Is it possible to output vectors/scalars from a neural network that are of a different type than the input











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I have just begun researching convolutional neural networks on images and I see that they are useful for processes such as feature recognition and de noising etc. as they apply transformations to the input images.



What I want to know if there is a way to input a 2D matrix of identical signal traces out of phase along one axis (example in image); and output the amount that each trace moved in order for these traces to be in phase. So instead of outputting a transformation of the input, it would output vectors of the amount of transformation at each cell in the y direction. The input and output matrix sized could be equivalent I think.



example of a set of out of phase signals










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    down vote

    favorite












    I have just begun researching convolutional neural networks on images and I see that they are useful for processes such as feature recognition and de noising etc. as they apply transformations to the input images.



    What I want to know if there is a way to input a 2D matrix of identical signal traces out of phase along one axis (example in image); and output the amount that each trace moved in order for these traces to be in phase. So instead of outputting a transformation of the input, it would output vectors of the amount of transformation at each cell in the y direction. The input and output matrix sized could be equivalent I think.



    example of a set of out of phase signals










    share|cite|improve this question







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      up vote
      0
      down vote

      favorite









      up vote
      0
      down vote

      favorite











      I have just begun researching convolutional neural networks on images and I see that they are useful for processes such as feature recognition and de noising etc. as they apply transformations to the input images.



      What I want to know if there is a way to input a 2D matrix of identical signal traces out of phase along one axis (example in image); and output the amount that each trace moved in order for these traces to be in phase. So instead of outputting a transformation of the input, it would output vectors of the amount of transformation at each cell in the y direction. The input and output matrix sized could be equivalent I think.



      example of a set of out of phase signals










      share|cite|improve this question







      New contributor




      rbschris is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
      Check out our Code of Conduct.











      I have just begun researching convolutional neural networks on images and I see that they are useful for processes such as feature recognition and de noising etc. as they apply transformations to the input images.



      What I want to know if there is a way to input a 2D matrix of identical signal traces out of phase along one axis (example in image); and output the amount that each trace moved in order for these traces to be in phase. So instead of outputting a transformation of the input, it would output vectors of the amount of transformation at each cell in the y direction. The input and output matrix sized could be equivalent I think.



      example of a set of out of phase signals







      machine-learning neural-networks artificial-intelligence






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      share|cite|improve this question







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      asked Nov 20 at 20:18









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