Keras implementation of DenseNets

Original paper: https://arxiv.org/abs/1608.06993
Original implementation: https://github.com/liuzhuang13/DenseNet

@inproceedings{
  huang2017densely,
  title={Densely connected convolutional networks},
  author={Huang, Gao and Liu, Zhuang and van der Maaten, Laurens and Weinberger, Kilian Q },
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  year={2017}
}

Introduction to each folder and file:
  "Data": put the CIFAR-100 data set here
  "log": training log for model with no augmentation, relates to main.py
  "Pictures": the place store generated visualized samples, relates to get_img_samples_and_conv_results.py
  "Preprocess": the folder stores pre-processing files
    load_data.py: load CIFAR-100 data set
    normalize.py: functions used to normalize data by mean and variance way
    utils: some functions used to visualize samples from data set
  "weights": stores trained model weights with no augmentation

  main.py: run this Python script if you want to test the data with no augmentation
  main_aug.py: run this Python script if you want to evaluate data with augmentation
  model.py: model building using Keras
  predict.py: Run this Python script to see predicted results and generate confusion matrix, change file names or file
      paths to get corresponding data for successful running

Notice: this is the re-implementation of DenseNets, but not detailed ones as same as the original paper. Differences like pre-processing, normalize method and hyper-parameters settings.