https://github.com/srama2512/vae
A simple variational autoencoder to generate images from MNIST. Implemented in TensorFlow.
https://github.com/srama2512/vae
autoencoder generative generative-model image-generation variational
Last synced: about 1 year ago
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A simple variational autoencoder to generate images from MNIST. Implemented in TensorFlow.
- Host: GitHub
- URL: https://github.com/srama2512/vae
- Owner: srama2512
- Created: 2017-02-05T16:58:14.000Z (over 9 years ago)
- Default Branch: master
- Last Pushed: 2017-03-05T15:52:02.000Z (over 9 years ago)
- Last Synced: 2024-12-25T14:43:04.584Z (over 1 year ago)
- Topics: autoencoder, generative, generative-model, image-generation, variational
- Language: Python
- Size: 24.7 MB
- Stars: 3
- Watchers: 4
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Variational Autoencoder
This is an implementation of a simple variational autoencoder which trains
on the MNIST dataset and generates similar images of digits.
## Requirements:
1. CUDA toolkit 7.5
2. cuDNN v5
3. TensorFlow (https://github.com/tensorflow/tensorflow/tree/r0.10)
from r0.10 branch, select binary compatible with the above.
## Instructions to run:
Run `python trainScriptClass.py`. It will train a simple 2 layer VAE generator with 2 layer encoder for training. The parameters are defined below:
1. `batch_size`: size of the training and testing batch (for batch size of 20, it nearly reached 11 GB on NVIDIA-Titan X Maxwell)
2. `X_size`: size of the input (here it is the total number of pixels)
3. `hidden_enc_1_size`: hidden layer 1 size in the encoder
4. `hidden_enc_2_size`: hidden layer 2 size in the encoder
5. `hidden_gen_1_size`: hidden layer 1 size in the generator
6. `hidden_gen_2_size`: hidden layer 2 size in the generator
7. `z_size`: size of the latent variable
The model trains with a default learning rate of 1e-4 using the adam optimizer.
The model is trained for 200000 iterations and 20 randomly generated samples and the checkpoint of the corresponding model are saved in `generated_class/' directory after every 50000 iterations.
## References:
1. This was implemented based on the Carl Doersch's tutorial available at: https://arxiv.org/abs/1606.05908
2. Another useful reference for implementing VAEs is: https://jmetzen.github.io/2015-11-27/vae.html
## Changelog:
Feb 10, 2017
* Added convolution + deconvolution based VAE
* Batch size is again fixed at initialization, have to alter the technique.
======================================
* Added support for a Beta weighting term in the KL Divergence loss
* Batch size is no longer fixed at initialization
* Added functions to encode a given x and decode a given z, and also to
perform both these operations to generate an image "like" the given one.
* beta_trainScriptClass_conditional.py adds visualization of latent
features