This repository can be thought of as an introduction to my paper, "Using three-band SDSS imaging to predict gas-phase metallicity". Here I give a demonstration of how to use the fastai and Pytorch libraries to train convolutional neural networks (convnets). We use the convnets to predict gas-phase metallicity from images.
Download this repository by running
git clone https://github.com/jwuphysics/convnet-demo.git
cd convnet-demo
Install version 0.7.0 of fastai and the corresponding Pytorch. You can do this by executing the following (assuming you don't have a GPU):
conda env create -f environment-cpu.yml
conda activate fastai-cpu
pip install fastai==0.7.0
pip install torchtext==0.2.3
Note that if you do have a GPU, you can instead run:
conda env create -f environment.yml
In order to execute the Jupyter notebook(s), simply make sure you are in
the fastai-cpu environment, and then serve up a Jupyter notebook.
You can then navigate to the ./notebook directory and open whatever
notebook you want.
The notebook Pytorch and fastai demo.ipynb contains some information
and instructions on getting started with writing a basic Pytorch convnet.
I've also included some fastai functionality such as finding an optimal
learning rate, cyclical learning rate schedules, and data augmentation.
The notebook Image resolution demo.ipynb shows examples for how to
resize images with fastai and gives examples how to make predictions
using low-resolution images with a super simple network.
I snagged 500 random objects from the actual data set used in our paper. These are directly bundled in this repository since the images are small and they only total about 10 MB. There are 400 training images (which should be broken up into roughly an 320/80 training/validation split) and 100 test images.
If you want the full SDSS data set that we have used in the paper, please see the other repo, which contains instructions for querying the data.