I think tensordot should do what you want
http://deeplearning.net/software/theano/library/tensor/basic.html#theano.tensor.tensordot
something like
result = T.tensordot(prob, cases, axes=1)
On Friday, May 5, 2017 at 3:17:14 AM UTC-7, Šarūnas S. wrote:
>
> I was shown that in *numpy* I could speed it up in the following way:
>
> result = np.einsum('ijk,ijk->ik', prob, cases)[:,None,:]
>
>
> result = np.matmul(prob.transpose(2,0,1), cases.T).T
>
>
> Bot give me the expected speedup in *numpy*, but neither is implemented
> in *Theano*. Is there a way to do the same in *Theano* on the *GPU*?
>
>
>
> On Friday, 5 May 2017 11:15:26 UTC+3, Šarūnas S. wrote:
>>
>> In my current theano script the bottleneck is equivalent to the following
>> numpy code:
>>
>> import numpy as np
>>
>> # 3D example
>> axis = 0
>> prob = np.random.random( ( 1, 1000, 50 ) )
>> cases = np.random.random( ( 1000, 1000, 50 ) )
>>
>> start = time.time( )
>> for i in xrange( 1000 ):
>> result = ( cases * prob ).sum( axis=1-axis, keepdims=True )
>> print '3D naive method took {} seconds'.format( time.time() - start )
>> print result.shape
>> print
>>
>> I had seen in 2D case that replacing elementwise+sum with a dot product
>> gave me 5x speedup. Are there any theano matrix operations that could help
>> me out here?
>>
>
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