Pass reactive object as input to function in a shiny app - r

Question

In the shiny app below I want to pass as input in the initialize_NN() function the layers() which is a reactive object. But when I try to pass it as layers() instead of layers I get an error in my console like unexpected token'layers',expected RPAREN. Any solution to this or any workaround?The initialize_NN() do not expect to give something now but I just want to know how could I pass reactive input into it.
library(shiny)
library(dplyr)
### user inter phase
ui <- fluidPage(
### App title ----
titlePanel(
#title=div(img(src="pics/IRP_NHSc.jpg", width="99%"))
title="Network App"
)
,
# Sidebar layout with input and output definitions ----
sidebarLayout(
# Sidebar panel for inputs ----
sidebarPanel(
sliderInput("lay","Adjust layers",min=2,max = 6,value = 2,step=1),
sliderInput("neu","Adjust Neurons",min=10,max = 60,value = 50,step=1)
),
### Main panel for displaying outputs ----
mainPanel(
)
)
)
#### Server
server <- function(input, output, session) {
layers <- reactive({
l<-c(1, rep(input$neu, input$lay), 1)
})
initialize_NN <- function(layers()){
weights <-c()
biases <- c()
num_layers <- length(layers())-1
for (i in seq(num_layers)){
W <- xavier_init(c(layers()[i], layers()[i+1]))
weights <- c(weights, W)
biases <- c(biases, b)
}
return(list(Weights = weights, Biases = biases))
}
obj <- initialize_NN(layers=layers())
weights <- obj$Weights
biases <- obj$Biases
neural_net <- function(x_, weights, biases){
num_layers <- length(weights) + 1
H <- x_
for (l in seq(num_layers-2)){
W <- weights[[l]]
b <- biases[[l]]
H <- tf$nn$tanh(tf$add(tf$matmul(H, W), b))
}
W <- tail(weights, n=1)[1]
b <- tail(biases, n=1)[1]
Y <- tf$add(tf$matmul(H, W), b)
return(Y)
}
}
shinyApp(ui = ui, server = server)

Answer 1

Define function initialize_NN() with "normal" arguments:
initialize_NN <- function(layers){
weights <-c()
biases <- c()
num_layers <- length(layers)-1
for (i in seq(num_layers)){
W <- xavier_init(c(layers[i], layers[i+1]))
weights <- c(weights, W)
biases <- c(biases, b)
}
return(list(Weights = weights, Biases = biases))
}
Then wherever you call the function pass your reactive as the argument layers:
initialize_NN(layers = layers())
Edit 1:
As #jpdugo17 pointed out in the comments, it needs to be done inside a reactive context: whether a reactive(), observe(), observeEvent() etc.
For example:
r_NN <- reactive({
initialize_NN(layers = layers())
})