This is a combine of complex-value network and semi-supervised GAN, this may not have much advanced in study research.It just a personal interest motivated practise in coding.And the code is from four part of already exist repositories. There are the four part of the code:
SSGAN
CV-Network_1(the first CVNet code) CV-Network_2(the second CVNet code)
CapsNet(Todo:this part is under develop, and may be soon to come)

• Python 2.7 or Python 3.3+
• Tensorflow 1.0.0
• SciPy
• NumPy

Download datasets with:

$ python download.py --dataset MNIST SVHN CIFAR10

Train models with downloaded datasets:

$ python trainer.py --dataset MNIST
$ python trainer.py --dataset SVHN
$ python trainer.py --dataset CIFAR10

Test models with saved checkpoints:

$ python evaler.py --dataset MNIST --checkpoint ckpt_dir
$ python evaler.py --dataset SVHN --checkpoint ckpt_dir
$ python evaler.py --dataset CIFAR10 --checkpoint ckpt_dir

The ckpt_dir should be like: train_dir/default-MNIST_lr_0.0001_update_G5_D1-20170101-194957/model-1001

Train and test your own datasets:

• Create a directory

$ mkdir datasets/YOUR_DATASET

• Store your data as an h5py file datasets/YOUR_DATASET/data.hy and each data point contains
  • 'image': has shape [h, w, c], where c is the number of channels (grayscale images: 1, color images: 3)
  • 'label': represented as an one-hot vector
• Maintain a list datasets/YOUR_DATASET/id.txt listing ids of all data points
• Modify trainer.py including args, data_info, etc.
• Finally, train and test models:

$ python trainer.py --dataset YOUR_DATASET
$ python evaler.py --dataset YOUR_DATASET

Part of codes is from an unpublished project with Jongwook Choi