[R] Backpropagation to adjust weights in a neural net when receiving new training examples

Fri May 29 19:35:39 CEST 2009

Not that I know of.

If you do come across any, let me know, or better still, email r-help.

Good luck with what you are trying to do.

Jude Ryan

________________________________

From: Filipe Rocha [mailto:filipemarocha at gmail.com] 
Sent: Friday, May 29, 2009 1:17 PM
To: Ryan, Jude
Cc: r-help at r-project.org
Subject: Re: [R] Backpropagation to adjust weights in a neural net when
receiving new training examples

Thanks a lot for your answers. I can try to implement backpropagation
myself with that information.

But there isnt a function or method of backpropagation of error for new
examples of training only to change the already created neural net?

I want to implemennt reinforcement learning...

Thanks in advance

Filipe Rocha

2009/5/29 <jude.ryan at ubs.com>

You can figure out which weights go with which connections with the
function summary(nnet.object) and nnet.object$wts. Sample code from
Venables and Ripley is below:

# Neural Network model in Modern Applied Statistics with S, Venables and
Ripley, pages 246 and 247

> library(nnet)

> attach(rock)

> dim(rock)

[1] 48  4

> area1 <- area/10000; peri1 <- peri/10000

> rock1 <- data.frame(perm, area = area1, peri = peri1, shape)

> dim(rock1)

[1] 48  4

> head(rock1)

  perm   area     peri     shape

1  6.3 0.4990 0.279190 0.0903296

2  6.3 0.7002 0.389260 0.1486220

3  6.3 0.7558 0.393066 0.1833120

4  6.3 0.7352 0.386932 0.1170630

5 17.1 0.7943 0.394854 0.1224170

6 17.1 0.7979 0.401015 0.1670450

> rock.nn <- nnet(log(perm) ~ area + peri + shape, rock1, size=3,
decay=1e-3, linout=T, skip=T, maxit=1000, Hess=T)

# weights:  19

initial  value 1196.787489 

iter  10 value 32.400984

iter  20 value 31.664545

...

iter 280 value 14.230077

iter 290 value 14.229809

final  value 14.229785 

converged

> summary(rock.nn)

a 3-3-1 network with 19 weights

options were - skip-layer connections  linear output units  decay=0.001

 b->h1 i1->h1 i2->h1 i3->h1 

 -0.51  -9.33  14.59   3.85 

 b->h2 i1->h2 i2->h2 i3->h2 

  0.93   3.35   6.09  -5.86 

 b->h3 i1->h3 i2->h3 i3->h3 

  0.80 -10.93  -4.58   9.53 

  b->o  h1->o  h2->o  h3->o  i1->o  i2->o  i3->o 

  1.89 -14.62   7.35   8.77  -3.00  -4.25   4.44 

> sum((log(perm) - predict(rock.nn))^2)

[1] 13.20451

> rock.nn$wts

 [1]  -0.5064848  -9.3288410  14.5859255   3.8521844   0.9266730
3.3524267   6.0900909  -5.8628448   0.8026366 -10.9345352  -4.5783516
9.5311123

[13]   1.8866734 -14.6181959   7.3466236   8.7655882  -2.9988287
-4.2508948   4.4397158

> 

In the output from summary(rock.nn), b is the bias or intercept, h1 is
the 1st hidden neuron, i1 is the first input (peri) and o is the
(linear) output. So b->h1 is the bias or intercept to the first hidden
neuron, i1->h1 is the 1st input (peri) to the first hidden neuron (there
are 3 hidden neurons in this example), h1->o is the 1st hidden neuron to
the first output, and i1->o is the first input to the output (since
skip=T - this is a skip layer network). The weights are below (b->h1 ..)
but are rounded. But rock.nn$wts gives you the un-rounded weights. If
you compare the output from summary(rock.nn) and rock.nn$wts you will
see that the first row of weights from summary() is listed first in
rock.nn$wts, followed by the 2nd row of weights from summary() and so
on.

