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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:


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 classes. This all happens very quickly in memory; the stored files are left alone in case we ever decide we need 26 classes or 3 color channels. As for our minimal working model: it was just a simple feed-forward neural network with an input layer, one hidden layer, and an output layer. It used RMSProp as its optimizer, categorical cross-entropy as its loss function, and accuracy for its metric. The output layer uses softmax for its activation function, resulting in decimal values between 0 and 1. I trained the model overnight on our currently processed dataset of 301 samples, where the average sample contained between 1000 and 2000 labelled frames.

After training the basic model, I wrote a quick game agent which loads the model and then feeds the it game frames in exchange for predictions. The predictions come as a list of 9 values from the softmax layer. The agent is pretty simple; it just tests each prediction class against an arbitrarily-defined threshold value. If the prediction value exceeds the threshold, then the button for the corresponding action is pressed; otherwise, that button is released. Some classes, such as left and right or up and down, are mutually exclusive. For example, if both left and right pass the threshold test, then the agent will choose the higher of the two.

So, what did the feed-forward neural network make game agent do? Well, it mostly held down and left and occasionally tried to jump. The result was a pacifist bot that appears to cower in the corner before jumping off of the stage to kill itself. I believe that this is a good first step.
Image credit

After confirming that the feed-forward model successfully trains, saves, loads, and predicts, Rei and I set to work on developing bigger and better models. So far we have created and trained an LSTM and a 2D convolutional network. However, neither of these have had great results in the game yet. I was unable to make the LSTM load, and the 2D convolutional network somehow managed to play even more poorly than the feedforward network. Furthermore, we have a serious resource problem. I am only able to train our advanced models on  CPU and RAM, because my 6 GB of VRAM is not nearly enough for me to run them on GPU. The convolutional network in particular utilized nearly all 32 GB of RAM on my computer as well as 100% of the processing power of my I7 6700. It also took around 8 hours to train.

The last major issue is that of creating our perfect dream-model. We are trying to combine the 2D convolutional network architecture with an LSTM. This means that our input shape needs an additional dimension: time. Currently, our input shape is batch size × image u × image v × image color channels, or n×135×240×1. The time dimension, which represents frames, must go between batch size and image u. However, to my knowledge only batch size can be variable, and our replays have all kinds of durations! Keras provides a TimeDistributed wrapper that we can use to encapsulate our Conv2D layers. Also, we will be looking into the ConvLSTM2D layer provided by Keras; it is a specialized type of layer that performs the exact task we are interested in. However, I am not sure how to we can define our input shape. We may have to overhaul our Sequence subclass so that it produces a fixed number of frames. Only additional research and experimentation will lead to an answer, though.


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Matthew - Blog Post 8 is only 339 lines long but it feels at least three times that when I'm working on it. The module is definitely due for a refactor. For one, the term subdataset should be renamed to version_set and extracted into a class. version_set more accurately and describes what it is, and the class extraction would clean up the namespace in ReplayManager. There is probably some kind of class extraction possible for replays, so that their names, paths, file streams, and Twitter profiles can all be neatly encapsulated, thereby cleaning up the namespace even more. However, I do not want to worry about having two kinds of replays: the one used by ReplayManager and the one used by ReplayParser. Even though ReplayManager does not use ReplayParser, the prospect of making things more muddier deters me.

There's probably a right way to refactor this code, but, to put it simply, now is not the time. Speaking of time, I came up with a great way to get work done, even when I am sleep…

Matthew - Blog Post 10

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: