<gpu> and <cpu> generates totally different results

[DrustZ]

Help.
I improved the /guide/neuralnet/convnet.cu, add my own function
when I use -cpu parameter, things go well , the error rate is declining
however, -gpu generates totally different results: the error just stays at 0.9 or so(didn't change a little bit)...
It may seems there's a bug in mshadow's gpu implementation, but I don't know.

I didn't add my own gpu code, just use mshadow's(like <xpu>).

for cpu , I used blas lib. And my cuda version is 7.0.
Please help.
@tqchen @antinucleon

[DrustZ]

besides, when I change the configuration of stride, ksize and pad, the outputs are also not the same.
some configuration work well on both cpu and gpu, but some only on cpu.

[DrustZ]

Is there any probability that GPU and CPU performs differently on = and Copy?
I use a lot of = between tensor and expression, following the original convent version.
And I also use Copy, which is used when = can't work (like tensor[i] = tensor2.Slice(a, a+1) or tensor[i] = tensor2[i]
What's more, I used expression like tensor.Slice(i, i+1) = some expression, is this valid? or do I must turn it into Copy(tensor.Slice(i, i+1) , exp, stream_)?

Please help.
@tqchen @antinucleon

[DrustZ]

I set all tensor's stream, and in GPU mode it didn't report error.
Is there a bug or other reason that GPU and CPU performs differently?

[DrustZ]

Besides, I used a std::vector to store tensors,
like vector[i] = tensor

[DrustZ]

I'll be really really grateful if someone has some idea to help me.

[antinucleon]

Could post you code with gist so I can have a glance?

[tqchen]

Note there is a special semantics on = in mshadow. So when you assign a tensor to another, it is a pointer copy instead of a assignment. So if you intended it be be an assignment, use

Copy(dst, src);

Or

dst = F<op::identity>(src);

Or

dst =1.0f * src;

[szha]

This code base has been donated to the Apache MXNet project per #373, and repo is deprecated. Future development and issue tracking should continue in Apache MXNet.