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source(paste(getwd(), "Layer_refCls.r", sep = "/"))
attr_list <- list(
node_counts = "numeric",
l_rate = "numeric",
layers = "list"
)
func_i <- function(node_counts, l_rate, act_func_names) {
node_counts <<- node_counts
l_rate <<- l_rate
layers <<- list()
for (i in 1:(length(node_counts) - 1)) {
layers <<- c(
layers,
Layer(
node_counts[i + 1],
node_counts[i],
act_func_names[i + 1]
)
)
}
}
# forward propagation caller as passable variable
func_1 <- function(act_in) {
act_i <- act_in
for (i in 1:length(layers)) {
act_i <- layers[[i]]$act_out(act_i)
}
return(act_i)
}
# back propagation caller as passable variable
func_2 <- function(act_in, tar_out) {
act_out <- fw_prop(act_in)
cost <- sum((act_out - tar_out) ^ 2)
dc_da <- 2 * (act_out - tar_out)
for (i in length(layers):1) {
dc_da <- layers[[i]]$back_prop(dc_da)
}
return(cost)
}
# gradient descent as passable variable
func_3 <- function() {
for (i in length(layers):1) {
layers[[i]]$grad_desc(l_rate)
}
}
# these are class methods, so not adjustable instance-to-instance
meth_list <- list(
initialize = func_i,
fw_prop = func_1,
back_prop = func_2,
grad_desc = func_3
)
NNetwork <- setRefClass("NNetwork", fields = attr_list, methods = meth_list)
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