CGAN_MNIST

Conditional Generative Adversarial Networks for Generating Handwriting Numbers

1. Introduction

• In the previous GAN_MINIST we can generate handwriting numbers randomly, for this project we wanna generate numbers conditionally(for example we wanna generate 7, then generated pictures should contain number 7). We will train a CGAN to do this
• 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

• We don't have to modify too much, only part of the model and input should be modified, see the code to check this
• 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, we will use random Gaussian Noise and label information(for line i, we put label i, i from 0 to 9) to sample some images, here are 200 examples

• I think the quality is just ok, it does generate image according to our label but not that great

4. Some references

• Generative Adversarial Nets
• Conditional Generative Adversarial Nets