Hey Rahul, If you don't mind me asking, are you mentoring this project? Coz it was not listed on the idea page and there are many things which I would I like to discuss about this project from a mentor's perspective. About serialized model, I need to go through the saved .h5 file to see how exactly we can use it. Also I am just trying to determine what all can be included in this project, I am yet to decide how to implement these things coz there are many options available. As it was mentioned on the idea page that proof of concept is required so I am just working on determining the outlines of the project first,
Regards. Gopi M Tatiraju On Fri, Mar 13, 2020 at 1:35 AM Rahul Prabhu <cupertin...@gmail.com> wrote: > 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 < > deathcod...@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 >> >> _______________________________________________ >> mlpack mailing list >> mlpack@lists.mlpack.org >> http://knife.lugatgt.org/cgi-bin/mailman/listinfo/mlpack >> >
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