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pixelcnn_keras

This is keras implementation of Gated PixelCNN model as proposed in Conditional Image Generation with PixelCNN Decoders.

Requirements

Python

  • Version: 2.7.X or 3.4.X

TensorFlow

  • Version: 1.0.0rc1
  • GPU: enabled

Keras

  • Version: 1.2.2

other packages

  • graphviz

other python dependencies

  • scipy==0.18.1
  • numpy==1.12.0
  • scikit-image==0.12.3
  • h5py==2.6.0
  • pydot-ng==1.0.0

Setup with Docker

This repository contains Dockerfile of execution environment. Docker image is registered using automated build.

Base Docker Image

My Docker Image

Requirements

  • Linux OS
  • GPU
  • NVIDIA Driver
  • Docker
  • NVIDIA-Docker

Installation

  1. Install Docker
  2. Install NVIDIA-Docker
  3. Get docker image: docker pull suga93/pixelcnn_keras
  4. Run docker image: nvidia-docker run -it suga93/pixelcnn_keras

Usage

Train (conditional) Gated PixelCNN model using keras datasets

Show help message:

python3 train_keras_datasets.py -h

Train unconditional sample mnist model (set config options):

python3 train_keras_datasets.py -c configs/sample_train_mnist_small.cfg

Train mnist model with overwriting some config options: (ex. unconditional -> conditional)

python3 train_keras_datasets.py -c configs/sample_train_mnist_small.cfg --conditional True

Train cifar10 model:

python3 train_keras_datasets.py -c configs/sample_train_cifar10.cfg

Generate images from trained model

  • In this implementation, Keras fails to save model architecture (model.save(), model.to_json(), model.to_yaml()). Therefore, first you need to build same architecture as your trained model.

Show help message:

python3 predict_keras_datasets.py -h

Generate mnist images from trained model:

python3 predict_keras_datasets.py -c configs/sample_predict_mnist.cfg --checkpoint_file /path/to/trained_model_mnist.hdf5

Generate cifar10 images from trained model:

python3 predict_keras_datasets.py -c configs/sample_predict_cifar10.cfg --checkpoint_file /path/to/trained_model_cifar10.hdf5

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Keras implementation of Gated PixelCNN

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