Adversarial Auto-Encoder for MNIST
An implementation of adversarial auto-encoder (AAE) for MNIST descripbed in the paper:
- Adversarial Autoencoders by Alireza Makhzani et al.
Implementation Details
The paper suggest various ways of using AAE.
- Basic AAE
- Incorporatiing Label Information in the Adversarial Regularization
- Supervised AAE
- Semi-supervised AAE
- Unsupervised Clustering with AAE
- Dimensionality Reduction with AAE
Only results on 'Incorporatiing Label Information in the Adversarial Regularization' are given here.
Target Distributions
Three types of prior distrubtion are considered.
- a mixture of 10 2-D Gaussians
- a swiss roll distribution
- a normal distribution : not suggested in the paper.
The following graphs can be obtained with command:
python test_prior_type.py --prior_type <type>
| mixGaussian | swiss_roll | normal |
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Results
Leveraging label information to better regularize the hidden code in Figure 4 in the paper.
Prior distribution type : a mixture of 10 2-D Gaussians
The following results can be reproduced with command:
python run_main.py --prior_type mixGaussian
| Learned MNIST manifold (20 Epochs) | Distribution of labeled data (20 Epochs) |
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Prior distribution type : a swiss roll distribution
The following results can be reproduced with command:
python run_main.py --prior_type swiss_roll
| Learned MNIST manifold (20 Epochs) | Distribution of labeled data (20 Epochs) |
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Prior distribution type : a normal distribution (not suggested in the paper)
The following results can be reproduced with command:
python run_main.py --prior_type normal
| Learned MNIST manifold (20 Epochs) | Distribution of labeled data (20 Epochs) |
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Usage
Prerequisites
- Tensorflow
- Python packages : numpy, scipy, PIL(or Pillow), matplotlib
Command
python run_main.py --prior_type <type>
Arguments
Required :
--prior_type: The type of prior distrubition. Choices: mixGaussian, swiss_roll, normal. Default:mixGaussian
Optional :
--results_path: File path of output images. Default:results--n_hidden: Number of hidden units in MLP. Default:1000--learn_rate: Learning rate for Adam optimizer. Default:1e-3--num_epochs: The number of epochs to run. Default:20--batch_size: Batch size. Default:128--PRR: Boolean for plot-reproduce-result. Default:True--PRR_n_img_x: Number of images along x-axis. Default:10--PRR_n_img_y: Number of images along y-axis. Default:10--PRR_resize_factor: Resize factor for each displayed image. Default:1.0--PMLR: Boolean for plot-manifold-learning-result. Default:True--PMLR_n_img_x: Number of images along x-axis. Default:15--PMLR_n_img_y: Number of images along y-axis. Default:15--PMLR_resize_factor: Resize factor for each displayed image. Default:1.0--PMLR_z_range: Range for unifomly distributed latent vector. Default:3.0--PMLR_n_samples: Number of samples in order to get distribution of labeled data. Default:10000
Acknowledgements
This implementation has been tested with Tensorflow 1.2.1 on Windows 10.