anchor.diff:  
   [.text-center]
   .Similarity between vectors X and W
  -image::sim_neuron/vector_similarity.png[vector_similarity]
  +image::sim_neuron/vector_similarity.png[vector_similarity, 300]
   {empty} +
   
   If we view a neuron as fundamental template matching unit, then various magnitude (length) of \(X\) and \(W\) should yield the same output. If we constrain \(W\) to be a unit vector (\(|W| = 1\)), then backprop would only need to search along points on a hypersphere of unit radius. Although the hypersphere contains infinitely many points, with sufficient discretization, the search space intuitively _feels_ a lot smaller than the entire weight vector space.
  
positive.code: 
  [.text-center]
  .Training convergence graphs of loss and accuracy on validation set
  image::sim_neuron/convergence.png[convergence_graphs]
  {empty} +
  
  TODO: Compare learned conv filters between old/new model.
  
negative.code: 
  [.text-center]
  .Test model
  image::sim_neuron/model.png[test_model, 300]
  {empty} +
  
  == Results
  