Question

require(neuralnet)
require(nnet)
require(ggplot2)
set.seed(10)
songs <- read.csv("songs.csv")
no_rows <- 64
no_columns <- 13
feature_vector_size = no_rows * no_columns
names(songs) <- vector(mode ="character",length=0)
for (i in 1:(feature_vector_size+1))
names(songs)[i] <- i
names(songs)[1] = "label"
train <- cbind(songs[, 2:(feature_vector_size+1)], 
class.ind(as.factor(songs$label)))
# Set labels name
names(train) <- c(names(songs)[2:
(feature_vector_size+1)],"l1","l2","l3")
scl <- function(x){ (x - min(x))/(max(x) - min(x)) }
train[, 1:feature_vector_size] <- data.frame(lapply(train[, 
1:feature_vector_size], scl))
n <- names(train)
f <- as.formula(paste("l1 + l2 + l3 ~", paste(n[!n %in% 
c("l1","l2","l3")], collapse = " + ")))  
nn <- neuralnet(f,
            data = train,
            hidden = c(832, 100, 3),
            act.fct = "logistic",
            linear.output = FALSE,
            lifesign = "minimal")

songs.csv包含833列，第一列为标签（1或2或3），2至833包含要素（实际值）。错误来自神经网络功能： term.formula（公式）中的错误：无效的模型公式 ExtractVars

terms.formula（公式）中的错误：ExtractVars中的模型公式无效;在神经网络R

0 个答案: