# How to use Before / After images to train a model

I am trying to create a model that can clean pictures of noise, blur, high luminosity etc, but I do not know how to do that. I have tried to search for it a lot, and I couldn't find anything that could properly teach me, as I am very new in this.

I have tried to do something using Google Colaboratory so that people can look it up and help me a bit: Image Cleaning Modedl

At first, I download a file containing 5000+ clean pictures and their degraded version. Then, I load a training set of 500 pictures in two ndarray datasets called degraded_dataset and clean_dataset of shape=(500, 576, 720, 3).

Then, I do not know what to do with these two datasets, I do not know how to train a model. I vaguely have an idea, which is giving the clean_dataset to the model as the label_dataset, however I am not sure it is the good way of doing it.

I followed these tutorials: Tensorflow CNN & Kaggle "From images to narray"

Thank you for helping me.

• Hi I think you might try asking on StackOverflow. This community doesn't focus on implementation (see ai.stackexchange.com/help/on-topic). I'd also recommend A. Ng Deep Learning Specialization to learn how to do these sort of things. – respectful Jan 20 '20 at 3:26