You can construct the neural network equations manually (this is not in
the Venables Ripley book) and check the results against the predict()
function to verify that the weights are listed in the order I described.
The code to do this is:

# manually calculate the neural network predictions based on the neural
network equations

rock1$h1 <- -0.5064848 -9.3288410 * rock1$area + 14.5859255 * rock1$peri
+ 3.8521844 * rock1$shape

rock1$logistic_h1 <- exp(rock1$h1) / (1 + exp(rock1$h1))

rock1$h2 <- 0.9266730 + 3.3524267 * rock1$area + 6.0900909 * rock1$peri
-5.8628448 * rock1$shape

rock1$logistic_h2 <- exp(rock1$h2) / (1 + exp(rock1$h2))

rock1$h3 <- 0.8026366 - 10.9345352 * rock1$area - 4.5783516 * rock1$peri
+ 9.5311123 * rock1$shape

rock1$logistic_h3 <- exp(rock1$h3) / (1 + exp(rock1$h3))

rock1$pred1 <- (1.8866734 - 14.6181959 * rock1$logistic_h1 + 7.3466236 *
rock1$logistic_h2 + 

                8.7655882 * rock1$logistic_h3 - 2.9988287 * rock1$area -
4.2508948 * rock1$peri + 

                4.4397158 * rock1$shape)

rock1$nn.pred <- predict(rock.nn)

head(rock1)      

  perm   area     peri     shape         h1 logistic_h1       h2
logistic_h2        h3  logistic_h3    pred1  nn.pred

1  6.3 0.4990 0.279190 0.0903296 -0.7413656   0.3227056 3.770238
0.9774726 -5.070985 0.0062370903 2.122910 2.122910

2  6.3 0.7002 0.389260 0.1486220 -0.7883026   0.3125333 4.773323
0.9916186 -7.219361 0.0007317343 1.514820 1.514820

3  6.3 0.7558 0.393066 0.1833120 -1.1178398   0.2464122 4.779515
0.9916699 -7.514112 0.0005450367 2.451231 2.451231

4  6.3 0.7352 0.386932 0.1170630 -1.2703391   0.2191992 5.061506
0.9937039 -7.892204 0.0003735057 2.656199 2.656199

5 17.1 0.7943 0.394854 0.1224170 -1.6854993   0.1563686 5.276490
0.9949156 -8.523675 0.0001986684 3.394902 3.394902

6 17.1 0.7979 0.401015 0.1670450 -1.4573040   0.1888800 5.064433
0.9937222 -8.165892 0.0002841023 3.072776 3.072776

The first 6 records show that the numbers from the manual equations and
the predict() function are the same (the last 2 columns). As VR point
out in their book, there are several solutions and a random starting
point and if you run the same example your results may differ.

Hope this helps.

Jude Ryan

Filipe Rocha wrote:

I want to create a neural network, and then everytime it receives new
data,

instead of  creating a new nnet, i want to use a backpropagation
algorithm

to adjust the weights in the already created nn.

I'm using nnet package, I know that nn$wts gives the weights,  but I
cant

find out which weights belong to which conections so I could implement
the

backpropagation algorithm myself.

But if anyone knows some function to  do this, it would be even better.

In anycase, thank you!

Filipe Rocha

___________________________________________
Jude Ryan
Director, Client Analytical Services
Strategy & Business Development
UBS Financial Services Inc.
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account electronically, including but not limited to e-mail, 
fax, text or instant messaging. The information provided in 
this e-mail or any attachments is not an official transaction 
confirmation or account statement. For your protection, do not 
include account numbers, Social Security numbers, credit card 
numbers, passwords or other non-public information in your e-mail. 
Because the information contained in this message may be privileged, 
confidential, proprietary or otherwise protected from disclosure, 
please notify us immediately by replying to this message and 
deleting it from your computer if you have received this 
communication in error. Thank you. 

UBS Financial Services Inc. 
UBS International Inc. 
UBS Financial Services Incorporated of Puerto Rico 
UBS AG

UBS reserves the right to retain all messages. Messages are protected
and accessed only in legally justified cases.