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ThousandLights

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About ThousandLights

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  1. ThousandLights

    BackPropagation Help

    thats the code : Sub NN() 'const e = 2.718281828 alpha = 0.25 'get Data a = Cells(2, 1) b = Cells(2, 2) truth = a - b W13 = Cells(4, 1) W14 = Cells(4, 2) W23 = Cells(4, 3) W24 = Cells(4, 4) W35 = Cells(6, 2) W45 = Cells(6, 3) 'answer Sum1 = a + b Sum2 = a + b f1 = 1 / (1 + e ^ (-Sum1)) f2 = 1 / (1 + e ^ (-Sum2)) Sum3 = f1 + W13 + f2 * W23 Sum4 = f1 * W14 + f2 * W24 f3 = 1 / (1 + e ^ (-Sum3)) f4 = 1 / (1 + e ^ (-Sum4)) Sum5 = f3 * W35 + f4 * W45 f5 = 1 / (1 + e ^ (-Sum5)) answer = -1 + f5 * 2 Cells(2, 4) = answer 'backPropagate err5 = (truth - answer + 1) / 2 err3 = err5 * W35 err4 = err5 * W45 err1 = err3 * W13 + err4 * W14 err2 = err3 * W23 + err4 * W24 Cells(2, 5) = err5 'update W13 = W13 + alpha * (f3 * (1 - f3)) * (f1 * W13) * err3 W23 = W23 + alpha * (f3 * (1 - f3)) * (f2 * W23) * err3 W14 = W14 + alpha * (f4 * (1 - f4)) * (f1 * W14) * err4 W24 = W24 + alpha * (f4 * (1 - f4)) * (f2 * W24) * err4 W35 = W35 + alpha * (f5 * (1 - f5)) * (f3 * W35) * err5 W45 = W45 + alpha * (f5 * (1 - f5)) * (f4 * W45) * err5 'show weghits Cells(4, 1) = W13 Cells(4, 2) = W14 Cells(4, 3) = W23 Cells(4, 4) = W24 Cells(6, 2) = W35 Cells(6, 3) = W45 End Sub actually i didnt add any bias , for i didnt see it in some of the written algorithems . so how does it sepose to be ? aint the bias an error that we dont know wich is exprcted to be zero ? what should i do with it ?
  2. ThousandLights

    BackPropagation Help

    Hmm .. Its combaind with the excel datasheet, nothing special to add except refering excel cells , but ill post it anyway when im on my comp .i belive the ann shold answer more than just positive / negatuve ,for It is only a binary question ..
  3. ThousandLights

    BackPropagation Help

    Sorry the end is written twice , stupied iphone ..
  4. ThousandLights

    BackPropagation Help

    I transformed the output into those trerms, if theres closest values to 1 means big positive diffrence , the closest to 0 means negative gape and 0.5 is close to equals . The thing is that the net answers about the same answer for all cases , so i guess its beacuse of the expected values of randonm distribution .. Just a guess . Can it be that this kind of problem is unsolveable with ANN or the net structure doesnt fit the problem ? How can i know if iys solveable or not and what structure to choose ? Do you know a problem wich is solvable for sure and the net structure that i shold apply? Do you know any other problem wich is easier to solve The fact is that it doesnt learn . Can it br that this is a type of problam that cant be solved with ANN? Or that this net structure doesnt fit
  5. ThousandLights

    BackPropagation Help

    hi adaline . thanx but i belive thats not it . first : how do i create an output of boolean values ? wide range values etc ? i output neouron has a 0-1 sigmoid function but it`s translated into -1 till 1 terms . and even if it must be just 0-1 without scalling , i tried it in deffrent ways , and it didnt improve .. about the delta , i belive its [color=#1C2837][size=2]alpha*(1-output)*output*(error)*inputi because you mentioned the formula without the input and i saw this while Xij is a certain input . thus i still dont know what the problem is , and i dont really know if this net structure is sepose to be enough for this task or may be its imposible to solve this way ? i wil be glad if someone hhas simple written code of BP wich i can learn from as an example .. [color=#1C2837][size=2][sub] [/sub]
  6. ThousandLights

    BackPropagation Help

    p.s the net has to two input numbers , two neourons that recives them ( neourons 1, 2) two neourons as hidden layer (neouron 3,4) output neouron 5.
  7. ThousandLights

    BackPropagation Help

    hi guys, my first post here, excited i`ve been trying to learn some of the BP algorithem and wrote the simplest code just to be sure i understand the basics, but somehow the net output is always about the same and i cant figure why ? if anyone can take a look i would be very gratefull . (should be easy for someone whose familiar with bp). as input I entered two numbers between 0-1 wich are a and b the output should be the subtraction between them ,output= (a - b) , might be negative.. i used simple sigmoid function . was written in VB , i pasted just the hart of the code , its just for understanding , procedural way . some Technical stuff , i scales the output to the range of -1 to 1 , should the output neuron recive an un scaled value of the error? like i did here or how should it be? so can someone explain What is wrong ? thank you.. e = 2.718281828 alpha = 0.1 'input = two numbers between 0-1 a = Cells(2, 1) b = Cells(2, 2) truth = a - b 'correct answer Sum1 = a + b Sum2 = a + b f1 = 1 / (1 + e ^ (-Sum1)) f2 = 1 / (1 + e ^ (-Sum2)) Sum3 = f1 + W13 + f2 * W23 Sum4 = f1 * W14 + f2 * W24 f3 = 1 / (1 + e ^ (-Sum3)) f4 = 1 / (1 + e ^ (-Sum4)) Sum5 = f3 * W35 + f4 * W45 f5 = 1 / (1 + e ^ (-Sum5)) answer = -1 + f5 * 2 ' need to spred over the area : -1 till 1 'backPropagate err5 = (truth - answer + 1) / 2 err3 = err5 * W35 err4 = err5 * W45 err1 = err3 * W13 + err4 * W14 err2 = err3 * W23 + err4 * W24 'update wieghts W13 = W13 + alpha * (f3 * (1 - f3)) * (f1 * W13) * err3 W23 = W23 + alpha * (f3 * (1 - f3)) * (f2 * W23) * err3 W14 = W14 + alpha * (f4 * (1 - f4)) * (f1 * W14) * err4 W24 = W24 + alpha * (f4 * (1 - f4)) * (f2 * W24) * err4 W35 = W35 + alpha * (f5 * (1 - f5)) * (f3 * W35) * err5 W45 = W45 + alpha * (f5 * (1 - f5)) * (f4 * W45) * err5
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