我在R中使用neuralnet包，但是在将绘图保存到磁盘时遇到了问题。
data(iris)
attach(iris)
library(neuralnet)
nn <- neuralnet(as.numeric(Species) ~ Sepal.Length + Sepal.Width + Petal.Length + Petal.Width, data = iris)
png("test.png")
plot(nn)
dev.off()
尝试引用plot.nn()，file a character string naming the plot to write to. If not stated,
the plot will not be saved.
部分说明：
png("test.png")
plot(nn, file = "test.png")
dev.off()
但是，执行以下操作无法保存有效的图表：
Mac OSX 10.7.3
平台：
platform x86_64-apple-darwin11.3.0
arch x86_64
os darwin11.3.0
system x86_64, darwin11.3.0
status
major 2
minor 15.1
year 2012
month 06
day 22
svn rev 59600
language R
version.string R version 2.15.1 (2012-06-22)
nickname Roasted Marshmallows
R版：
{{1}}
注意：R是通过macports安装的
答案 0 :(得分：6)
在与Frauke Guenther（neuralnet的维护者）交谈后，她解释了问题在于neuralnet能够同时绘制多个神经网络。
plot(..., file = "")应该已将文件保存到磁盘，但似乎无法正常运行，修补程序很快就会修补到程序包本身，但是她平时提供了快速{{1}修复，这将允许人们将绘图保存到磁盘。
plot.nn()将上面的代码粘贴到您的R互动提示中，当您希望保存您的地图时，请执行以下操作：
plot.nn <-
function (x, rep = NULL, x.entry = NULL, x.out = NULL, radius = 0.15,
arrow.length = 0.2, intercept = TRUE, intercept.factor = 0.4,
information = TRUE, information.pos = 0.1, col.entry.synapse = "black",
col.entry = "black", col.hidden = "black", col.hidden.synapse = "black",
col.out = "black", col.out.synapse = "black", col.intercept = "blue",
fontsize = 12, dimension = 6, show.weights = TRUE, file = NULL,
...)
{
net <- x
if (is.null(net$weights))
stop("weights were not calculated")
if (!is.null(file) && !is.character(file))
stop("'file' must be a string")
if (is.null(rep)) {
for (i in 1:length(net$weights)) {
if (!is.null(file))
file.rep <- paste(file, ".", i, sep = "")
else file.rep <- NULL
#dev.new()
plot.nn(net, rep = i, x.entry, x.out, radius, arrow.length,
intercept, intercept.factor, information, information.pos,
col.entry.synapse, col.entry, col.hidden, col.hidden.synapse,
col.out, col.out.synapse, col.intercept, fontsize,
dimension, show.weights, file.rep, ...)
}
}
else {
if (is.character(file) && file.exists(file))
stop(sprintf("%s already exists", sQuote(file)))
result.matrix <- t(net$result.matrix)
if (rep == "best")
rep <- as.integer(which.min(result.matrix[, "error"]))
if (rep > length(net$weights))
stop("'rep' does not exist")
weights <- net$weights[[rep]]
if (is.null(x.entry))
x.entry <- 0.5 - (arrow.length/2) * length(weights)
if (is.null(x.out))
x.out <- 0.5 + (arrow.length/2) * length(weights)
width <- max(x.out - x.entry + 0.2, 0.8) * 8
radius <- radius/dimension
entry.label <- net$model.list$variables
out.label <- net$model.list$response
neuron.count <- array(0, length(weights) + 1)
neuron.count[1] <- nrow(weights[[1]]) - 1
neuron.count[2] <- ncol(weights[[1]])
x.position <- array(0, length(weights) + 1)
x.position[1] <- x.entry
x.position[length(weights) + 1] <- x.out
if (length(weights) > 1)
for (i in 2:length(weights)) {
neuron.count[i + 1] <- ncol(weights[[i]])
x.position[i] <- x.entry + (i - 1) * (x.out -
x.entry)/length(weights)
}
y.step <- 1/(neuron.count + 1)
y.position <- array(0, length(weights) + 1)
y.intercept <- 1 - 2 * radius
information.pos <- min(min(y.step) - 0.1, 0.2)
if (length(entry.label) != neuron.count[1]) {
if (length(entry.label) < neuron.count[1]) {
tmp <- NULL
for (i in 1:(neuron.count[1] - length(entry.label))) {
tmp <- c(tmp, "no name")
}
entry.label <- c(entry.label, tmp)
}
}
if (length(out.label) != neuron.count[length(neuron.count)]) {
if (length(out.label) < neuron.count[length(neuron.count)]) {
tmp <- NULL
for (i in 1:(neuron.count[length(neuron.count)] -
length(out.label))) {
tmp <- c(tmp, "no name")
}
out.label <- c(out.label, tmp)
}
}
grid.newpage()
pushViewport(viewport(name = "plot.area", width = unit(dimension,
"inches"), height = unit(dimension, "inches")))
for (k in 1:length(weights)) {
for (i in 1:neuron.count[k]) {
y.position[k] <- y.position[k] + y.step[k]
y.tmp <- 0
for (j in 1:neuron.count[k + 1]) {
y.tmp <- y.tmp + y.step[k + 1]
result <- calculate.delta(c(x.position[k],
x.position[k + 1]), c(y.position[k], y.tmp),
radius)
x <- c(x.position[k], x.position[k + 1] - result[1])
y <- c(y.position[k], y.tmp + result[2])
grid.lines(x = x, y = y, arrow = arrow(length = unit(0.15,
"cm"), type = "closed"), gp = gpar(fill = col.hidden.synapse,
col = col.hidden.synapse, ...))
if (show.weights)
draw.text(label = weights[[k]][neuron.count[k] -
i + 2, neuron.count[k + 1] - j + 1], x = c(x.position[k],
x.position[k + 1]), y = c(y.position[k],
y.tmp), xy.null = 1.25 * result, color = col.hidden.synapse,
fontsize = fontsize - 2, ...)
