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-# GAN & cGAN tutorial.
 
-We recommand to use the notebook (.ipynb) but the Python script (.py) is also provided if more convenient for you.
+# GAN & cGAN tutorial.
 
-# How to submit your Work ?
+Matheus MACHADO E SILVA
 
-This work must be done individually. The expected output is a repository named gan-cgan on https://gitlab.ec-lyon.fr. It must contain your notebook (or python files) and a README.md file that explains briefly the successive steps of the project. The last commit is due before 11:59 pm on Wednesday, March 29, 2023. Subsequent commits will not be considered.
+# GAN 
 
+GAN is a generative adversarial network that we use to generate new handwritten digits, after feeding it with pictures from the MNIST database;
+To reduce the time of execution we use Google Colab GPU;
+The MNIST dataset uses black and white images so to adapt this tutorial to the MNIST database, I changed the number of channels for 1 (in the tutorial was 3 because of RGB data used);
 
-# DCGAN
+# cGAN tutorial.
 
-To reduce the time of execution we use Google Colab GPU;
-The MNIST dataset uses black and white images so the first step is to change the number of channels for 1 (in the tutorial was 3 because of RGB data used);
+cGAN is a conditional generative adversarial network (cGAN) and in this project I trained it to identify if building facades images have generated by a model or are real;
+The data used is from the CMP Facade Database;
+To generate the facade images, a mask, already included in the database, passes through the generator, which delivers as output a believable image.