Child of Crowded Station

No preview image

1 collaborator

Default-person Ye Xue (Author)

Tags

(This model has yet to be categorized with any tags)
Child of model Crowded Station preview imageCrowded Station
Model group MAM-2016 | Visible to everyone | Changeable by everyone
Model was written in NetLogo 6.0-M5 • Viewed 187 times • Downloaded 20 times • Run 0 times
Download the 'Child of Crowded Station' modelDownload this modelEmbed this model

Do you have questions or comments about this model? Ask them here! (You'll first need to log in.)


Comments and Questions

Please start the discussion about this model! (You'll first need to log in.)

Click to Run Model

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     ;; Determines the nodes output
  err            ;; Used by backpropagation to feed error backwards
]

globals [
  epoch-error    ;; measurement of how many training examples the network got wrong in the epoch
  input-node-1   ;; keep the input and output nodes
  input-node-2   ;; in global variables so we can
  input-node-3
  output-node-1  ;; refer to them directly
  output
]

;;;
;;; SETUP PROCEDURES
;;;

to setup
  clear-all
  ask patches [ set pcolor gray ]
  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"
  set-default-shape links "small-arrow-shape"
  setup-nodes
  setup-links
  propagate
  reset-ticks
end 

to setup-nodes
  create-bias-nodes 1 [ setxy -4 6 ]
  ask bias-nodes [ set activation 1 ]
  create-input-nodes 1 [
    setxy -6 -2
    set input-node-1 self
  ]
  create-input-nodes 1 [
    setxy -6 2
    set input-node-2 self
  ]
  create-input-nodes 1 [
    setxy -6 4
    set input-node-3 self
  ]
  ask input-nodes [ set activation random 2 ]
  create-hidden-nodes 1 [ setxy 0 -2 ]
  create-hidden-nodes 1 [ setxy 0  2 ]
  create-hidden-nodes 1 [ setxy 0  4 ]
  ;create-hidden-nodes 1 [ setxy 3 -2 ]
  ;create-hidden-nodes 1 [ setxy 3  2 ]
  ;create-hidden-nodes 1 [ setxy 3  4 ]
  ask hidden-nodes [
    set activation random 2
    set size 1.5
  ]
  create-output-nodes 1 [
    setxy 7 0
    set output-node-1 self
    set activation random 2
  ]
end 

;; connect input nodes with hidden nodes
;; connect bias-nodes with hidden nodes and output nodes
;; connect hidden nodes with output nodes

to setup-links
  connect-all bias-nodes hidden-nodes
  connect-all bias-nodes output-nodes
  connect-all input-nodes hidden-nodes ;with [xcor = 0]
  connect-all hidden-nodes output-nodes
  ;connect-all hidden-nodes with [xcor = 0] hidden-nodes with [xcor = 3]
  ;connect-all hidden-nodes with [xcor = 3] output-nodes
end 

to connect-all [ nodes1 nodes2 ]
  ask nodes1 [
    create-links-to nodes2 [
      set weight random-float 1
    ]
  ]
end 

;; color nodes

to recolor
  ask turtles [
    set color item (scale activation) [ black white red ]
  ]
  ask links [
    set thickness 0.05 * abs weight
    ifelse show-weights? [
      set label precision weight 4
    ] [
      set label ""
    ]
    ifelse weight > 0
      [ set color [ 255 0 0 196 ] ] ; transparent red
      [ set color [ 0 0 255 196 ] ] ; transparent light blue
  ]
end 

to-report scale [input]
  ifelse input > 0.8
  [ report 2 ]
  [ ifelse input < 0.2 [ report 0 ] [ report 1 ] ]
end 

;;;
;;; TRAINING PROCEDURES
;;;

to train
  set epoch-error 0
  repeat examples-per-epoch [
    ask input-node-1 [ set activation random 20 ]
    ask input-node-2 [ set activation random 20 ]
    ask input-node-3 [ set activation random 20 ]
    propagate
    backpropagate
  ]
  set epoch-error epoch-error / examples-per-epoch
  tick
end 

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

to-report target-answer
  let a [ activation ] of input-node-1
  let b [ activation ] of input-node-2
  let c [ activation ] of input-node-3
  ;let s (a + b + c)

  ifelse (b <= a and b <= c)
  [ report 0.5 ]
  [
    ifelse (a < b and a < c)
    [ report 0 ]
    [ report 1 ]
  ]
  ;; run-result will interpret target-function as the appropriate boolean operator
  ;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 

;; Determine the activation of a node based on the activation of its input nodes

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 backpropagate
  let example-error 0
  let answer target-answer
  ;print target-answer

  ask output-node-1 [
    ;; `activation * (1 - activation)` is used because it is the
    ;; derivative of the sigmoid activation function. If we used a
    ;; different activation function, we would use its derivative.
    ;print activation
    set err activation * (1 - activation) * (answer - activation)
    set example-error example-error + ((answer - activation) ^ 2)
  ]
  set epoch-error epoch-error + example-error

  ;; The hidden layer nodes are given error values adjusted appropriately for their
  ;; link weights
  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]
  ifelse input > 0.8
  [ report 1 ]
  [ ifelse input < 0.2 [ report 0 ] [ report 0.5 ] ]
  ;report ifelse-value (input > 0.5) [ 1 ] [ 0 ]
end 

;;;
;;; TESTING PROCEDURES
;;;

;; test runs one instance and computes the output

to test
  let result result-for-inputs input-1 input-2 input-3
  set output step result
  let correct? ifelse-value (result = target-answer) [ "correct" ] [ "incorrect" ]
  user-message (word
    "The expected answer for " input-1 " " input-2 " " input-3 " is " target-answer ".\n\n"
    "The network reported " result ", which is " correct? ".")
end 

to test-with [i1 i2 i3]
  let result result-for-inputs i1 i2 i3
  set output step result
end 

to-report result-for-inputs [n1 n2 n3]
  ask input-node-1 [ set activation n1 ]
  ask input-node-2 [ set activation n2 ]
  ask input-node-3 [ set activation n3 ]
  propagate
  print [ activation ] of one-of output-nodes
  report step [ activation ] of one-of output-nodes
end 

There are 3 versions of this model.

Uploaded by When Description Download
Ye Xue almost 3 years ago Final version Download this version
Ye Xue almost 3 years ago version 1.1-new measure Download this version
Ye Xue almost 3 years ago version 1.0 Download this version

Attached files

No files

Parent: Crowded Station

This model does not have any descendants.

Graph of models related to 'Child of Crowded Station'