jakebelew / gated-pixel-cnn

A TensorFlow implementation of the gated variant of PixelCNN (Gated PixelCNN) from "Conditional Image Generation with PixelCNN Decoders" (https://arxiv.org/abs/1606.05328).
Apache License 2.0
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Gated PixelCNN

A TensorFlow implementation of the gated variant of PixelCNN (Gated PixelCNN) from "Conditional Image Generation with PixelCNN Decoders" (https://arxiv.org/abs/1606.05328). The Gated PixelCNN matches the log-likelihood of PixelRNN on both CIFAR and ImageNet while requiring less than half the training time.

Training the Network

MNIST (default)

python main.py

Color-MNIST

python main.py --data color-mnist --gated_conv_num_layers 7 --gated_conv_num_feature_maps 48 --output_conv_num_feature_maps 96 --q_levels 4

CIFAR-10

python main.py --data cifar --gated_conv_num_layers 15 --gated_conv_num_feature_maps 126 --output_conv_num_feature_maps 1020 --q_levels 256
Configuration Parameter Quick MNIST MNIST COLOR-MNIST CIFAR-10 Description
batch_size 100 100 100 100 Size of a batch.
gated_conv_num_layers 1 7 7 15 The number of gated conv layers.
gated_conv_num_feature_maps 4 16 48 126 (128 in paper) The number of input / output feature maps in gated conv layers. Must be multiple of two, should be multiple of two times num_channels.
output_conv_num_feature_maps 16 32 96 1020 (1024 in paper) The number of output feature maps in output conv layers. Must be multiple of two, should be multiple of two times num_channels.
q_levels 4 4 4 256 The number of quantization levels in the output.
data "mnist" "mnist" "color-mnist" "cifar" Name of dataset.

Gated Convolutional Layers

architecture

activation_unit

References