}
if (k == 1) {
grid.lines(x = c((x.position[1] - arrow.length),
x.position[1] - radius), y = y.position[k],
arrow = arrow(length = unit(0.15, "cm"),
type = "closed"), gp = gpar(fill = col.entry.synapse,
col = col.entry.synapse, ...))
draw.text(label = entry.label[(neuron.count[1] +
1) - i], x = c((x.position - arrow.length),
x.position[1] - radius), y = c(y.position[k],
y.position[k]), xy.null = c(0, 0), color = col.entry.synapse,
fontsize = fontsize, ...)
grid.circle(x = x.position[k], y = y.position[k],
r = radius, gp = gpar(fill = "white", col = col.entry,
...))
}
else {
grid.circle(x = x.position[k], y = y.position[k],
r = radius, gp = gpar(fill = "white", col = col.hidden,
...))
}
}
}
out <- length(neuron.count)
for (i in 1:neuron.count[out]) {
y.position[out] <- y.position[out] + y.step[out]
grid.lines(x = c(x.position[out] + radius, x.position[out] +
arrow.length), y = y.position[out], arrow = arrow(length = unit(0.15,
"cm"), type = "closed"), gp = gpar(fill = col.out.synapse,
col = col.out.synapse, ...))
draw.text(label = out.label[(neuron.count[out] +
1) - i], x = c((x.position[out] + radius), x.position[out] +
arrow.length), y = c(y.position[out], y.position[out]),
xy.null = c(0, 0), color = col.out.synapse, fontsize = fontsize,
...)
grid.circle(x = x.position[out], y = y.position[out],
r = radius, gp = gpar(fill = "white", col = col.out,
...))
}
if (intercept) {
for (k in 1:length(weights)) {
y.tmp <- 0
x.intercept <- (x.position[k + 1] - x.position[k]) *
intercept.factor + x.position[k]
for (i in 1:neuron.count[k + 1]) {
y.tmp <- y.tmp + y.step[k + 1]
result <- calculate.delta(c(x.intercept, x.position[k +
1]), c(y.intercept, y.tmp), radius)
x <- c(x.intercept, x.position[k + 1] - result[1])
y <- c(y.intercept, y.tmp + result[2])
grid.lines(x = x, y = y, arrow = arrow(length = unit(0.15,
"cm"), type = "closed"), gp = gpar(fill = col.intercept,
col = col.intercept, ...))
xy.null <- cbind(x.position[k + 1] - x.intercept -
2 * result[1], -(y.tmp - y.intercept + 2 *
result[2]))
if (show.weights)
draw.text(label = weights[[k]][1, neuron.count[k +
1] - i + 1], x = c(x.intercept, x.position[k +
1]), y = c(y.intercept, y.tmp), xy.null = xy.null,
color = col.intercept, alignment = c("right",
"bottom"), fontsize = fontsize - 2, ...)
}
grid.circle(x = x.intercept, y = y.intercept,
r = radius, gp = gpar(fill = "white", col = col.intercept,
...))
grid.text(1, x = x.intercept, y = y.intercept,
gp = gpar(col = col.intercept, ...))
}
}
if (information)
grid.text(paste("Error: ", round(result.matrix[rep,
"error"], 6), " Steps: ", result.matrix[rep,
"steps"], sep = ""), x = 0.5, y = information.pos,
just = "bottom", gp = gpar(fontsize = fontsize +
2, ...))
popViewport()
if (!is.null(file)) {
weight.plot <- recordPlot()
save(weight.plot, file = file)
}
}
}
calculate.delta <-
function (x, y, r)
{
delta.x <- x[2] - x[1]
delta.y <- y[2] - y[1]
x.null <- r/sqrt(delta.x^2 + delta.y^2) * delta.x
if (y[1] < y[2])
y.null <- -sqrt(r^2 - x.null^2)
else if (y[1] > y[2])
y.null <- sqrt(r^2 - x.null^2)
else y.null <- 0
c(x.null, y.null)
}
draw.text <-
function (label, x, y, xy.null = c(0, 0), color, alignment = c("left",
"bottom"), ...)
{
x.label <- x[1] + xy.null[1]
y.label <- y[1] - xy.null[2]
x.delta <- x[2] - x[1]
y.delta <- y[2] - y[1]
angle = atan(y.delta/x.delta) * (180/pi)
if (angle < 0)
angle <- angle + 0
else if (angle > 0)
angle <- angle - 0
if (is.numeric(label))
label <- round(label, 5)
pushViewport(viewport(x = x.label, y = y.label, width = 0,
height = , angle = angle, name = "vp1", just = alignment))
grid.text(label, x = 0, y = unit(0.75, "mm"), just = alignment,
gp = gpar(col = color, ...))
popViewport()
}
答案 1 :(得分：3)
绘图 plot(nn)后，这对我有用：
dev.print(pdf, 'ANN_coefficients_plot.pdf')
答案 2 :(得分：1)
包neuralnet使用网格图形。这意味着您需要以非交互模式print绘图。
试试这个：
png("iris.png")
print(plot(nn))
dev.off()