handling errors well with the neural netork api

working on new python implementation with cleaner code

initial dumb api working

stuck on working with computeOutput function, getting dim error every

mapping activation arg to the actual function

initializing weights and biases

working through Train() function

updated ignore for vim *.swp files when committing from vim terminal

added trainTestSplit() function

adding activation funcs

updated example for new format

significantly decreased the complexity of the API by deciding to make

re configured the Alg interface and the NN sttuct, now we have to work

splitting up code into seperate modules

reads incoming request json if user specifys correctly

initial basic REST API setup

going to use this branch to re make this program in go using the gota

switching back to bas64 encoding/decoding with image byte data

re formatting the api to contain the image bytes in json

stuck

changing the get plot route to return bytes of the image

creating the plot endpoint for getting result images

fixing typos

think I might have to programatically create the path for the images ...

think I was right??

think I have to specify the full path??

more typos

fixing simple typo

instead of returning the image bytes to the api user, we are moving towards hosting them on the backend but we need to be careful moving forwards and make sure that we delete the images after use

got changes working on postman

readme

splitting up rest api functionality to make more readable

reverting more code back to python 3 syntax

having to retract to pyhton 3.7 syntax for render

scikit learn

requirements

updating requirements

back to app.py

getting rid of some more problematic type hints

railway did not like my type hinting

think I have to change it to main .py not app.py for railway

trying without dockerfile

re adding the get request again

server is blocking reqeusts, trying to change cors arguments and stuff

making it only accept post reqeusts

debug false

clustering plot for kmeans

requirements

making better loss history / epoch plot

savving memory with close fig