Re: [theano-users] Re: Theano dot function has different output than Numpy's
One correction above for posterity. The Theano version you posted, f = theano.function([x,y], np.dot(x, y)), does two vector-vector dot products. So it interprets your input matrix as two separate vectors and separately does a dot of each of them with the input vector, y. Also, note that the vectors from the first argument, the vectors from x, is considered a column vector and the second argument, y, is considered a row vector, which is not the case in numpy. -- --- You received this message because you are subscribed to the Google Groups "theano-users" group. To unsubscribe from this group and stop receiving emails from it, send an email to theano-users+unsubscr...@googlegroups.com. For more options, visit https://groups.google.com/d/optout.
Re: [theano-users] Re: Theano dot function has different output than Numpy's
I believe in Theano world, "dot" is only meant for vector-to-vector dot
products. Where as in Numpy world, it as an overloaded operator/function
that is sometimes a matrix-vector product, a matrix-matrix product or a
vector-vector product depending on the input.
On Tue, Oct 2, 2018 at 7:40 PM DL_user wrote:
> I would think z=np.dot(x,y) is more meaningful but anyway apparently dot()
> has different meanings in Theano's world.
>
> thanks
>
> On Tuesday, October 2, 2018 at 3:59:14 PM UTC-7, Buruk Aregawi wrote:
>>
>> It seems that np.dot is interpreting this as the more standard A*x where
>> A is a 2x3 matrix and x is a 3 dimensional vector. Where as theano is
>> interpreting it is X*A where X is a 3x1 matrix and A is a 2x3 matrix.
>> If you do
>> z = T.dot(x,y)
>> instead of
>> z = np.dot(x,y)
>> they will both work the same and the theano function will interpret it as
>> the standard A*x.
>>
>> On Tuesday, October 2, 2018 at 5:55:10 PM UTC-4, DL_user wrote:
>>>
>>> Why do these two functions have different outputs, even both of them
>>> defined from numpy's dot() function:
>>>
>>> x = T.dmatrix('x')
>>>
>>> y = T.dvector('y')
>>>
>>> z = np.dot(x,y)
>>>
>>> f = theano.function([x,y],z)
>>>
>>> f([[1,2,3],[4,5,6]],[7,8,9])
>>> Out[31]:
>>> array([[ 7., 16., 27.],
>>>[28., 40., 54.]])
>>>
>>> np.dot([[1,2,3],[4,5,6]],[7,8,9])
>>> Out[32]: array([ 50, 122])
>>>
>> --
>
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[theano-users] Re: Theano dot function has different output than Numpy's
I would think z=np.dot(x,y) is more meaningful but anyway apparently dot()
has different meanings in Theano's world.
thanks
On Tuesday, October 2, 2018 at 3:59:14 PM UTC-7, Buruk Aregawi wrote:
>
> It seems that np.dot is interpreting this as the more standard A*x where A
> is a 2x3 matrix and x is a 3 dimensional vector. Where as theano is
> interpreting it is X*A where X is a 3x1 matrix and A is a 2x3 matrix.
> If you do
> z = T.dot(x,y)
> instead of
> z = np.dot(x,y)
> they will both work the same and the theano function will interpret it as
> the standard A*x.
>
> On Tuesday, October 2, 2018 at 5:55:10 PM UTC-4, DL_user wrote:
>>
>> Why do these two functions have different outputs, even both of them
>> defined from numpy's dot() function:
>>
>> x = T.dmatrix('x')
>>
>> y = T.dvector('y')
>>
>> z = np.dot(x,y)
>>
>> f = theano.function([x,y],z)
>>
>> f([[1,2,3],[4,5,6]],[7,8,9])
>> Out[31]:
>> array([[ 7., 16., 27.],
>>[28., 40., 54.]])
>>
>> np.dot([[1,2,3],[4,5,6]],[7,8,9])
>> Out[32]: array([ 50, 122])
>>
>
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[theano-users] Re: Theano dot function has different output than Numpy's
It seems that np.dot is interpreting this as the more standard A*x where A
is a 2x3 matrix and x is a 3 dimensional vector. Where as theano is
interpreting it is X*A where X is a 3x1 matrix and A is a 2x3 matrix.
If you do
z = T.dot(x,y)
instead of
z = np.dot(x,y)
they will both work the same and the theano function will interpret it as
the standard A*x.
On Tuesday, October 2, 2018 at 5:55:10 PM UTC-4, DL_user wrote:
>
> Why do these two functions have different outputs, even both of them
> defined from numpy's dot() function:
>
> x = T.dmatrix('x')
>
> y = T.dvector('y')
>
> z = np.dot(x,y)
>
> f = theano.function([x,y],z)
>
> f([[1,2,3],[4,5,6]],[7,8,9])
> Out[31]:
> array([[ 7., 16., 27.],
>[28., 40., 54.]])
>
> np.dot([[1,2,3],[4,5,6]],[7,8,9])
> Out[32]: array([ 50, 122])
>
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