Day 13

Today, Nate came back from the mountains to work in the lab. Welcome back Nate!
I kept on doing the same work from last time. With the CAE experiment v.2 running in the background, I proofread and edited my section of the paper, and I worked with the ladder network for the rest of the day. First, I got rinuboney's implementation of the ladder network in TensorFlow (github) working in Python 3. Testing it with the MNIST dataset, I got the expected results (very good). Since we all love graphs, I quickly made one for said results.
The idea is pretty simple. The ladder network is trained on 60,000 images of handwritten digits, 100 of which are labeled. The network makes predictions about the labels of 10,000 other images, and we calculate how accurate those predictions are. This process comprises 1 epoch, and is repeated 149 more times. By the end, the network achieves almost 99% accuracy. This is what we're hoping to do with HSI classification, but there seems to be an issue with our current configuration, since we're getting significantly lower accuracy than what we're expecting. With code that works well (albeit on a different dataset), I'm hoping this will help me debug our own code.

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Outline

Motivation/Background: HSI classification What is HSI? Comparison to RGB The advantages of HSI over other kinds of data Instant, remote, nondestructive data collection Hundreds of data points per pixel What is classification? Introduce using toy data set, like CUB-200 Applications of HSI classification Cancer detection Art authentication Food quality analysis Remote sensing Urban development agricultural monitoring environmental assessment Problems faced High dimensionality Scarcity of labeled data Method: MCAE and Ladder Network Feature Extractors Baseline: PCA Inspiration: Autoencoder Our proposed method: MCAE Classifiers Baseline: SVM Inspiration: Neural network Our proposed method: Ladder network Results : State-of-the-art Comparison Comparison of MCAE against PCA and state-of-the-art Comparison of Ladder Network against SVM and state-of-the-art
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Day 2

Day 2 started with a very brief meeting where we went over the schedule for the next six weeks. Abstract, outline, presentation; two weeks per task. It was at this point that I realized the difficulty of making technical topics sound interesting, then I immediately dismissed the problem as one for my future self. Today was dedicated to Support Vector Machine (SVM) creation, evaluation, and optimization. I was unaware of many of the wonderful functions included in the provided libraries, and ended up doing the data reformatting less elegantly. It still worked, so I left it. When I finished up my SVM code, I tried to help Nate with the errors he was running into. The keyword here is "tried." Note that it's not easy to explain concepts that you figured out hours earlier and happen to be more complicated than they should be. Nate was a good sport about it though, and with Ron's help, he had a good idea of the solution by the end of the day. For the last part of the day,
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Day 28

Week 6 of the internship; the final presentation is on Thursday. It doesn't feel like week 6, but that's probably because I missed almost all of week 4. Regardless, there was much work to do before Thursday, so Nate and I rehearsed our presentations twice in front of the lab. Many revisions were made, and I'm feeling much more prepared for the real deal. Thank you to everybody who was willing to listen to four lame high school presentations about the same topic in the same day. Your contribution will make the very same presentations significantly less lame on Thursday. Apart from presentation-related stuff, Ron and I ran some ladder network and SMCAE experiments all day. Ron tackled the ladder network with PELU activation (which was apparently very difficult) and I continued with adjusting initial layer sizes on the ladder network and doing SMCAE runs with fixed component count PCA as a feature scaler. The results aren't looking too hot, but I'll withhold judgem
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