Day 22

Today was my first day back in the lab, just in time for the undergraduate research symposium. Nate and I headed down for a bit to look around at the posters. I didn't know RIT had so many REU students, and there was a huge variety of topics. Everyone from the lab went down to stop by Angelina's poster, and Nate had a friend presenting there as well. There was definitely some cool stuff being presented.
I mainly kept working on the ladder network today, trying to find out how to improve it. Once again, Andrej Karpathy's wonderful course notes for his CS231n class helped out a ton. I mainly focused on testing different learning rate functions. These functions determine how much a neural network adjusts its weights after every iteration of training. The goal is to minimize loss more efficiently.
These are a few examples of learning rate functions. I originally had the "Rectified Linear" function, but I noticed that loss and accuracy would hardly change after 1 or 2 epochs, which indicates a high learning rate. Hence, the "Inverse Proportional" functions. "Inverse Proportional I" was a bit better, but still showed signs of being too high. I think "Inverse Proportional II" could mitigate my problems, but I have yet to test it. There are also adaptive learning rates, like Adagrad, Adadelta, and the simpler ReduceLROnPlateau, and I may look into them as well.

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