Adversarial Autoencoder
This is the Chainer implementation of Adversarial Autoencoder [arXiv:1511.05644]
この記事で実装したコードです。
Requirements
- Chainer 1.6+
Running
Create "images" directory in the root or use "image_dir" option to specify the directory that contains training images.
Options:
- --image_dir
- Specify the directory that contains the training images.
- --load_epoch
- Specify the model you want to load.
Class label
Add the label index (must start 0) to the image filename.
format: [0-9]+_.+\.(bmp|png|jpg)
e.g. MNIST
Training
e.g. swiss roll distribution
cd swiss_roll
python train.py
Visualizing:
e.g. swiss roll distribution
cd swiss_roll
python visualize.py --load_epoch 10
MNIST Unsupervised Learning
Uniform (-2.0 ~ 2.0)
1,000 train data
9,000 test data
MNIST Supervised Learning
10 2D-Gaussian Distribution
1,000 train data
9,000 test data
Swiss Roll Distribution
1,000 train data
9,000 test data
