Robust-first Computing Wiki

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===== 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. 

===== Elements =====
  * Neuron:
    * Will contain the weights from the pre-trained network that it will be using to classify the image
    * Deal with all of the computations for the convolution
    * Will update the neighboring pixels based on the output from the computation
  * Init_Layer:
    * This is the middle Neuron, which will initialize, reset, and repair all of the other Neurons in the specific filter
    * This will also contain its own weight, and bias which will be summed into the total from the filter
  * FC_Neuron:
    * Neurons for the fully-connected layer, which will used fixed point multiplication to calculate its values. 
    * There will be four layers of 12, which will each represent a pattern to be classified
  * FC_Init_Layer:
    * Will initialize, reset, and repair the FC_Neurons in each layer
  * Label:
    * Will be used to classify the network once it is complete, meaning the FC_Layer_Twelve will create this once the the classification values are complete


===== Weekly Logs =====

Week 7 Update:
   * Now understand the concept of CNN better through messing with tiny_dnn
   * Found "final" design for blocks of CNN
   * Will now work on communication between the blocks in the CNN
   * Also will work on fixing a size for the CNN based around a smallish image 

Week 8 Update:
   * Updated project page to more clearly state what my projects is and my goals
   * Messed around with trying to get my own images as inputs for tiny_dnn
   * Researched how small my input images could be for a CNN

Week 9 Update:
   *Created Demo video for progress on project
   *New Title: Object Classification in the Movable Feast Machine
   *Scaled back project a lot to 3x3 hand made images without padding. So they represent object as the pixel values can only be 0 or 1.  
     * These 3x3 images can contain a dot (1) or a 2-length line (0).  
     * I get a 80% successful classification rate in tiny_dnn with the new CNN structure and these input images 
   *Researched into CNN structures and tested them in tiny_dnn with hand-made images of 9x9, 7x7, 5x5, 3x3, with padding and without 
   *New CNN structure is a 12 layer Convolution layer, that goes into a Fully_connected layer that takes in 12 input and outputs 2 results.
   *Implemented working convolution layer in MFM, which takes pixels from a image element and will calculate the output from this layer
   *Created shell for Fully-Connected layer for MFM, contains weights and biases, but nothing else yet.  
   *Scratched communication concept, as I can place all layer in a sort of column fashion, so there is no need for this

Week 10 Update:
   *Implemented the fully connected layer
   *Compared values with weights and results from each layer with the tiny-dnn output, they compared well
     * The results weren't exact, but I found a bug where the values were changing barely based on certain Neurons exceeding their stages and skipping certain steps
   * Found some major problems in the weights I was using after testing every single possible input pattern
     * Will find better weights in week 11
   * Forgot to put my week 10 update on week 10....

Week 11 Update:
   *Found better patterns and shapes to use together, am using a 2x2 box and a horizontal 2 length line
     * Will look into adding more objects that can still be successfully classified
     * Implemented these weights and biases into the project, and it classifies 8/10 possible patterns (6 from the horizontal 2 length line, and 4 from the 2x2 box)
   * Does not pull the image (pixels) from a element anymore, now can be hand drawn by the user
   * Tested hitting the network with "radiation", and it failed horribly every time
   * Working towards getting a reset to work
     * The neurons in the convolution already had their weights stored separately from their output values, whil the fully connected layer did not
       * I have successfully separated the weights and the output values for the fully-connected layer, and it works perfectly  
   * Added a reset element, as when the Neurons see it, they will reset the network
   * Created a presentation for this project

Week 12 Update:
   *Fixed bug with fully_connected layer when the simulation was going too fast for it to correctly pass the weights
   *Created scripts to get data from modifying parameters for training in tiny_dnn and then will compare the results to what I get from my pre-trained model
   *Added different objects, so It can classify up to 5 now, with a much worse percentage...
   *Wrote the abstract for this project

Week 13 Update:
   *Added more rotations and shifts of each shape, and this increased the classification rates for every number of classification patterns
     * Can no only classify 4, but way better percentages up by about 20-30% from the previous version
   * Also found a problem with the version I was using in tiny-dnn, but was able to fix it.  Was having to do with the rescaling of the outputs
   * Created more plots with the improved numbers and places them in my paper
   * Added a lot more to my paper including introduction, methods, some results, and some sort of conclusion/discussion
   * Corrected my abstract
   * Fixed my CNN in the MFM to be more robust, where when a big chunk is removed it will repair itself, and then when a reset is used it can be re-run

Week 14 Update:
   *Found a few bugs in the repairing and was able to improve it
   *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.