Htykuut

I am writing a neural network from scratch in r programming language and apply it on iris dataset.
here

d1f is a matrix of propagation error of output layer

d2f is a matrix of propagation error of hidden layer

a1,a2,a3,a4 are activation output of hidden layer

o1,o2,o3 are activation output of output layer

w1b & w2b are the matrix of weights of hidden layer and output layer respectively

j is used to store cost of each observation

till now I had written it for only single pass through the whole dataset. So only propagation error is calculated but weights will not get updated.
whenever I run this code I got an error

Error in a1(1 - a1) : could not find function "a1"

bt after termination i can see the value of a1 on the right side of R-Studio.
It seems like is d2f is not calculated even.so what i am doing wrong.

here is the code

library(datasets)

library("phonTools", lib.loc="~/R/win-library/3.5")

iris.df =data(iris)

data(iris)

data("iris")

iris.df=iris



J = vector(mode = "numeric" , length = nrow(iris.df) )



attach(iris.df)



iris.df$y1 = 0



iris.df$y2 = 0



iris.df$y3 = 0



iris.df$y1[iris.df$Species =="setosa"] = 1



iris.df$y2[iris.df$Species =="versicolor"] = 1



iris.df$y3[iris.df$Species =="virginica"] = 1



w1b = matrix(rexp(20, rate=.1), ncol=5)  #Declaring weights between 1st             layer



w1b = wb1*0.24 - 0.12



q = c(1,1,1,1)



#w1b = cbind(q,w1b)



w2b = matrix(rexp(12, rate=.1), ncol=4)



w2b = w2b*0.24 - 0.12



q = c(1,1,1)



#w2b = cbind(q,w2b)



d1f = zeros(3,4)



d2f = zeros(4,5)



sigmoid = function(z)

{

  sig = 1/( 1 + exp(-z) )



  return(sig)  

}



for (i in 1:150) {





# Forward Propagation For First Layer



a1 = sigmoid( 1*w1b[1,1] + iris.df$Sepal.Length[i]*w1b[1,2] +     iris.df$Sepal.Width[i]*w1b[1,3] + iris.df$Petal.Length[i]*w1b[1,4] +     iris.df$Petal.Width[i]*w1b[1,5] )



a2 = sigmoid( 1*w1b[2,1] + iris.df$Sepal.Length[i]*w1b[2,2] + iris.df$Sepal.Width[i]*w1b[2,3] + iris.df$Petal.Length[i]*w1b[2,4] + iris.df$Petal.Width[i]*w1b[2,5] )



a3 = sigmoid( 1*w1b[3,1] + iris.df$Sepal.Length[i]*w1b[3,2] + iris.df$Sepal.Width[i]*w1b[3,3] + iris.df$Petal.Length[i]*w1b[3,4] + iris.df$Petal.Width[i]*w1b[3,5] )



a4 = sigmoid( 1*w1b[4,1] + iris.df$Sepal.Length[i]*w1b[4,2] + iris.df$Sepal.Width[i]*w1b[4,3] + iris.df$Petal.Length[i]*w1b[4,4] + iris.df$Petal.Width[i]*w1b[4,5] )



#Forward Propagation For Second Layer



o1 = sigmoid( 1*w2b[1,1] + a1*w2b[1,2] + a1*w2b[1,3] + a1*w2b[1,4] )



o2 = sigmoid( 1*w2b[2,1] + a2*w2b[2,2] + a2*w2b[2,3] + a2*w2b[2,4] )



o3 = sigmoid( 1*w2b[3,1] + a3*w2b[3,2] + a3*w2b[3,3] + a3*w2b[3,4] )









