Tag: Status Report

I’m happy to say that I accomplished everything I set as my goals last week and in fact am ahead of schedule at the moment. I was able to port my data over to the ECE machines and finish training (with about a 13x speedup) on there. I solidified both the weights and the architecture for the value network. I then utilized the design space exploration from the value network to solidify the architecture for the policy network. Finally, I began testing the MCTS process locally, and once I am sure it fully works, I will port it over to continue on the ECE machines as well.

Starting off with the relocation to the ECE machines, I was able to move 13 GB of training data (more than 3.5 million data points) over to my ECE AFS so I could train the value network remotely. This had the added advantage of speeding up training time by a factor of about 13, meaning I had more freedom with the network architecture. The architecture I ended up settling on took about 13 minutes per epoch on the ECE machine, meaning it would have taken ~170 minutes per epoch locally which obviously would have been impossible as even a lower bound of 50 epoch would have taken about a week.

Secondly, my finalized architecture is shown below in listed form,

As you can see, there are three parallel convolution towers, with kernel sizes of 3, 5, and 7, which help the network derive trends in different sized subsections of the board. Each tower than has a flattening layers, and a fully-connected dense layer. These layers are concatenated together with the other towers, giving us a singular data-stream that passes through successive dense and dropout layers to prevent overfitting, culminating in a singular sigmoid output node, which provides the positional evaluation. This network was trained on 3.5 million data points, pulled evenly from over 60,000 expert level go matches. After training, the network was able to identify the winner of a game from a position 94.98% of the time, with a binary cross-entropic loss of .0886. This exceeded my expectations, especially considering many of the data points come from the opening of matches, where it is considerably harder to predict the winner as not many stones have been placed.

Using my design space exploration for the value network, I was able to solidify an initial architecture for the policy network, which will have the same convolutional towers, only differing in the amount of nodes in the post-concatenation dense layers, and the output form of a length 362 vector.

I have started testing MCTS locally, with success, once I am convinced everything works as expected I will port over to the ECE machines to continue generating training data for the policy network, in addition to tuning the value network. Fortunately, as for the first iteration of MCTS the policy network essentially evaluates all moves equally, the training data will be valid for further training, even if the architecture for the policy network needs to be changed.

In the next week, I plan to move MCTS over to the ECE machines and complete at least one iteration of the (generate training data via MCTS, tune value network and train policy network, repeat) cycle.

ABET: For the overall strength of the Go engine, we plan to test it by simply having it play against different Go models of known strength, found on the internet. This will allow us to quantitatively evaluate its performance. However, the Go engine is made of 2 parts, the value and the policy networks. Training performance gives me an inkling into how these networks are working, but even with “good” results, I still test manually to make sure the models are performing as expected. Examples of this include walking through expert games to see how the evaluations change over time, and measuring against custom-designed positions (some of which were shown in the interim demo).

In my previous week’s report, I mentioned that my goal this week was to finish the design space exploration for the Value Neural Network, and begin running simulations. Unfortunately, I am running about one day behind schedule, as processing the expert-level games dataset into consumable board states took longer than expected. However, I have a baseline version of the value network set aside for the interim demo, and am finishing up the design exploration as we speak, meaning if a better model is trained between now and Monday I can replace the already competent baseline.

That being said, I have not fallen very far behind at all, and it is easily covered by the slack built into my schedule. However, there are a few things of note before I start simulation proper, the first being ECE Machine setup. For the preliminary value network, I trained locally as the training data I generated takes up roughly 40 GB of space, well above my AFS limit. However, locally I am also limited by 8 GB of RAM meaning I can only use about 7.5 GB of this training subset anyway. As such, even if I cannot port all 40 GB of data onto the ECE Machines, anything over 8 GB would be an improvement, and worth trying just in case it helps train a substantially different model. As such, I am planning on asking Prof. Tamal on Monday who I should ask about getting my storage limit increased, and I will work on it from there.

