neural-network-2-layer-NEWEST.R

# DNN2.R
# Bastiaan Quast
# [email protected]

# derivative
der <- function(x){
  x * (1-x)
}

# input data
inputs = matrix(c(0,0,1,
                  0,1,1,
                  1,0,1,
                  1,1,1), nrow=4, byrow=TRUE)

# output data
true_values = matrix(c(0,
                       1,
                       1,
                       0), nrow=4)


weights_0 = matrix(runif(n = 12, min=-1, max=1), nrow=3)
weights_1 = matrix(runif(n =  4, min=-1, max=1), nrow=4)

for (j in 1:60000) {
  layer_1 = plogis( inputs  %*% weights_0 )
  layer_2 = plogis( layer_1      %*% weights_1 )

  layer_2_update = (true_values - layer_2 ) * der(layer_2)
  layer_1_update = layer_2_update %*% t(weights_1) * der(layer_1)
  
  weights_1 = weights_1 + t(layer_1) %*% layer_2_update
  weights_0 = weights_0 + t(inputs) %*% layer_1_update
}