#Backward Propagation For First Layer



d1f[1,1] = d1f[1,1] + o1*(1-o1) *(o1-y1[i])  #For Baised Node



d1f[2,1] = d1f[2,1] + o2*(1-o2) *(o2-y2[i])  #For Baised Node



d1f[3,1] = d1f[3,1] + o3*(1-o3) *(o3-y3[i])  #For Baised Node







d1f[1,2] = d1f[1,2] + a1*o1*(1-o1)*(o1-y1[i])



d1f[2,2] = d1f[2,2] + a1*o2*(1-o2)*(o2-y2[i])



d1f[3,2] = d1f[3,2] + a1*o3*(1-o3)*(o3-y3[i])









d1f[1,3] = d1f[1,3] + a2*o1*(1-o1)*(o1-y1[i])



d1f[2,3] = d1f[2,3] + a2*o2*(1-o2)*(o2-y2[i])



d1f[3,3] = d1f[3,3] + a2*o3*(1-o3)*(o3-y3[i])









d1f[1,4] = d1f[1,4] + a3*o1*(1-o1)*(o1-y1[i])



d1f[2,4] = d1f[2,4] + a3*o2*(1-o2)*(o2-y2[i])



d1f[3,4] = d1f[3,4] + a3*o3*(1-o3)*(o3-y3[i])







#Backward Propagation For Second Layer



d2f[1,2] = d2f[1,2] + iris.df$Sepal.Length[i] * a1(1-a1) * ( (o1-y1[i])*o1*(1-o1)*w2b[1,2] + (o2-y2[i])*o2*(1-o2)*w2b[2,2] + (o3-y3[i])*o3*(1-o3)*w2b[3,2] )



d2f[1,3] = d2f[1,3] + iris.df$Sepal.Width[i] * a1(1-a1) * ( (o1-y1[i])*o1*(1-o1)*w2b[1,2] + (o2-y2[i])*o2*(1-o2)*w2b[2,2] + (o3-y3[i])*o3*(1-o3)*w2b[3,2] )



d2f[1,4] = d2f[1,4] + iris.df$Petal.Length[i] * a1(1-a1) * ( (o1-y1[i])*o1*(1-o1)*w2b[1,2] + (o2-y2[i])*o2*(1-o2)*w2b[2,2] + (o3-y3[i])*o3*(1-o3)*w2b[3,2] )



d2f[1,5] = d2f[1,5] + iris.df$Petal.Width[i] * a1(1-a1) * ( (o1-y1[i])*o1*(1-o1)*w2b[1,2] + (o2-y2[i])*o2*(1-o2)*w2b[2,2] + (o3-y3[i])*o3*(1-o3)*w2b[3,2] )





###########



d2f[1,1] = d2f[1,1] + a1(1-a1) * ( (o1-y1[i])*o1*(1-o1)*w2b[1,2] + (o2-y2[i])*o2*(1-o2)*w2b[2,2] + (o3-y3[i])*o3*(1-o3)*w2b[3,2] ) #For Biased Node



d2f[2,1] = d2f[2,1] + a2(1-a2) * ( (o1-y1[i])*o1*(1-o1)*w2b[1,2] + (o2-y2[i])*o2*(1-o2)*w2b[2,2] + (o3-y3[i])*o3*(1-o3)*w2b[3,2] ) #For Biased Node



d2f[3,1] = d2f[3,1] + a3(1-a3) * ( (o1-y1[i])*o1*(1-o1)*w2b[1,2] + (o2-y2[i])*o2*(1-o2)*w2b[2,2] + (o3-y3[i])*o3*(1-o3)*w2b[3,2] ) #For Biased Node



d2f[4,1] = d2f[4,1] + a4(1-a4) * ( (o1-y1[i])*o1*(1-o1)*w2b[1,2] + (o2-y2[i])*o2*(1-o2)*w2b[2,2] + (o3-y3[i])*o3*(1-o3)*w2b[3,2] ) #For Biased Node











d2f[2,2] = d2f[2,2] + iris.df$Sepal.Length[i] * a2(1-a2) * ( (o1-y1[i])*o1*(1-o1)*w2b[1,2] + (o2-y2[i])*o2*(1-o2)*w2b[2,2] + (o3-y3[i])*o3*(1-o3)*w2b[3,2] )



d2f[2,3] = d2f[2,3] + iris.df$Sepal.Width[i] * a2(1-a2) * ( (o1-y1[i])*o1*(1-o1)*w2b[1,2] + (o2-y2[i])*o2*(1-o2)*w2b[2,2] + (o3-y3[i])*o3*(1-o3)*w2b[3,2] )



