This project is an attempt to create a Convolutional Neural Net that predicts how many upvotes a picture posted on Reddit r/art would get.
I was hoping that in using a CNN, I could identify features that highly upvoted posts have.
The main tool used is Tensorflow/Keras, and pictures were downloaded from Reddit for training using PSAW (PushShift API Wrapper) and PRAW (Python Reddit API Wrapper)
The neural net ended up not training too well, with the accuracy plateauing around 25%. I'm not sure if it's because it's naturally hard to find a pattern that highly rated posts share, or if it's a flaw in my implementation of the convolutional layers.
It also doesn't help that so many posts, regardless of quality, get stuck at 0 or 1 upvotes on Reddit, and so it would make sense if my neural net only wanted to guess 1 for every input.
ALl in all, this project was a learning experience for me, and it taught me that variable input size and creating my own dataset are possible.
The main Jupyter notebook I used is main.ipynb, and the noteboook that I adapted for training on Google colab is colab.ipynb
The path names for the folders where images were downloaded/processed from have been changing as I've been prcoessing different pictures. Change them for your own usage.
Since I downloaded images from Reddit, some preprocessing was necessary. I had to:
- Detect and delete corrupt/images deleted by Reddit
- Convert grayscale images to colour so that every image has 3 channels
- Remove alpha channels if those existed
- Scale images down to 1000 pixels on a dimension max so that my computer could actually train in a reasonable amount of time
Since I was trying to train on over 20000 pictures, it wasn't possible to load them all into memory, so I instead made a custom image generator custom_file_image_generator to feed pictures to Keras.fit one at a time. This required storing the names of files from my training and validation set into .txt files so that I could follow this tutorial I ended up resizing the files anyway to make training shorter/make be able to upload the pictures to Google Drive, so maybe loading them into memory was viable now, but this was a good learning experience in not having my hand held in terms of dataset processing.
Another quirk that I wanted was the ability to input pictures of varying shape, rather than a set input shape. This was achieved using GlobalMaxPooling and following the steps in this Stack Overflow post. Again, it could be that my network isn't learning because of this quirk I tried to implement.
I'm using a layer in the neural net to normalize the pictures to [0 1]
I've tried normalizing the scores to [0 1] or just leaving them as [0 maxscore], but no success