Skip to main content

Rei - Blog Post 10

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 would be useful to mention these tests.




Next Steps


Now that we are in the extreme-final steps of our project, my primary role has been actually training the data. The results of which are already on our github.

Matthew and I will be moving to focusing on our paper and our poster. While we will be helping one another, Matthew will be taking the lead for the paper while I will be taking the lead for the poster. I personally hope to get the poster done within the next couple of days.

Comments

Popular posts from this blog

Matthew - Blog Post 9

After our last meeting, Professor Auerbach asked us to shift our focus towards building and training our model. So that's what we've been working on lately. The results so far have been interesting and problematic. The first step was to define a minimal working model and a loading system to feed it our labelled data-set. I wrote a Sequence subclass, which is essentially a kind of generator designed for use with the fit_generator method. With fit_generator and a sequence, we're able to train and test the model with just a couple of one-liners: model.fit_generator(sequence) model.evaluate_generator(sequence) The sequence subclass also has a few other tricks up its proverbial sleeve. For one, it reduces the dimensionality of the frame buffer data from 135×240×3 to 135×240×1 by converting it to gray-scale. This reduces the number of features from 97,200 to 32,400. For two, it does the same with the labels, combining and dropping 26 action types into just 9 atomic clas...

Rei - Blog Post 4 & 5

Due to external factors, I accidentally missed the last Blog Post, however I shall make up for it by writing two posts in one! The last couple of weeks have been at least a little productive. Blog Post 4 Over these weeks, Matthew and I were both working on solidifying and exploring the context portion of our project. Of course, we have been exploring the context since nearly the beginning of the year, but because of both capstone classes have asked us to write something about the context of our paper. The context of our project can be found throughout our previous blog post, but a short and summarized version of out context section boils down to a couple main points.  Neural Networks are not too present in video games. When neural nets are present, they are used for research or marketing. As for the rest of our Design Document, unfortunately we rushed it a bit. We got caught up in some upsetting events these weeks and allowed our work to suffer instead of staying on t...