daQuincy / PixelRNN

Accompanying repo for the O'Reilly Article, `Using Tensorflow to Generate Images with PixelRNNs`

Using TensorFlow to Generate Images with PixelRNNs

This repo hosts the code associated with the O'Reilly article, "Using Tensorflow to Generate Images with PixelRNNs: How to generate novel images using neural networks".

In this article, we walk through creating a generative model to produce realistic-looking images using recurrent neural networks in Tensorflow. Specifically, we use a PixelRNN architecture trained on MNIST to generate images that look like handwritten digits.

Requirements

In order to run this notebook, you will need to install TensorFlow v1.0, Jupyter, and NumPy.

The notebook also uses TQDM to display nice progress bars during training.

There are two easy ways to install these libraries and their dependencies:

Option A: use the provided Dockerfile configured for this notebook

1. Download and unzip this entire repo from GitHub, either interactively, or by entering

   git clone https://github.com/philkuz/PixelRNN.git

2. Open your terminal and use cd to navigate into the top directory of the repo on your machine

3. To build the Dockerfile, enter

   docker build -t pixelrnn_dockerfile .

   If you get a permissions error on running this command, you may need to run it with sudo:

   sudo docker build -t pixelrnn_dockerfile .

4. Run Docker from the Dockerfile you've just built

   docker run -it -p 8888:8888 -p 6006:6006 pixelrnn_dockerfile bash

   or

   sudo docker run -it -p 8888:8888 -p 6006:6006 pixelrnn_dockerfile bash

   if you run into permission problems.

5. Launch Jupyter by entering

   jupyter notebook

   and, using your browser, navigate to the URL shown in the terminal output (usually http://localhost:8888/)

Option B: install Anaconda Python, TensorFlow, and TQDM manually

NumPy can be tricky to install manually, so we recommend using the managed Anaconda Python distribution, which includes NumPy, Matplotlib, and Jupyter in a single installation. The Docker-based method above is much easier, but if you have a compatible NVIDIA GPU, manual installation makes it possible to use GPU acceleration to speed up training.

1. Follow the installation instructions for Anaconda Python. We recommend using Python 3.6.

2. Follow the platform-specific TensorFlow installation instructions. Be sure to follow the "Installing with Anaconda" process, and create a Conda environment named tensorflow.

3. If you aren't still inside your Conda TensorFlow environment, enter it by typing

   source activate tensorflow

4. Install TQDM by entering

   pip install tqdm

5. Download and unzip this entire repo from GitHub, either interactively, or by entering

   git clone https://github.com/philkuz/PixelRNN.git

6. Use cd to navigate into the top directory of the repo on your machine

7. Launch Jupyter by entering

   jupyter notebook

   and, using your browser, navigate to the URL shown in the terminal output (usually http://localhost:8888/)