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https://github.com/hcnoh/vae-mnist-tensorflow2
TensorFlow2 Implementation for VAE for generating MNIST
https://github.com/hcnoh/vae-mnist-tensorflow2
deep-learning neural-networks tensorflow2 tensorflow2-experiments tensorflow2-models
Last synced: about 2 months ago
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TensorFlow2 Implementation for VAE for generating MNIST
- Host: GitHub
- URL: https://github.com/hcnoh/vae-mnist-tensorflow2
- Owner: hcnoh
- Created: 2019-10-15T12:09:24.000Z (about 5 years ago)
- Default Branch: master
- Last Pushed: 2022-11-21T22:31:43.000Z (about 2 years ago)
- Last Synced: 2023-03-08T23:13:29.846Z (almost 2 years ago)
- Topics: deep-learning, neural-networks, tensorflow2, tensorflow2-experiments, tensorflow2-models
- Language: Jupyter Notebook
- Homepage:
- Size: 522 KB
- Stars: 4
- Watchers: 2
- Forks: 4
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Variational Autoencoder (VAE) Implementation for Generating MNIST Images in TensorFlow2
This repository is for the TensorFlow2 implementation for VAE. This repository provides the training module and Jupyter notebook for testing a generation of the trained models. MNIST dataset was used for this repository.
## Install Dependencies
1. Install Python 3.5.2.
2. Install TensorFlow ver 2.0.0. If you can use a GPU machine, install the GPU version of TensorFlow, or just install the CPU version of it.
3. Install Python packages(Requirements). You can install them simply by using following bash command.
```bash
$ pip install -r requirements
```
You can use `Virtualenv`, so that the packages for requirements can be managed easily. If your machine have `Virtualenv` package, the following bash command would be useful.
```bash
$ virtualenv vae-mnist-tf2-venv
$ source ./vae-mnist-tf2-venv/bin/activate
$ pip install -r requirements.txt
```
## Training
*Note: MNIST-in-CSV dataset was used for this repository. But you can use MNIST dataset module in TensorFlow. But the following process is for just using MNIST-in-CSV dataset.*
1. **Download the dataset.**
The link for MNIST-in-CSV: [https://www.kaggle.com/oddrationale/mnist-in-csv](https://www.kaggle.com/oddrationale/mnist-in-csv)
2. **Unpack the dataset.**
You can check that there are two csv files named `mnist_train.csv` and `mnist_test.csv`.
3. **Modify the path for dataset in `config.py`.**
4. **Modify the path for directory for saving model checkpoint.**
5. **Execute training process by `train.py`.**
## Checking Results and Testing Generation
The Jupyter notebook for checking results and testing the image generation is provided. Please check `result_plot.ipynb`.
## Results
1. **Ploting the Encoder and Decoder Losses**
2. **Image Generation Results**
3. **Plotting the Latent Distribution**
## References
- VAE Tutorial: [Tutorial on Variational Autoencoders](https://arxiv.org/abs/1606.05908)
- VAE: [Auto-Encoding Variational Bayes](https://arxiv.org/abs/1312.6114)