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 < [email protected]> 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 > > _______________________________________________ > mlpack mailing list > [email protected] > http://knife.lugatgt.org/cgi-bin/mailman/listinfo/mlpack >
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