A TensorFlow implementation of the PixelCNN.
There are three different datasets that the model is intended to be tested on: MNIST, Frey Face, and CIFAR-10 dataset.
To train and test the model, run the command: python main.py [--MNIST | --FREY | --CIFAR].
The model was trained for 25 epochs on a binarized version of the MNIST dataset.
The model was able to reach a negative log-likelihood score of 80.97 nats.
Incomplete Images:
Completed Images:
Incomplete Images:
Completed Images (Current):
Currently a work in progress...
The original PixelCNN paper was written by Aaron van den Oord, Nal Kalchbrenner, and Koray Kavukcuoglu.
The paper can be found here: Pixel Recurrent Neural Networks.