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.