readme.md

Variational Auto Encoder for Generating Handwriting Numbers

1. Introduction

• Here we will train a VAE to generate handwriting number
• The dataset is MNIST, it will be downloaded under the folder dataset using torchvision, the dataset folder structure looks like this:

dataset
├── mnist
│   └── MNIST
│   │   └── raw
│   │       ├── t10k-images-idx3-ubyte
│   │       ├── t10k-images-idx3-ubyte.gz
│   │       ├── t10k-labels-idx1-ubyte
│   │       ├── t10k-labels-idx1-ubyte.gz
│   │       ├── train-images-idx3-ubyte
│   │       ├── train-images-idx3-ubyte.gz
│   │       ├── train-labels-idx1-ubyte
│   │       └── train-labels-idx1-ubyte.gz

2. Load dataset, Build model, Train model

• For this task, we just build a easy model which only contains fully connected layer, no convolutions
• Here I use a NVIDIA GeForce RTX 3090 to train, each epoch will cost about 3 seconds
• If you want to train from scratch, you don't have to modify anything. If you finish training and want to generate number picture, modify mode, simply run program and wait for your generated numbers

python run.py

• Of course, you can modify the model architecture or try some other hyper-parameters, do anything you want

3. Check the quality of generated image

• First of all, we will use random Gaussian Noise to sample some images, here are 256 examples

• Then we can see the reconstruct numbers

• I think the quality is good because we just use such a simple model, you can try the model we used in VAE_ANIME

4. Some references

• Tutorial on Variational Autoencoders(English)
• Tutorial on Variational Autoencoders(Chinese)
• During training, it's reasonable that reconstruct loss decrease, but why KL divergence increase