Neural Network resource

I haven't used the AMORE package before, but it sounds like you
haven't set linear output units or something. Here's an example using
the nnet package of what you're doing i think:

### R START###

# set random seed to a cool number
set.seed(42)

# set up data
x1<-rnorm(100); x2<-rnorm(100); x3<-rnorm(100)
x4<-rnorm(100); x5<-rnorm(100); x6<-rnorm(100)
b1<-1; b2<-2; b3<-3
b4<-4; b5<-5; b6<-6
y<-b1*x1 + b2*x2 + b3*x3 + b4*x4 + b5*x5 + b6*x6
my.df <- data.frame(cbind(y, x1, x2, x3, x4, x5, x6))

# 1. linear regression
my.lm <- lm(y~., data=my.df)

# look at correlation
my.lm.predictions<-predict(my.lm)
cor(my.df["y"], my.lm.predictions)

[,1]
y    1

# 2. nnet
library(nnet)
my.nnet<-nnet(y~., data=my.df, size=3,

linout=TRUE, skip=TRUE,
                     trace=FALSE, maxit=1000)

my.nnet.predictions<-predict(my.nnet, my.df)
# look at correlation
cor(my.df["y"], my.nnet.predictions)

[,1]
y    1

# to look at the values side by side
cbind(my.df["y"], my.nnet.predictions)

y     my.nnet.predictions
1    10.60102566         10.59958907
2     6.70939465          6.70956529
3     2.28934732          2.28928930
4    14.51012458         14.51043732
5   -12.85845371        -12.85849345
[..etc]
### R END ###

Hope that helps a wee bit mate,

Tony Breyal

You are right there is a pdf file which describes the function. But let tell you where I am coming from.

Just to test if a neural network will work better than a ordinary least square regression, I created a dataset with one dependent variable and 6 other independent variables. Now I had deliberately created the dataset in such manner that we have an excellent regression model. Eg: Y = b0 + b1*x1 + b2*x2 + b3*x3.. + b6*x6 + e
where e is normal random variable. Naturally any statistical analysis system running regression would easily predict the values of b1, b2, b3, ..., b6 with around 30-40 observations.

I fed this data into a Neural network (3 hidden layers with 6 neurons in each layer) and trained the network. When I passed the input dataset and tried to get the predictions, all the predicted values were identical! This confused me a bit and was wondering whether my understanding of the Neural Network was wrong.

Have you ever faced anything like it?

Regards,
Indrajit

________________________________
From: "markle... at verizon.net" <markle... at verizon.net>

Sent: Wednesday, May 27, 2009 7:54:59 PM
Subject: Re: [R] Neural Network resource

Hi: I've never used that package but most likely there is a? AMORE vignette that shows examples and describes the functions.
it should be on the same cran? web page where the package resides, in pdf form.

Hi All,

I am trying to learn Neural Networks. I found that R has packages which can help build Neural Nets - the popular one being AMORE package. Is there any book / resource available which guides us in this subject using the AMORE package?

Any help will be much appreciated.

Thanks,
Indrajit

______________________________________________
R-h... at r-project.org mailing listhttps://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guidehttp://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.

? ? ? ? [[alternative HTML version deleted]]

______________________________________________
R-h... at r-project.org mailing listhttps://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guidehttp://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.

I fed this data into a Neural network (3 hidden layers with 6 neurons in each layer) and trained the network. When I passed the input dataset and tried to get the predictions, all the predicted values were identical! This confused me a bit and was wondering whether my understanding of the Neural Network was wrong.

Have you ever faced anything like it?

You should really provide code for us to help. I would initially
suspect that you didn't use a linear function between your hidden
units and the outcomes.

Also, using 3 hidden layers and 6 units per layer is a bit much for
your data set (30-40 samples). You will probably end up overfitting.

Max

Here is the code that i had used:

#########################################


## Read in the raw data
fitness <- c(44,89.47,44.609,11.37,62,178,182,
40,75.07,45.313,10.07,62,185,185,
44,85.84,54.297,8.65,45,156,168,
42,68.15,59.571,8.17,40,166,172,
38,89.02,49.874,9.22,55,178,180,
47,77.45,44.811,11.63,58,176,176,
40,75.98,45.681,11.95,70,176,180,
43,81.19,49.091,10.85,64,162,170,
44,81.42,39.442,13.08,63,174,176,
38,81.87,60.055,8.63,48,170,186,
44,73.03,50.541,10.13,45,168,168,
45,87.66,37.388,14.03,56,186,192,
45,66.45,44.754,11.12,51,176,176,
47,79.15,47.273,10.6,47,162,164,
54,83.12,51.855,10.33,50,166,170,
49,81.42,49.156,8.95,44,180,185,
51,69.63,40.836,10.95,57,168,172,
51,77.91,46.672,10,48,162,168,
48,91.63,46.774,10.25,48,162,164,
49,73.37,50.388,10.08,67,168,168,
57,73.37,39.407,12.63,58,174,176,
54,79.38,46.08,11.17,62,156,165,
52,76.32,45.441,9.63,48,164,166,
50,70.87,54.625,8.92,48,146,155,
51,67.25,45.118,11.08,48,172,172,
54,91.63,39.203,12.88,44,168,172,
51,73.71,45.79,10.47,59,186,188,
57,59.08,50.545,9.93,49,148,155,
49,76.32,48.673,9.4,56,186,188,
48,61.24,47.92,11.5,52,170,176,
52,82.78,47.467,10.5,53,170,172
)
fitness2 <- data.frame(matrix(fitness,nrow = 31, byrow = TRUE))
colnames(fitness2) <- c("Age","Weight","Oxygen","RunTime","RestPulse","RunPulse","MaxPulse")
attach(fitness2)
## Create the input dataset
indep <- fitness2[,-3]
## Create the neural network structure 
net.start <- newff(n.neurons=c(6,6,6,1),????? 
???????????? learning.rate.global=1e-2,??????? 
???????????? momentum.global=0.5,????????????? 
???????????? error.criterium="LMS",?????????? 
???????????? Stao=NA, hidden.layer="tansig",?? 
???????????? output.layer="purelin",?????????? 
???????????? method="ADAPTgdwm")
## Train the net
result <- train(net.start, indep, Oxygen, error.criterium="LMS", report=TRUE, show.step=100, n.shows=5 ) 
## Predict
pred <- sim(result$net, indep)
pred???????????? 
###########################################???????????? 

Here?I am trying to predict Oxygen levels using the 6 independent variables.?But whenever I am trying to run a prediction - I am getting constant values throughout (In the above example - the values of pred).

Thanks & Regards,
Indrajit

?


----- Original Message ----
From: Max Kuhn <mxkuhn at gmail.com>
To: Indrajit Sengupta <indra_calisto at yahoo.com>
Cc: markleeds at verizon.net; R Help <r-help at r-project.org>
Sent: Wednesday, May 27, 2009 9:19:47 PM
Subject: Re: [R] Neural Network resource

I fed this data into a Neural network (3 hidden layers with 6 neurons in each layer) and trained the network. When I passed the input dataset and tried to get the predictions, all the predicted values were identical! This confused me a bit and was wondering whether my understanding of the Neural Network was wrong.

Have you ever faced anything like it?

You should really provide code for us to help. I would initially
suspect that you didn't use a linear function between your hidden
units and the outcomes.

Also, using 3 hidden layers and 6 units per layer is a bit much for
your data set (30-40 samples). You will probably end up overfitting.

Max