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.r...@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
>
>
>
> ___________________________________________
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> UBS Financial Services Inc.
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