• project implemented as part of the Deep Learning Methods course attended on MSc degree of Data Science at the Faculty of Mathematics and Information Sciences in Warsaw University of Technology,
• the implemented architectures are DC-GAN and MLP-GAN,
• written in TensorFlow r1.13.

• Google Colab access,
• Google Storage Bucket with public access.

There are three files for Google Colab, which are described below:

• Google_Colab_Train_eval_predict.ipynb - main notebook which allows to train GAN model, make small evaluation and generate images based on noise,
• Google_Colab_Most_similar.ipynb - after training model you can use this notebook to find most similar images in training set to that which your model can generate,
• Google_Colab_Latent_space_interpolation.ipynb - this notebook can ensure you that the GAN model has learnt some deep knowledge about the training set examples.

In order to run any of script please do following:

1. Open Google_Colab_*.ipynb in Google Colab by clicking any of these links: train_eval_predict, most_similar, latent_space_interpolation,
2. set Google Colab environment to support TPU computation,
3. modify variables placed in the first cell of each notebook,
4. run all cells,
5. after all cells execution, please take a look either on the cells output or the Google Storage Bucket, which you provided to store model's checkpoints and generated images in.

Animation showing generated images during training of MLP-GAN and DC-GAN architectures:

1. MLP-GAN:

   

2. DC-GAN:

   

Image generated for 1000-epoch trained DC-GAN using Google_Colab_Latent_space_interpolation.ipynb:

Image generated for 1000-epoch trained DC-GAN using Google_Colab_Most_similar.ipynb:

For more results and diagrams of architectures please read Project_Final_Report_PL.pdf file.