Day 15

Today I got to the lab earlier than usual and worked on my experiment before the morning meeting. Then, at the meeting, we reviewed some of our outlines. It seemed from the outlines that everyone's projects were coming along pretty well. I noticed that not many of the other interns have results yet, as their projects require longer-term preparation, while I already have results from my first experiment. When I got back to the lab, I continued working on my classifier experiment. The whole time I was in the lab today, I was writing code for a parameter search on the MLP classifier. I was able to reuse some of the code I had previously written, but the parameter search is outside of the pipeline, so I had to write some parts on my own that the pipeline had done itself previously. I almost finished the parameter search code today, but I ran into a confusing error regarding cross validation (CV). Ron helped me look over the problem, but I am still having a tough time understanding CV and the error that it is causing. I hope to figure this out tomorrow.
Also, today I was a test subject for Titus and Aditi's experiment. They were not allowed to tell me exactly what they were testing, so it was kind of mysterious. They brought Henry, who was another test subject, and I over to the Golisano Building. They had me wear glasses that tracked my eye movements and they had me walk aimlessly around the building. Then, also with the glasses on, I sat on a cart while Titus and Aditi's supervisor pushed it around the building, including up and down an inclined floor. I thought that was fun, but the supervisor looked pretty tired. After that, I walked with the glasses on back to the Imaging Science building. One day to hope to discover the meaning of this experiment.

Comments

Post a Comment

Popular posts from this blog

Day 6

I began today by finishing the documentation I had been doing of the code for my experiment. After that, I began working on new code that would test the performance of data preprocessing methods that were already written. I am using the Pavia University scene, which is a set of labeled hyperspectral data. I began to write code to run the experiment, which will include testing various preprocessing methods each with thirty trials. In the process, I used the code from my first tutorial as a reference, and I found an error in that code that had been making it run too slowly. It had been using too much data to train. I fixed this issue, making that code run more quickly, and continued with the code for my experiment. Eventually, in my experiment code, I ran into a problem: I was shaping the data into different dimensions to split it into training and testing groups, but it had to be in its original dimensions to be used by the preprocessing methods. I am still working on this issue, and I ...
Read more

Day 14

Today I continued testing different parameters for the MLP classifier. While I ran tests, I finished documenting the classifiers that I am testing. I also wrote an introduction to my descriptions of the preprocessing methods, finished citations for the descriptions, and wrote a summary of my preprocessing experiment in an article being written in Overleaf about the machine learning pipeline. Although I am not yet ready to test the next classifier, RandomForest, I began writing code to test for the optimal preprocessing methods and parameters to use with it. Throughout the day, I collected data on the metrics of different combinations of parameters for MLP. I looked through this data and ran a larger number of trials on the most promising parameters, but it still appears that the default parameters are the most accurate, even though the overall accuracy resulting from the default parameters averages at about seventy percent, which is not very good compared to the other two classifiers I...
Read more

Day 3

Today I began by implementing the changes in the tutorial that Ron and I had previously discussed. I used arrays in my code instead of multilevel lists, because the machine learning program requires arrays. I then used two different machine learning programs, one linear and one non-linear, to process the training data and predict the second array of testing data based on the first. The linear learning program (a Linear Support Vector Machine) was more successful and now only takes about two minutes to train and make its predictions. I ran metrics to test the accuracy of the linear program, such as overall accuracy, mean-class accuracy, and kappa statistic, and it proved to be fairly accurate. Later in the tutorial, the accuracy will be improved with further measures. However, the non-linear program (a Support Vector Machine with Radial Basis Function kernel) is still too slow and inefficient. I am not certain why this is, but tomorrow I hope to fix this issue.
Read more