- website that lets you draw a number in a field
- neural network (python) guessing the number after every stroke
- show all possibilities for 0-9
- ability to train the AI
- by clicking correct answer (no matter if guess was right or wrong)
- app.py can be changed to always start with the better of initial/trained model (
###comments)
- ability to compare initial model with trained one
- show loss functions by clicking eval button
- initial model depends on app.py > stupid, genius, trained
In the 'shipped' version i decided to always load the most stupid model (initialized with random parameters).
Like the heading 'AI TRAINER 3000' suggests, you are now able to train a model that has never seen numbers, to a model that recognizes them after a few trainings.
I am aware that one can always click the training buttons, no matter if they are wrong, or there isn't even a number drawn. But if a user decides to do this, he/she will just have a more stupid model and would be responsible for it.
(i tried out different settings to really get the feeling that bad training results in a bad network, even though 'good data' from mnist gets included in every train - depends on weight of user-inputs, sample size of mnist, number of training epochs)
The training always takes all weighted user samples since site refresh and n random samples from the mnist data set. Although i am aware that shuffling mnist data set on every train takes much time, this is the result of experimenting with different training sets and i think the current version gives a good sense of training with small sample sizes for the user.
I kept the styling (CSS) simple and am also aware that the site might not be scalable but my focus was on the following lessons:
- sending data from frontend to backend and reverse
- single site application (no reloading / subsites)
- using, training, evaluating neural network
- making the site robust (i.e. handling inputs, while training)
- initial model
- user input weight
- mnist size on training
- training epochs
app.py
- flask app
model_init.ipynb
- model built and config
/models
- model saves
/static & /templates
- JavaScript, CSS, icon, html
- python
- numpy
- flask
- tensorflow keras
- jupyter notebook
- JavaScript
- asynchronous, fetch API
- JSON
- HTML
- templating (JINJA)
- CSS
