readme.md

Generative Adversarial Networks for Generating Handwriting Numbers

1. Introduction

• Here we will train a GAN 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

• Make sure you have normalize the data, otherwise you'll find model doesn't converge, check dataset.py
• 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 7 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

• I train it for 200 epochs and the training process is shown below, blue curve for generator loss, red curve for discriminator loss

• You can check the model generation effect using tensorboard, we save it every 20 epochs
• Here we will use random Gaussian Noise to sample some images, here are 256 examples

• I think the quality is good because we just use such a simple model, but the time cost is a little bit high

4. Some references

• Generative Adversarial Nets