[SciPy-User] optimising python numpy snippet

[Eric Hermes]

Hello,

A quick and dirty simplification may end up in a bit of a speed boost. 
You can eliminate some of the loops and replace them with vector 
operations, as below.

for k in xrange(self.pooled_shape[3]):
     for l in xrange(self.pooled_shape[4]):
         self.pooled[0, :, :, k, l] = numpy.tanh(self.conv_out[0, :, :, 
k*3:(k+1)*3, l*3:(l+1)*3].sum((2, 3))/9. + self.b[:, :])

I have left in some extraneous slices (:) for clarity of code.

Eric

On 7/24/2014 12:40 PM, Sai Rajeshwar wrote:
> hi all,
>
>   I have written the following for loop statement inn my code..  when 
> i profiled it.. i found that it is taking huge amount of time..  can 
> any one suggest to make this statement faster
> ----------------------------------------------------------------------------
>
> f*or i in xrange(self.pooled_shape[1]):
>             for j in xrange(self.pooled_shape[2]):
>                 for k in xrange(self.pooled_shape[3]):
>                     for l in xrange(self.pooled_shape[4]):
> self.pooled[0][i][j][k][l]=math.tanh((numpy.sum(self.conv_out[0][i][j][k*3][l*3:(l+1)*3])+numpy.sum(self.conv_out[0][i][j][k*3+1][l*3:(l+1)*3])+numpy.sum(self.conv_out[0][i][j][k*3+2][l*3:(l+1)*3]))/9.0+self.b[i][j])*
>
> thanks a lot in advance
>
>
> *with regards..*
> *
> *
> *M. Sai Rajeswar*
> *M-tech  Computer Technology*
> *IIT Delhi
> ----------------------------------Cogito Ergo Sum---------
> *
>
>
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> SciPy-User mailing list
> SciPy-User at scipy.org
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-- 
Eric Hermes
J.R. Schmidt Group
Chemistry Department
University of Wisconsin - Madison

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