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I was hoping for an explanation of, or some insight from, the loss curve. Training makes very little progress for a long time, then suddenly converges. In my (b
by wrs 1y ago
I was hoping for an explanation of, or some insight from, the loss curve. Training makes very little progress for a long time, then suddenly converges. In my (brief) experience with NN training, I typically see more rapid progress at the beginning, then a plateau of diminishing returns, not an S-curve like this.
- montebicyclelo 1y agoHmm, just my intuition: training this model was very sensitive to the initial seed and training hyperparameters. It struggles to actually get to the 3x3 conv solution; but once it gets close to that things move much more quickly. This can kind of be seen in the animation of the attention matrix over time, which starts off random / spread out, but then once it starts to get more parts of the attention matrix in place it moves quicker. (Assuming all the experimentation wasn't in some bad part of the hyperparameter space.) Also, it may just be the nature of the task. Some tasks you might have more to learn, all the time, with each training sample potentially giving information that's different from all the others. But with this, once it gets close to the solution of Life, it's quick.