TensorFlow implementation of Pixel Recurrent Neural Networks. This implementation contains:
- PixelCNN
- Masked Convolution (A, B)
- PixelRNN
- Row LSTM (in progress)
- Diagonal BiLSTM (skew, unskew)
- Residual Connections
- Multi-Scale PixelRNN (in progress)
- 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