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--- project_people:josh_donckels [2017/11/27 17:18] – jdonckels
+++ project_people:josh_donckels [2017/12/10 20:01] (current) – jdonckels
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-===== Project Idea =====
+===== Project Concept =====
 Implement a convolutional neural network (CNN) that will be able to classify an input pattern (a 3x3 window on the MFM).  It will use the layers based around a very basic CNN including: convolution layer and a fully-connected layer.  This will have to start off static in the MFM, as passing data would be very difficult otherwise.  This will require five elements that I will go into detail further down this page.
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    *Redesigned fully-connected layer, to where it can classify four different patterns at any rotation
      * Box, two-length line, L shape, and a three-length line, I get a global max of 77.5% class rate
+     * Went back to three as I was running into problems and I get consistent 85% classification rate
    * Improved paper, still need more plots for results and an improved discussion
+     * Finished Paper!
+Week 15 Update:
+   *Improved slides, and practiced presenting.
+   *Finished everything and turned it in.