[mlpack] GSoC 2020: Visualization Tool

[Rahul Prabhu]

Hey Gopi,
Thanks for the interest in this project. I was wondering, to visualize the
neural network, could we not just parse the serialized model returned by
data::Save()?

On Thu, Mar 12, 2020 at 11:59 PM Gopi Manohar Tatiraju <
deathcoderx at gmail.com> wrote:

> Hey,
>
> Regarding Visualization Tool, I think we may need to use one or more
> different libraries to build it, so a discussion regarding the dependencies
> is needed to proceed further.
>
> I took the example of Digit Recogniser
> <https://github.com/mlpack/models/tree/master/Kaggle> and started working
> on it.
>
> I started by visualising the dataset itself. Using OpenCV I wrote code to
> read images from CSV file and display them(OpenCV doesn't have any function
> to read csv files as images).
>
> Now I think another good visual will be a list of all the layers and
> activation function which are used and connections between them. Now we
> have some options to do this:
>
>    1. *Total Naive Approach: *We can use file handling. Our tool will
>    take code file as input. All layers are added like this(Add<Parameter>). We
>    can detect the parameters and using openCV we can arrange them in a graph
>    fashion.
>    2. *A better approach: *A better approach will be to add a variable or
>    function (for ex. FNN class) which keep track of the layers being added and
>    other required parameters. Then we can create an object of visual class,
>    and the FNN class object can be passed to this visual class which then can
>    produce the required visualization.
>
> *Method 1 *maybe not that efficient and is prone to many errors as here
> we also have to ensure code file given by the user contains right code and
> all the connections are properly done. But here we don't need to touch any
> of the base code of the library so required testing will be only be limited
> to Visual Tool Class
>
> *Method 2 *is efficient but changing the base code of the library will
> required extensive testing before we can merge it. Testing will take more
> time here, but using objects can we more beneficial.
>
> I need some views regarding what method should be chosen and how to
> proceed from here. Once the flow is established other parameters like
> accuracy, bias and other parameters can be visualised using graphs. I have
> some parameters in mind for now, we can also take some inspiration from
> tensor-board <https://www.tensorflow.org/tensorboard> for that.
>
> Waiting for suggestion as  I am planning to implement a proof of concept
> so that we can understand the project better.
>
> Regards
> Gopi M Tatiraju
>
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