## Solution
We can upload images to the server that with classify them with a neural network.
We have to submit an image that has a gud_prob > 0.99.
We are given the source code.
There is a lot of literature (research papers) about the subject of visualizing or even reversing a neural network, but I didn't find something easy to use.
Armed with ingenuity I decided to do it by hand.
Deploy the service locally and add a couple of debugging logs- weights of various layers- output values- normalized image
At first I tried with some simple images like pure white and pure black, and get a gud_prob of around 0.5-0.6.
I noticed that in the normalized white image there were some different values for the different colour channels of the image.
I took note of the ratios and made an image filled with a colour that respected those ratios (dark grey) and got a gud_prob of 10 ^ -40 and every value in the normalized image was negative.
So I conjectured that the image had to be overall somewhat bright (mostly positive values).
I inverted the colour and got a score of 0.7.
I then tried to play with the scoring system one paint stroke at a time.
After each edit we submit and observe the change in the score, decide if we want to revert and the next edit.
## The final result
It doesn't work every time, but one time is enough.
![Working image: Light Grey background, red dots and some blue](https://raw.githubusercontent.com/TheRomanXpl0it/TheRomanXpl0it.github.io/8d265a9739e3a6bbe6401fed131b6117cf5d611f/assets/acebear/black.png)