So, I missed blog post 9. This is me acknowledging that for consistency. Anyway, the past couple of weeks have been incredibly productive for ContentsMayBeHot. Matthew has finished collecting all the replay data, we have refactored our project to reduce complexity, we have improved the runtime of our code, and finally we have started seriously training our model. The Changes Matthew implemented multi-threading for the model loading. Which reduced our load time between files from about 3-5 Seconds to 1 Second or less. Which allows us to fully train a model in much less time! While Matthew did this I reduced the code duplication in our project. This way, if we needed to change how we loaded our training data, we didn't have to change it in multiple places. This just allows us to make hot-fixes much more efficiently. We also started working on some unittests for our project using pytest. These tests were written because of a requirement for another class, but we thought it...
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At the time of my last blog post, we were managing quite a few problems. Our model was essentially vaporware, our training and testing was hindered by slow, blocking function calls from our loader, and our VRAM was continually getting exhausted during training sessions. But there is nothing to worry about. We have made major strides since then. Major strides. Model improvements First, we have completely overhauled our model's architecture. We are now using a model composed of special layers that combine the functionality of a 2D convolutional neural network with that of an LSTM. Here is a summary of our model as printed by Keras: This model was made with the help of the wonderful community over on Stack Overflow . I would also like to mention that Professor Auerbach made invaluable contributions. In general, his tutelage made this project possible. We dropped our Sequence subclass, and replaced it with training and testing loops. In these loops, we iterate over the whol...