This week I began running my machine learning code on AWS. I first requested an EC2 instance with a GPU, which took about 2 days for AWS to process the request. Then on Wednesday I uploaded my data set and code to the EC2. Fortunately, the EC2 instance I requested was built for using Deep Learning libraries such as Tensorflow, so running the code on the GPU was a piece of cake. I simply had to change some settings on the EC2, and the code ran itself on the GPU (or so that’s what the terminal output said).
My first iteration of training resulted in a training accuracy of 76% and a validation accuracy of 74%, and took around 6 hours to run. This was surprisingly good. Although it doesn’t meet our goal of 95%, it seems reliable enough for demoing purposes. In order to bump up the accuracy, I added another convolution and activation (relu) layer. After training for a second time, I achieved a training accuracy of 95%, and a validation accuracy of 76%. What this means is that the model is overfit, and needs a larger data set and a regularization function. I will make the data set larger by flipping all of the images over the y-axis, and add a regularization function, and retrain tonight.
I will also draw a diagram for the solenoid sometime today. Once I finish the diagram, and once I retrain, I will update this post with the results.
