Random in nntrainer about nntrainer

[kparichay]

@zhoonit Yes, #133 is good and is definitely needed.
Thanks for your input, I was not very familiar with databuffer, I will check the corresponding files and then update this issue.

[jijoongmoon]

That's good idea and it will make nntrainer more deterministic. I tried to do this in other ways like #148 which has python code to generate the input, output and golden data. It is used for testing conv2d and pooling layer for forwarding and compared with output from nntrainer. (Tensorflow is used to generate the golden data). However, we do not have tensorflow for tizen so that tar file is used.

[taos-ci]

cibot: Thank you for posting issue #167. The person in charge will reply soon.

[zhoonit]

Setting seeds seems great idea to me (also related to #133 )
Could you check if setting seeds would make a reproducible result as well?
I am concerning that since databuffer is designed to run concurrently, calling order of random() is not deterministic.

[kparichay]

Current major issue is initialization. I will for now make the seed fixed.
Later, maybe add an interface for user to set it.