If you want to run on GPU, you need to check your cuda's version and install pytorch like below.

$ conda install pytorch torchvision cudatoolkit=9.0 -c pytorch

(Check pytorh homepage for more information.)
Then install Python Packages

$ pip install -r requirements.txt

OR pull and run docker image (e.g. blur-training:1.0) I made for this experiments.

AlexNet-Cifar10
This AlexNet has different kernel-size and dense-size due to the image size of Cifar10. This AlexNet is the same structure with this site (in Japanese).

General usage example:

$ cd ./training
$ python main.py --mode [TRAINING MODE] -n [EXPERIMENT NAME]

For main.py, you need to use --exp-name or -n option to define your experiment's name. Then the experiment's name is used for managing results under logs/ directory.
You can choose the training mode from {normal,blur-all,blur-half-epochs,blur-step,blur-half-data} by using --mode [TRAINING MODE] option.

• normal
  This mode trains Normal alexnetCifar10.
  usage example:

$ python main.py --mode normal -e 60 -n normal_60e

• blur-all
  This mode blurs ALL images in the training mode.
  usage exmaple:

$ python main.py --mode blur-all -s 1 -n blur-all_s1

• blur-half-epochs
  This mode blurs first half epochs (e.g. first 30 epochs in 60 entire epochs) in the training. usage example:

$ python main.py --mode blur-half-epochs -s 1 -n blur-half-epochs_s1

• blur-half-data
  This mode blurs half training data. usage example:

$ python main.py --mode blur-half-data -s 1 -n blur-half-data_s1

• blur-step
  This mode blurs images step by step (e.g. every 10 epochs).
  usage example:

$ python main.py --mode blur-step -n blur-step

• --blur-val
  This option blurs validation data as well. usage example:

$ python main.py --mode blur-half-epochs -s 1 --blur-val -n blur-half-epochs_blur-val_s1

• --resume [PATH TO SAVED MODEL]
  This option trains Normal alexnetCifar10 from your saved model.
  usage example:

python main.py -e 90 --mode normal --resume ../logs/models/blur-half-epochs_s1/model_060.pth.tar -n blur-half-epochs_s1_from60e

logs/ directory will automaticaly be created when you run one of training scripts.
logs/ directory contains outputs/, models/, and tb/ directories.

• logs/outputs/ : records "stdout" and "stderr" from the training scripts.
• logs/models/ : records model parameters in the form of pytorch state (default: every 10 epochs).
• logs/tb/ : records tensorboard outputs. (acc/train, acc/val, loss/train, loss/val)

data/ directory will automaticaly be created when you run one of training scripts.

Demonstrations and examples of Gaussian Blur.

Training scripts and functions are strongly relied on pytorch tutorial and pytorch imagenet trainning example.