d2f[2,4] = d2f[2,4] + iris.df$Petal.Length[i] * a2(1-a2) * ( (o1-y1[i])*o1*(1-o1)*w2b[1,2] + (o2-y2[i])*o2*(1-o2)*w2b[2,2] + (o3-y3[i])*o3*(1-o3)*w2b[3,2] )



d2f[2,5] = d2f[2,5] + iris.df$Petal.Width[i] * a2(1-a2) * ( (o1-y1[i])*o1*(1-o1)*w2b[1,2] + (o2-y2[i])*o2*(1-o2)*w2b[2,2] + (o3-y3[i])*o3*(1-o3)*w2b[3,2] )









d2f[3,2] = d2f[3,2] + iris.df$Sepal.Length[i] * a3(1-a3) * ( (o1-y1[i])*o1*(1-o1)*w2b[1,2] + (o2-y2[i])*o2*(1-o2)*w2b[2,2] + (o3-y3[i])*o3*(1-o3)*w2b[3,2] )



d2f[3,3] = d2f[3,3] + iris.df$Sepal.Width[i] * a3(1-a3) * ( (o1-y1[i])*o1*(1-o1)*w2b[1,2] + (o2-y2[i])*o2*(1-o2)*w2b[2,2] + (o3-y3[i])*o3*(1-o3)*w2b[3,2] )



d2f[3,4] = d2f[3,4] + iris.df$Petal.Length[i] * a3(1-a3) * ( (o1-y1[i])*o1*(1-o1)*w2b[1,2] + (o2-y2[i])*o2*(1-o2)*w2b[2,2] + (o3-y3[i])*o3*(1-o3)*w2b[3,2] )



d2f[3][5] = d2f[3,5] + iris.df$Petal.Width[i] * a3(1-a3) * ( (o1-y1[i])*o1*(1-o1)*w2b[1,2] + (o2-y2[i])*o2*(1-o2)*w2b[2,2] + (o3-y3[i])*o3*(1-o3)*w2b[3,2] )









d2f[4,2] = d2f[4,2] + iris.df$Sepal.Length[i] * a4(1-a4) * ( (o1-y1[i])*o1*(1-o1)*w2b[1,2] + (o2-y2[i])*o2*(1-o2)*w2b[2,2] + (o3-y3[i])*o3*(1-o3)*w2b[3,2] )



d2f[4][3] = d2f[4,3] + iris.df$Sepal.Width[i] * a4(1-a4) * ( (o1-y1[i])*o1*(1-o1)*w2b[1,2] + (o2-y2[i])*o2*(1-o2)*w2b[2,2] + (o3-y3[i])*o3*(1-o3)*w2b[3,2] )



d2f[4,4] = d2f[4,4] + iris.df$Petal.Length[i] * a4(1-a4) * ( (o1-y1[i])*o1*(1-o1)*w2b[1,2] + (o2-y2[i])*o2*(1-o2)*w2b[2,2] + (o3-y3[i])*o3*(1-o3)*w2b[3,2] )



d2f[4,5] = d2f[4,5] + iris.df$Petal.Width[i] * a4(1-a4) * ( (o1-y1[i])*o1*(1-o1)*w2b[1,2] + (o2-y2[i])*o2*(1-o2)*w2b[2,2] + (o3-y3[i])*o3*(1-o3)*w2b[3,2] )









cost = ( iris.df$y1[i] * log(o1) + (1-y1[i]) * log(1-o1) + iris.df$y2[i] * log(o2) + (1-y2[i]) * log(1-o2) + iris.df$y3[i] * log(o3) + (1-y3[i]) * log(1-o3) )/nrow(iris.df)



j[i] = cost

}

If you want to get the product of a1 and 1-a1, you explicitly have to write the '*' in between.

d2f[1,2] = d2f[1,2] + iris.df$Sepal.Length[i] * a1*(1-a1) * ( (o1-y1[i])*o1*(1-o1)*w2b[1,2] + (o2-y2[i])*o2*(1-o2)*w2b[2,2] + (o3-y3[i])*o3*(1-o3)*w2b[3,2] )

If you open parentheses after a1, r thinks youre referring to a function called a1, because thats the syntax for functions. The same problem is in multiple occasions of your code. You should always state the arithmetic operators you want to use.