/statcompute/blog)
thank you very much. I have a another question about nnet if I set size=0, and skip=TRUE. Then this network has just input layer and out layer. Is this also called perceptron network? thanks, Aimin Yan At 12:39 PM 3/9/2007, Wensui Liu wrote:>AM, >Sorry. please ignore the top box in the code. It is not actually a cv >validation but just a simple split-sample validation. >sorry for confusion. > >On 3/9/07, Wensui Liu <liuwensui at gmail.com> wrote: >>AM, >>I have a pieice of junk on my blog. Here it is. >>################################################# >># USE CROSS-VALIDATION TO DO A GRID-SEARCH FOR # >># THE OPTIMAL SETTINGS (WEIGHT DECAY AND NUMBER # >># OF HIDDEN UNITS) OF NEURAL NETS # >>################################################# >> >>library(nnet); >>library(MASS); >>data(Boston); >>X <- I(as.matrix(Boston[-14])); >># STANDARDIZE PREDICTORS >>st.X <- scale(X); >>Y <- I(as.matrix(Boston[14])); >>boston <- data.frame(X = st.X, Y); >> >># DIVIDE DATA INTO TESTING AND TRAINING SETS >>set.seed(2005); >>test.rows <- sample(1:nrow(boston), 100); >>test.set <- boston[test.rows, ]; >>train.set <- boston[-test.rows, ]; >> >># INITIATE A NULL TABLE >>sse.table <- NULL; >> >># SEARCH FOR OPTIMAL WEIGHT DECAY >># RANGE OF WEIGHT DECAYS SUGGESTED BY B. RIPLEY >>for (w in c(0.0001, 0.001, 0.01)) >>{ >> # SEARCH FOR OPTIMAL NUMBER OF HIDDEN UNITS >> for (n in 1:10) >> { >> # UNITIATE A NULL VECTOR >> sse <- NULL; >> # FOR EACH SETTING, RUN NEURAL NET MULTIPLE TIMES >> for (i in 1:10) >> { >> # INITIATE THE RANDOM STATE FOR EACH NET >> set.seed(i); >> # TRAIN NEURAL NETS >> net <- nnet(Y~X, size = n, data = train.set, rang = 0.00001, >> linout = TRUE, maxit = 10000, decay = w, >> skip = FALSE, trace = FALSE); >> # CALCULATE SSE FOR TESTING SET >> test.sse <- sum((test.set$Y - predict(net, test.set))^2); >> # APPEND EACH SSE TO A VECTOR >> if (i == 1) sse <- test.sse else sse <- rbind(sse, test.sse); >> } >> # APPEND AVERAGED SSE WITH RELATED PARAMETERS TO A TABLE >> sse.table <- rbind(sse.table, c(WT = w, UNIT = n, SSE = mean(sse))); >> } >>} >># PRINT OUT THE RESULT >>print(sse.table);http://statcompute.spaces.live.com/Blog/cns!39C8032DBD1321B7!290.entry >> >> >>On 3/9/07, Aimin Yan <aiminy at iastate.edu> wrote: >> > I want to adjust weight decay and number of hidden units for nnet by >> > a loop like >> > for(decay) >> > { >> > for(number of unit) >> > { >> > for(#run) >> > {model<-nnet() >> > test.error<-.... >> > } >> > } >> > } >> > >> > for example: >> > I set decay=0.1, size=3, maxit=200, for this set I run 10 times, and >> > calculate test error >> > >> > after that I want to get a matrix like this >> > >> > decay size maxit #run test_error >> > 0.1 3 200 1 1.2 >> > 0.1 3 200 2 1.1 >> > 0.1 3 200 3 1.0 >> > 0.1 3 200 4 3.4 >> > 0.1 3 200 5 .. >> > 0.1 3 200 6 .. >> > 0.1 3 200 7 .. >> > 0.1 3 200 8 .. >> > 0.1 3 200 9 .. >> > 0.1 3 200 10 .. >> > 0.2 3 200 1 1.2 >> > 0.2 3 200 2 1.1 >> > 0.2 3 200 3 1.0 >> > 0.2 3 200 4 3.4 >> > 0.2 3 200 5 .. >> > 0.2 3 200 6 .. >> > 0.2 3 200 7 .. >> > 0.2 3 200 8 .. >> > 0.2 3 200 9 .. >> > 0.2 3 200 10 .. >> > >> > I am not sure if this is correct way to do this? >> > Does anyone tune these parameters like this before? >> > thanks, >> > >> > Aimin >> > >> > ______________________________________________ >> > R-help at stat.math.ethz.ch mailing list >> > https://stat.ethz.ch/mailman/listinfo/r-help >> > PLEASE do read the posting guide >> http://www.R-project.org/posting-guide.html >> > and provide commented, minimal, self-contained, reproducible code. >> > >> >> >>-- >>WenSui Liu >>A lousy statistician who happens to know a little programming >>(http://spaces.msn.com/statcompute/blog) > > >-- >WenSui Liu >A lousy statistician who happens to know a little programming >(http://spaces.msn.com/statcompute/blog)
