[mlpack] GSoC 2020: Visualization Tool

[Gopi Manohar Tatiraju]

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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