TensorFlow implementation of Pixel Recurrent Neural Networks. This implementation contains:

1. PixelCNN

• Masked Convolution (A, B)

2. PixelRNN

• Row LSTM (in progress)
• Diagonal BiLSTM (skew, unskew)
• Residual Connections
• Multi-Scale PixelRNN (in progress)

3. Datasets

• MNIST
• cifar10 (in progress)
• ImageNet (in progress)

• Python 2.7
• Scipy
• TensorFlow 0.9+

First, install prerequisites with:

$ pip install tqdm gym[all]

To train a pixel_rnn model with mnist data (slow iteration, fast convergence):

$ python main.py --data=mnist --model=pixel_rnn

To train a pixel_cnn model with mnist data (fast iteration, slow convergence):

$ python main.py --data=mnist --model=pixel_cnn --hidden_dims=64 --recurrent_length=2 --out_hidden_dims=64

To generate images with trained model:

$ python main.py --data=mnist --model=pixel_rnn --is_train=False

Samples generated with pixel_cnn after 50 epochs.

Below results uses two different parameters

[1] --hidden_dims=16 --recurrent_length=7 --out_hidden_dims=32
[2] --hidden_dims=64 --recurrent_length=2 --out_hidden_dims=64

Training results of pixel_rnn with [1] (yellow) and [2] (green) with epoch as x-axis:

Training results of pixel_cnn with [1] (orange) and [2] (purple) with epoch as x-axis:

Training results of pixel_rnn (yellow, green) and pixel_cnn (orange, purple) with hour as x-axis:

• Pixel Recurrent Neural Networks
• Conditional Image Generation with PixelCNN Decoders
• Review by Kyle Kastner
• igul222/pixel_rnn
• kundan2510/pixelCNN

Taehoon Kim / @carpedm20