no, it is called regression. ^_^. On 3/9/07, Aimin Yan <aiminy at iastate.edu> wrote:> thank you very much. > I have a another question about nnet > if I set size=0, and skip=TRUE. > Then this network has just input layer and out layer. > Is this also called perceptron network? > > thanks, > > Aimin Yan > > > At 12:39 PM 3/9/2007, Wensui Liu wrote: > >AM, > >Sorry. please ignore the top box in the code. It is not actually a cv > >validation but just a simple split-sample validation. > >sorry for confusion. > > > >On 3/9/07, Wensui Liu <liuwensui at gmail.com> wrote: > >>AM, > >>I have a pieice of junk on my blog. Here it is. > >>################################################# > >># USE CROSS-VALIDATION TO DO A GRID-SEARCH FOR # > >># THE OPTIMAL SETTINGS (WEIGHT DECAY AND NUMBER # > >># OF HIDDEN UNITS) OF NEURAL NETS # > >>################################################# > >> > >>library(nnet); > >>library(MASS); > >>data(Boston); > >>X <- I(as.matrix(Boston[-14])); > >># STANDARDIZE PREDICTORS > >>st.X <- scale(X); > >>Y <- I(as.matrix(Boston[14])); > >>boston <- data.frame(X = st.X, Y); > >> > >># DIVIDE DATA INTO TESTING AND TRAINING SETS > >>set.seed(2005); > >>test.rows <- sample(1:nrow(boston), 100); > >>test.set <- boston[test.rows, ]; > >>train.set <- boston[-test.rows, ]; > >> > >># INITIATE A NULL TABLE > >>sse.table <- NULL; > >> > >># SEARCH FOR OPTIMAL WEIGHT DECAY > >># RANGE OF WEIGHT DECAYS SUGGESTED BY B. RIPLEY > >>for (w in c(0.0001, 0.001, 0.01)) > >>{ > >> # SEARCH FOR OPTIMAL NUMBER OF HIDDEN UNITS > >> for (n in 1:10) > >> { > >> # UNITIATE A NULL VECTOR > >> sse <- NULL; > >> # FOR EACH SETTING, RUN NEURAL NET MULTIPLE TIMES > >> for (i in 1:10) > >> { > >> # INITIATE THE RANDOM STATE FOR EACH NET > >> set.seed(i); > >> # TRAIN NEURAL NETS > >> net <- nnet(Y~X, size = n, data = train.set, rang = 0.00001, > >> linout = TRUE, maxit = 10000, decay = w, > >> skip = FALSE, trace = FALSE); > >> # CALCULATE SSE FOR TESTING SET > >> test.sse <- sum((test.set$Y - predict(net, test.set))^2); > >> # APPEND EACH SSE TO A VECTOR > >> if (i == 1) sse <- test.sse else sse <- rbind(sse, test.sse); > >> } > >> # APPEND AVERAGED SSE WITH RELATED PARAMETERS TO A TABLE > >> sse.table <- rbind(sse.table, c(WT = w, UNIT = n, SSE = mean(sse))); > >> } > >>} > >># PRINT OUT THE RESULT > >>print(sse.table);http://statcompute.spaces.live.com/Blog/cns!39C8032DBD1321B7!290.entry > >> > >> > >>On 3/9/07, Aimin Yan <aiminy at iastate.edu> wrote: > >> > I want to adjust weight decay and number of hidden units for nnet by > >> > a loop like > >> > for(decay) > >> > { > >> > for(number of unit) > >> > { > >> > for(#run) > >> > {model<-nnet() > >> > test.error<-.... > >> > } > >> > } > >> > } > >> > > >> > for example: > >> > I set decay=0.1, size=3, maxit=200, for this set I run 10 times, and > >> > calculate test error > >> > > >> > after that I want to get a matrix like this > >> > > >> > decay size maxit #run test_error > >> > 0.1 3 200 1 1.2 > >> > 0.1 3 200 2 1.1 > >> > 0.1 3 200 3 1.0 > >> > 0.1 3 200 4 3.4 > >> > 0.1 3 200 5 .. > >> > 0.1 3 200 6 .. > >> > 0.1 3 200 7 .. > >> > 0.1 3 200 8 .. > >> > 0.1 3 200 9 .. > >> > 0.1 3 200 10 .. > >> > 0.2 3 200 1 1.2 > >> > 0.2 3 200 2 1.1 > >> > 0.2 3 200 3 1.0 > >> > 0.2 3 200 4 3.4 > >> > 0.2 3 200 5 .. > >> > 0.2 3 200 6 .. > >> > 0.2 3 200 7 .. > >> > 0.2 3 200 8 .. > >> > 0.2 3 200 9 .. > >> > 0.2 3 200 10 .. > >> > > >> > I am not sure if this is correct way to do this? > >> > Does anyone tune these parameters like this before? > >> > thanks, > >> > > >> > Aimin > >> > > >> > ______________________________________________ > >> > R-help at stat.math.ethz.ch mailing list > >> > https://stat.ethz.ch/mailman/listinfo/r-help > >> > PLEASE do read the posting guide > >> http://www.R-project.org/posting-guide.html > >> > and provide commented, minimal, self-contained, reproducible code. > >> > > >> > >> > >>-- > >>WenSui Liu > >>A lousy statistician who happens to know a little programming > >>(http://spaces.msn.com/statcompute/blog) > > > > > >-- > >WenSui Liu > >A lousy statistician who happens to know a little programming > >(http://spaces.msn.com/statcompute/blog) > > >-- WenSui Liu A lousy statistician who happens to know a little programming (http://spaces.msn.com/statcompute/blog)