[SciPy-Dev] scipy improve performance by parallelizing

[Sai Rajeshwar]

ok i guess axis=-1 option is not required..  that solved it

*with regards..*

*M. Sai Rajeswar*
*M-tech  Computer Technology*


*IIT Delhi----------------------------------Cogito Ergo Sum---------*


On Thu, Jul 24, 2014 at 11:04 PM, Sai Rajeshwar <rajsai24 at gmail.com> wrote:

> hi david,
>
> tried as you suggested
> ----------------------------------------------------------------
>
> )for i in xrange(pooled_shape[1]):
>             for j in xrange(pooled_shape[2]):
>                 for k in xrange(pooled_shape[3]):
>                     for l in xrange(pooled_shape[4]):
>                         pooled[0][i][j][k][l]=math.
>>
>> tanh((numpy.sum(conv_out[0][i][j][k*3][l*3:(l+1)*3])+numpy.
>> sum(conv_out[0][i][j][k*3+1][l*3:(l+1)*3])+numpy.sum(conv_
>> out[0][i][j][k*3+2][l*3:(l+1)*3]))/9.0+b[i][j])
>
>
> You should get a speed up by accessing the arrays in a more efficient way:
>
> pooled[0, i, j, k, l] = math.tanh((numpy.sum(conv_out[0, i, j, k*3,
> l*3:(l+1)*3]) + numpy.sum(conv_out[0, i, j, k*3+1, l*3:(l+1)*3]) +
> numpy.sum(conv_out[0, i, j, k*3+2, l*3:(l+1)*3]))/9.0+b[i, j])
>
> In fact:
>
> numpy.sum(conv_out[0, i, j, k*3, l*3:(l+1)*3]) + numpy.sum(conv_out[0, i,
> j, k*3+1, l*3:(l+1)*3])
>
> seems equivalent to:
>
> numpy.sum(conv_out[0, i, j, k*3: k*3 +1, l*3:(l+1)*3])
>
> To take the last one into account:
>
> vec = numpy.sum(conv_out[0, i, j, k*3: k*3 + 2, l*3:(l+1)*3], axis=-1)
> pooled[0, i, j, k, l] = vec[0] + vec[1] + vec[2] / 9.0
>
> And you can probably get rid of the i and j indexes all together.
> Something like this should work (untested):
>
> for k in...
> for l in...
> output = numpy.sum(conv_out[0, :, :, k*3: k*3 +1, l*3:(l+1)*3]), axis=-1)
> output += numpy.sum(conv_out[0, :, :, k*3 + 2, l*3 : (l+1)*3])),
> axis=-1)/9.0
>  output += b
> pooled[0, :, :, k, l] = numpy.tanh(output)
> -----------------------------------------------------------------
>
>
> for 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]):
>
>                         #-- commented--
> 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])
>
>                         vec = numpy.sum(self.conv_out[0, i, j, k*3: k*3 +
> 2, l*3:(l+1)*3], axis=-1)
>                         self.pooled[0, i, j, k, l] = math.tanh((vec[0] +
> vec[1] + vec[2] )/ 9.0+self.b[i][j])
>
>
> but it gave following error
>
> ----------------------------------------------------------------------------------
> Traceback (most recent call last):
>   File "3dcnn_test.py", line 401, in <module>
>     check()
>   File "3dcnn_test.py", line 392, in check
>     layer1.change_input(numpy.reshape(test_set_x[i],(1,1,9,60,80)))
>   File "3dcnn_test.py", line 77, in change_input
>     self.pooled[0, i, j, k, l] = math.tanh((vec[0] + vec[1] + vec[2] )/
> 9.0+self.b[i][j])
> IndexError: index out of bounds
>
> *with regards..*
>
> *M. Sai Rajeswar*
> *M-tech  Computer Technology*
>
>
> *IIT Delhi----------------------------------Cogito Ergo Sum---------*
>
>
> On Sun, Jul 13, 2014 at 9:08 PM, Daπid <davidmenhur at gmail.com> wrote:
>
>>
>> On 13 July 2014 14:28, Sai Rajeshwar <rajsai24 at gmail.com> wrote:
>>
>>>
>>> 2)for i in xrange(pooled_shape[1]):
>>>             for j in xrange(pooled_shape[2]):
>>>                 for k in xrange(pooled_shape[3]):
>>>                     for l in xrange(pooled_shape[4]):
>>>
>>> pooled[0][i][j][k][l]=math.tanh((numpy.sum(conv_out[0][i][j][k*3][l*3:(l+1)*3])+numpy.sum(conv_out[0][i][j][k*3+1][l*3:(l+1)*3])+numpy.sum(conv_out[0][i][j][k*3+2][l*3:(l+1)*3]))/9.0+b[i][j])
>>
>>
>> You should get a speed up by accessing the arrays in a more efficient way:
>>
>> pooled[0, i, j, k, l] = math.tanh((numpy.sum(conv_out[0, i, j, k*3,
>> l*3:(l+1)*3]) + numpy.sum(conv_out[0, i, j, k*3+1, l*3:(l+1)*3]) +
>> numpy.sum(conv_out[0, i, j, k*3+2, l*3:(l+1)*3]))/9.0+b[i, j])
>>
>> In fact:
>>
>> numpy.sum(conv_out[0, i, j, k*3, l*3:(l+1)*3]) + numpy.sum(conv_out[0, i,
>> j, k*3+1, l*3:(l+1)*3])
>>
>> seems equivalent to:
>>
>> numpy.sum(conv_out[0, i, j, k*3: k*3 +1, l*3:(l+1)*3])
>>
>> To take the last one into account:
>>
>> vec = numpy.sum(conv_out[0, i, j, k*3: k*3 + 2, l*3:(l+1)*3], axis=-1)
>> pooled[0, i, j, k, l] = vec[0] + vec[1] + vec[2] / 9.0
>>
>> And you can probably get rid of the i and j indexes all together.
>> Something like this should work (untested):
>>
>> for k in...
>> for l in...
>> output = numpy.sum(conv_out[0, :, :, k*3: k*3 +1, l*3:(l+1)*3]), axis=-1)
>> output += numpy.sum(conv_out[0, :, :, k*3 + 2, l*3 : (l+1)*3])),
>> axis=-1)/9.0
>> output += b
>> pooled[0, :, :, k, l] = numpy.tanh(output)
>>
>> In this case, one of the loops seems a great target for parallelisation.
>> Also, Cython should help reduce the loop overhead.
>>
>> _______________________________________________
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>> SciPy-Dev at scipy.org
>> http://mail.scipy.org/mailman/listinfo/scipy-dev
>>
>>
>
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