gan_ensembles

Ensembles of Generative Adversarial Networks as a Data Augmentation Technique for Alzheimer research. Machine Learning Engineer Nanodegree at Udacity - Capstone Project

Setup

# install dependencies
conda create -n gan_ensembles python=3.6.5
conda activate gan_ensembles
pip install -r requirements.txt
conda install pytorch torchvision torchaudio cudatoolkit=11.1 -c pytorch -c nvidia
# start jupyter server
jupyter notebook

Notebooks

Notebooks in this project were designed to be executed in sequence:

1. Download MRIs dataset - http://localhost:8888/notebooks/download_dataset.ipynb
2. Download MRIs dataset - http://localhost:8888/notebooks/data_exploration_viz.ipynb
3. Data preprocessing and Data splitting - http://localhost:8888/notebooks/data_preproc_split.ipynb
4. DCGAN implementation and Control Model (CM) training - http://localhost:8888/notebooks/dcgan_control_model.ipynb
5. DCGAN Ensemble Model 1 (eGANs) training - http://localhost:8888/notebooks/dcgan_ensemble_model_1.ipynb
6. DCGAN Ensemble Model 2 (seGANs) training - http://localhost:8888/notebooks/dcgan_ensemble_model_2.ipynb
7. DCGAN Ensemble Model 3 (cGANs) training - http://localhost:8888/notebooks/dcgan_ensemble_model_3.ipynb
8. Metrics visualisation and conclusion - http://localhost:8888/notebooks/metrics_viz_outro.ipynb

Docs

Original proposal, corrected proposal, and final project report included in capstone_project_docs folder.