Regarding Input shape of data into CNN deep learning extension

varunm1varunm1 Member Posts: 497   Unicorn
edited January 13 in Help
Hello

In the current deep learning extension, how is the input shape of CNN considered? In tensorflow, when I train images(converted to pixels) the shape of an array is (nb_samples, rows, columns, channels) for a 2d Conv. How will this happen in CNN of RM? Can we specify the samples? Is there a different convolution 1D or 2D or 3D option that can be chosen which I didn't find in the operator. 

@hughesfleming68 any input on this?

Thanks,
Varun


Regards,
Varun

Answers

  • hughesfleming68hughesfleming68 Member Posts: 214   Unicorn
    Hi Varun, unfortunately I don't have an answer. Perhaps @pschlunder could explain. I have only just started to scratch the surface with some CNN tests on time series problems with DL4J. If anything, I should be asking you questions not the other way around. :smile: Have you taken a look at the DL4J documentation and cheat sheets? https://deeplearning4j.org/docs/latest/deeplearning4j-cheat-sheet#layers-conv


    varunm1
  • varunm1varunm1 Member Posts: 497   Unicorn
    Hi @hughesfleming68

    I have gone through the document, thanks for sharing. It says that normal convolution operator is a 2D, but I am not sure how it's taking (nb_samples,channels, image_rows, image_columns) values. Will try to check it out.

    Thanks,
    Varun
    Regards,
    Varun
  • varunm1varunm1 Member Posts: 497   Unicorn
    @pschlunder can you provide some insights on this? Thanks
    Regards,
    Varun
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