The design space exploration has also yielded useful results in terms of what an allowable limit on network size would be. Locally, I’m currently operating with 2 convolutional layers, 1 pooling, and 1 fully connected dense layer, and this takes about 6.5 minutes per epoch with my reduced 8GB training set. The ECE machines will compute faster, and this 6.5 minutes per epoch rate is far shorter than my limit once we’re past the interim demo. This means if necessary, both the value and policy network architectures can grow without the training time becoming too prohibitive.

Therefore, beyond our interim demo, I plan to begin simulations next week to generate my first batch of policy-network-training and value-network-tuning data. Ideally I get the space increase on AFS quickly meaning I can do this remotely, but if possible I can run it locally as well, and port over the weights later. I also plan on setting up the architecture and framework for the policy network as well, so that I can begin training it as soon as the simulation data starts being generated.

In my previous weekly report, I mentioned that my goal for this week was to finish my expand() function, and to find a dataset to train the initial values for my value network. I am happy to say that I have accomplished both of these, as well as finishing the rest of the code required for MCTS. The dataset I am going to use is located here and contains over 60000 games from professional Go matches played in Japan. For the curious viewers, all the code required for MCTS can be found on Github.

With the aforementioned dataset, I plan to begin training the value network as soon as possible (hopefully by Monday). While I have the dataset, it is stored in the Smart Game File (SGF) format, which is the sequence of moves, not the sequences of board states generated. As I need the board states themselves for training, I am currently working on a script to automatically process all 60000 of these files, generating each board state and tagging them with the game result. These results are the training data I require. Once this is finished, I can begin training, which involves the physical training over the dataset, but also I will do some design space exploration with regards to network architecture (number of nodes, types and number of layers, etc.). This will allow me to find a closer to ideal combination of accuracy and processing time (as efficient simulations are helpful in training, but vital for usage).

This design-space exploration will actually prove helpful for the Policy Network as well, as it will provide a baseline for the allowable complexity. (Higher number of nodes and layers will generally perform better barring overfitting, but will take more time, the value network exploration will give me an estimate for the amount of layers I can use in the policy network as the networks can be used to evaluate positions in parallel.)

Then once I have the parameters (architectural mainly) for the networks set, and the initial weights for the value network trained, I can immediately begin running simulations, as all of that infrastructure is complete. I should be running these simulations by the time of my report next week, and will aim to do my first training run on both networks with the data the simulations generate.

As I mentioned in my previous status report, my goal for this week was to finish custom loss function implementation, then work towards getting the MCTS training data generation working, given that the framework was already there.

With regard to the former of the two goals, that work is all complete. I had actually misunderstood my needs, I don’t actually need a custom loss function, as the two networks are trained on mean-squared error (MSE) and binary cross-entropic loss (BCEL). Nevertheless, I have built the framework for the training of the two networks (Policy & Value), in addition to the data generation section of the MCTS code (where the board states and MCTS visit counts are stored as npz files.

With regard to the latter, there was a bit more involved than I originally anticipated. While I have all the Go gameplay functionality for training built, I am specifically not finished for the code for the MCTS expansion phase, where the leaf to expand is determined and children are generated. That being said, I have built out all other functionality, and am working to finish the expand() function by Monday in order to stay ahead of schedule. A brief schematic of the training structure is shown below.

Once the expand() function is finished, the next step is finding a dataset of expert go matches to use as training data for the value network pre-simulation. While this is not strictly necessary, and self-play could be used for this, giving the value network a strong foundation with expert-generated data improves the quality and function of the initial training data much faster. My goal is to have expand() finished and this dataset found by the end of the week. If that goes according to plan, I would be able to commence network training and MCTS generation immediately afterwards.

To accomplish my task, I had and will have to learn a few new tools. While not really a tool, I had to fully understand Monte Carlo Tree Search in order to program it correctly. More presciently, I have never used PyTorch before, or worked with Convolutional Neural Networks. While I have worked with similar technologies (fully connected deep neural networks and TensorFlow) I have had to do a lot of research into how to most effectively utilize them.