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Face Generation

Project Overview

In this project, I defined and trained a DCGAN on a dataset of faces. The goal of this project is to get a generator network to generate new images of faces that look as realistic as possble. The image below is a result of the training:

Generated Faces

Project Instruction

Instruction

  1. Clone the repository and navigage to the downloaded folder. 
    	git clone https://github.com/ayowolet/Face-Generation
	cd Face-Generation
  2. Open the dlnd_face_generation.ipynb file. The HTML version of the file is also available. 
    	jupyter notebook dlnd_face_generation.ipynb
  3. Read and follow the instructions! This repository does not include the dataset of faces. You can find and download it in the notebook.

Project Information

Contents

  • Pre-processed Data
  • Create a DataLoader
  • Define the Model
    • Discriminator
    • Generator
    • Initialize the weights of your network
    • Build complete network
  • Discriminator and Generator Losses
  • Optimizers
  • Training
  • Training Loss
  • Generator samples from training

Libraries

The list below represents main libraries and its objects for the project.

  • PyTorch (Generator and Discriminator)

Accelerating the Training Process

Training is very time consuming, try to use GPU to train.

Amazon Web Services

You can use Amazon Web Services to launch an EC2 GPU instance. It is not free though.