Artificial Neural Net

Artificial Neural Net preview image

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Uri_dolphin3 Uri Wilensky (Author)

Tags

computer science 

Tagged by Reuven M. Lerner over 10 years ago

Model group CCL | Visible to everyone | Changeable by group members (CCL)
Model was written in NetLogo 5.0.3 • Viewed 1080 times • Downloaded 87 times • Run 0 times
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links-own [weight]

breed [bias-nodes bias-node]
breed [input-nodes input-node]
breed [output-nodes output-node]
breed [hidden-nodes hidden-node]

turtles-own [activation err]

globals [
  epoch-error
  input-node-1   ;; keep the input and output nodes
  input-node-2   ;; in global variables so we can
  output-node-1  ;; refer to them directly
]

;;;
;;; SETUP PROCEDURES
;;;

to setup
  clear-all
  ask patches [ set pcolor gray + 2 ]
  set-default-shape bias-nodes "bias-node"
  set-default-shape input-nodes "circle"
  set-default-shape output-nodes "output-node"
  set-default-shape hidden-nodes "output-node"
  setup-nodes
  setup-links
  propagate
  reset-ticks
end 

to setup-nodes
  create-bias-nodes 1 [ setxy -5 5 ]
  ask bias-nodes [ set activation 1 ]
  create-input-nodes 1
  [ setxy -5 -1
    set input-node-1 self ]
  create-input-nodes 1
  [ setxy -5 1
    set input-node-2 self ]
  ask input-nodes [ set activation random 2 ]
  create-hidden-nodes 1 [ setxy 0 -1 ]
  create-hidden-nodes 1 [ setxy 0 1 ]
  ask hidden-nodes
  [ set activation random 2
    set size 1.5 ]
  create-output-nodes 1
  [ setxy 5 0
    set output-node-1 self ]
  ask output-nodes [ set activation random 2 ]
end 

to setup-links
  connect-all bias-nodes hidden-nodes
  connect-all bias-nodes output-nodes
  connect-all input-nodes hidden-nodes
  connect-all hidden-nodes output-nodes
end 

to connect-all [nodes1 nodes2]
  ask nodes1 [
    create-links-to nodes2 [
      set weight random-float 0.2 - 0.1
    ]
  ]
end 

to recolor
  ask turtles [
    set color item (step activation) [black white]
  ]
  ask links [
    set thickness 0.1 * abs weight
    ifelse weight > 0
      [ set color red ]
      [ set color blue ]
  ]
end 

;;;
;;; TRAINING PROCEDURES
;;;

to train
  set epoch-error 0
  repeat examples-per-epoch [
    ask input-nodes [ set activation random 2 ]
    propagate
    back-propagate
  ]
  tick
  set epoch-error epoch-error / examples-per-epoch
  plotxy ticks epoch-error
end 

;;;
;;; FUNCTIONS TO LEARN
;;;

to-report target-answer
  let a [activation] of input-node-1 = 1
  let b [activation] of input-node-2 = 1
  report ifelse-value run-result
    (word "a " target-function " b") [1][0]
end 

;;;
;;; PROPAGATION PROCEDURES
;;;

;; carry out one calculation from beginning to end

to propagate
  ask hidden-nodes [ set activation new-activation ]
  ask output-nodes [ set activation new-activation ]
  recolor
end 

to-report new-activation  ;; node procedure
  report sigmoid sum [[activation] of end1 * weight] of my-in-links
end 

;; changes weights to correct for errors

to back-propagate
  let example-error 0
  let answer target-answer

  ask output-node-1 [
    set err activation * (1 - activation) * (answer - activation)
    set example-error example-error + ( (answer - activation) ^ 2 )
  ]
  set epoch-error epoch-error + example-error
  ask hidden-nodes [
    set err activation * (1 - activation) * sum [weight * [err] of end2] of my-out-links
  ]
  ask links [
    set weight weight + learning-rate * [err] of end2 * [activation] of end1
  ]
end 

;;;
;;; MISC PROCEDURES
;;;

;; computes the sigmoid function given an input value and the weight on the link

to-report sigmoid [input]
  report 1 / (1 + e ^ (- input))
end 

;; computes the step function given an input value and the weight on the link

to-report step [input]
  report ifelse-value (input > 0.5) [1][0]
end 

;;;
;;; TESTING PROCEDURES
;;;

;; test runs one instance and computes the output

to test
  ;; output the result
  ifelse test-success? input-1 input-2
    [ user-message "Correct." ]
    [ user-message "Incorrect." ]
end 

to-report test-success? [n1 n2]
  ask input-node-1 [ set activation n1 ]
  ask input-node-2 [ set activation n2 ]
  propagate
  report target-answer = step [activation] of one-of output-nodes
end 

There are 9 versions of this model.

Uploaded by When Description Download
Uri Wilensky over 11 years ago Updated version tag Download this version
Uri Wilensky over 11 years ago Updated to version from NetLogo 5.0.3 distribution Download this version
Uri Wilensky about 12 years ago Updated to NetLogo 5.0 Download this version
Uri Wilensky almost 14 years ago Updated from NetLogo 4.1 Download this version
Uri Wilensky almost 14 years ago Updated from NetLogo 4.1 Download this version
Uri Wilensky almost 14 years ago Updated from NetLogo 4.1 Download this version
Uri Wilensky almost 14 years ago Updated from NetLogo 4.1 Download this version
Uri Wilensky almost 14 years ago Model from NetLogo distribution Download this version
Uri Wilensky almost 14 years ago Artificial Neural Net Download this version

Attached files

File Type Description Last updated
Artificial Neural Net.png preview Preview almost 11 years ago, by Reuven M. Lerner Download

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