R help - Dec 2012 - multi dimensional optim problem

I am attempting to use optim to solve a neural network problem.  I would like to
optimize coefficients that are currently stored in a matrix

Y=270 x 1
X= 27- x 14
b1= 10x14
b2= 11x1
V= 10 x 14 set of prior variances.

I have the following function:

posterior.mode1=function(y,X,b_0,b2,V) {

  log.like=function(b1) {
    a_g=compute(b1)
    z_g=tanh(a_g);
    z_g=cbind(1,z_g)
    p=softmax(z_g%*%b2);
    a=sum(y*log(p)+(1-y)*log(1-p));
    return(a);
  }

  compute=function(b1) {
    a_g=NULL;
    for(i in 1:nrow(b1)){
      a_g=cbind(a_g,X%*%b1[i,])
    }
    return(a_g);
  }

  log.posterior=function(b1) {
    -log.like(b1)+1/2*t(as.vector(b1))%*%diag(as.vector(V))%*%as.vector(b1)
  }

  a=optim(b_0,log.posterior,method="CG",hessian=TRUE)
  return(a);
}

When I run

posterior.mode1(y,X,b1,b2,b1)



I get the following error



Error in 1:nrow(b1) : argument of length 0

Any comments would be very helpful

Thanks



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Forgot to cc the help list.

On 18-12-2012, at 19:40, Lu, James T wrote:
> I am attempting to use optim to solve a neural network problem.  I would
like to optimize coefficients that are currently stored in a matrix
> 
> Y=270 x 1
> X= 27- x 14
> b1= 10x14
> b2= 11x1
> V= 10 x 14 set of prior variances.
> 
> I have the following function:
> 
> posterior.mode1=function(y,X,b_0,b2,V) {
> 
> log.like=function(b1) {
>   a_g=compute(b1)
>   z_g=tanh(a_g);
>   z_g=cbind(1,z_g)
>   p=softmax(z_g%*%b2);
>   a=sum(y*log(p)+(1-y)*log(1-p));
>   return(a);
> }
> 
> compute=function(b1) {
>   a_g=NULL;
>   for(i in 1:nrow(b1)){
>     a_g=cbind(a_g,X%*%b1[i,])
>   }
>   return(a_g);
> }
> 
> log.posterior=function(b1) {
>   -log.like(b1)+1/2*t(as.vector(b1))%*%diag(as.vector(V))%*%as.vector(b1)
> }
> 
> a=optim(b_0,log.posterior,method="CG",hessian=TRUE)
> return(a);
> }
> 
> When I run
> 
> posterior.mode1(y,X,b1,b2,b1)
> 
> 
> 
> I get the following error
> 
> 
> 
> Error in 1:nrow(b1) : argument of length 0
optim  treats parameter b_0 as a vector and passes as such to the function it is
trying to optimize.
So in log.like you should convert b1 back to a matrix of the correct dimensions
like so

  b1 <- matrix(b1,nrow=10)

Then it should work.
Note: you haven't provided function softmax.

Berend