My first Tensorflow project

This script visualizes how a simple network understands an image in grayscale.

before we start lets look at some results then explain them. for the image:

The output images will be:

Now that we could see a possible output of this script we could explain :) The methos is as follows (very abstract):

1. load an image in graysacle
2. build a cnn (from config file)
3. for each apoch:
   3.1 if points refresh is due: refresh random points
   3.2 feed points into cnn and test how close the output (white-black) to the real image pixel on that points
   3.3 minimize the loss (difference from real image values)

output images will be saved where u run this script and config file should be in that folder aswell.

configuration:

img_path: path to the input image

no_of_random_points: number of random points to train on for each refresh

no_of_iteration_before_refresh: number of iterations before each points refresh

hidden_layers: hidden layers (not the input 2 or output 2), example: "hidden_layers": [17,10,17]

learning_rate: gradient descent learning rate

epoch_count: iteration count