nsanghi / DLND-P4-face-generation

Solution to GAN Face Generation project 4 of Udacity DLND

DLND-P4-dlnd-face-generation

Solution to RNN base TV Script generation for Udacity DLND

Introduction

In this project, we will generate faces using DCGAN. We will be using a dataset celebrity faces. The Neural Network we build will use a convolutional GAN to generate new faces.

1. Installation

Download the latest version of miniconda that matches your system.

	Linux	Mac	Windows
64-bit	64-bit (bash installer)	64-bit (bash installer)	64-bit (exe installer)
32-bit	32-bit (bash installer)		32-bit (exe installer)

Install miniconda on your machine. Detailed instructions:

• Linux: http://conda.pydata.org/docs/install/quick.html#linux-miniconda-install
• Mac: http://conda.pydata.org/docs/install/quick.html#os-x-miniconda-install
• Windows: http://conda.pydata.org/docs/install/quick.html#windows-miniconda-install

2. Create and Activate the Environment

For Windows users, these following commands need to be executed from the Anaconda prompt as opposed to a Windows terminal window. For Mac, a normal terminal window will work.

Git and version control

These instructions also assume you have git installed for working with Github from a terminal window, but if you do not, you can download that first with the command:

conda install git

If you'd like to learn more about version control and using git from the command line, take a look at our free course: Version Control with Git.

Now, we're ready to create our local environment!

1. Clone the repository, and navigate to the downloaded folder. This may take a minute or two to clone due to the included image data.

git clone https://github.com/nsanghi/DLND-P4-face-generation
cd DLND-P4-face-generation

2. Create (and activate) a new environment, named deep-learning with Python 3.6. If prompted to proceed with the install (Proceed [y]/n) type y.

   • Linux or Mac:

   conda create -n deep-learning python=3.6
   source activate deep-learning
   

   • Windows:

   conda create --name deep-learning python=3.6
   activate deep-learning
   

   At this point your command line should look something like: (deep-learning) <User>:DLND-P4-face-generation <user>$. The (deep-learning) indicates that your environment has been activated, and you can proceed with further package installations.

3. Install PyTorch and torchvision; this should install the latest version of PyTorch.

   • Linux or Mac:

   conda install pytorch torchvision -c pytorch 
   

   • Windows:

   conda install pytorch -c pytorch
   pip install torchvision
   

4. Install a few required pip packages, which are specified in the requirements text file.

pip install -r requirements.txt

7. That's it!

Now most of the deep-learning libraries are available to you.

Noe, assuming your deep-learning environment is still activated, you can navigate to the main repo and start looking at the notebooks:

cd
cd DLND-P4-face-generation
jupyter notebook

To exit the environment when you have completed your work session, simply close the terminal window.

2. Running the Code

Open the jupyter notebook dlnd_face_generation.ipynb which contains complete implementation of the project.