Partially Trained Neural Network Updates

Week 14 Progress Update     This week's blog is going to be very short as I made literally no progress. This week I was planning on continuing to analyze data but unfortunately I was unable to gain anymore data due to my computer running unfathomably slow. As such I have simply been trying to speed up my computer over the last week all of which is menial tasks meaning I won't have any images or anythin to really show. I think it is now at a manageable speed meaning that by at least the middle of the week I will be able to continue with my plans, unfortunately this means I will have lost a week of data collection and analyzing which is rather frustrating.

Week 13 Progress Update     This week was mainly used to analyze the sessions that i have run so far. I came up with a system where I apply the weights to three different images, a checkered pattern, black and white noise, and a sample from the cifar10 database, more specifically a chicken. Applying the weights to these three images allows me to see the patterns that these filters "learn" to "look for".  While I don't have a particularly precise method of analyzing these new images, I have developed a system that allows for semi-consistent analysis of the images that can be compared across sessions. This system is in the form "x/n property (c/10)" where x is the number of images that appear to have the given property out of the total number n of filters in that layer. I say that these appear to have this feature with certainty c/10 where 10 is very certain and 1 is not certain at all. The document for one of my sessions is shown below.

Week 10 Progress Update     This week I've made some significant progress, I finished my program to show the convolutions as applied to specific images which should help me significantly. I haven't been able to start analyzing anything yet but even from a few tests of my program I can tell that it will be extremely helpful. The program is integrated into the program that displays the actual weights themselves so that I can run it and view everything at once. I also slightly modified my naming conventions to include batch size because I'm planning to vary the batch size in order to increase accuracy as well as get some different angles.  above: The code that graphs the convolutions as applied to an image. below: The weights from the first layer of a batch size 10, 10,000 image run as applied to an image of a bird (chicken)  

Week 11 progress update     This week I started to record and analyze the output of the neural network. Using the saving feature of my neural network I ran the network twice, first on 10,000 images and then on 30,000 images. I saved the weights, naming them used on a system as follows, (# of images)_run(run #), for example 10k_run1. I made a program that output the weights in a grid with a square representing a weight, with a high weight being red and a low weight being blue as shown below. I then began adding a function that could apply the weights to an image so I could better analyze it. I haven't quite completed this yet but as soon as I do I should be well prepared to analyze the weights after I have collected a few more samples. I mighr additionally make a program to average together several weight matrices from samples of the same size so that I can get a more accurate general example. below - the first and second level weights for the 10k run and 30 k run ...