Thank you very much for the reply.
In the script, I'm doing a 3d flip-type convolution between a 3*2*2
(time*width*height) signal x:
array([[[[[ 0., 1.],
[ 2., 3.]]],
[[[ 4., 5.],
[ 6., 7.]]],
[[[ 8., 9.],
[ 10., 11.]]]]], dtype=float32)
and a 3*2*2 kernel W:
array([[[[[ 0., 0.],
[ 0., 0.]]],
[[[ 1., 0.],
[ 0., 0.]]],
[[[ 0., 0.],
[ 0., 0.]]]]], dtype=float32)
(to fit conv3d, the shape of x and W is (1,3,1,2,2).)
The correct 3d convolution result is 7 which is exactly the result of
conv3D. However, conv3d2d.conv3d gives 11 on gpu. Besides, if I change W to
array([[[[[ 0., 0.],
[ 0., 0.]]],
[[[ 0., 0.],
[ 0., 0.]]],
[[[ 1., 0.],
[ 0., 0.]]]]], dtype=float32)
conv3D gives 3 but conv3d2d.conv3d still gives 11. If I use cpu, both
conv3D and conv3d2d.conv3d give the same result. I'm wondering if this
problem could be reproduced.
On Fri, Jul 15, 2016 at 2:28 PM, Frédéric Bastien <
[email protected]> wrote:
> I don't have time to investigate. Can you give the output of your script?
>
> On the GPU, the operation aren't done in the same order. Due to float
> a+(b+c) !- (a+b)+c. So this could be the reason.
>
> How big is the difference?
>
> On Thu, Jul 14, 2016 at 5:21 AM, Wang Xiyuan <[email protected]>
> wrote:
>
>> Hello, everyone.
>>
>> I was testing theano.tensor.nnet.conv3D
>> and theano.tensor.nnet.conv3d2d.conv3d on gpu, I expected that the two
>> functions give the same result. However, I found that when the input data
>> size is identical to the filter size and the output is a scale, the results
>> are different. When I switched to cpu, the results become the same. I'm
>> quite puzzled. I guess it has something to do with cudnn.
>>
>> But is there any way to fix this?
>>
>> I'm using Theano 8.2, cudnn v4, and cuda 7.5. and my OS is centos 6.5. A
>> simple example is in the attached script. (This script is actually from
>> adapting the one in the post:
>>
>> https://groups.google.com/forum/#!msg/theano-users/1S9_bZgHxVw/0cQR9a4riFUJ
>> )
>>
>> Thank you every much.
>>
>> Xiyuan
>>
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