使用Neuronet软件包保存神经网络图时遇到问题-R [英] Having problems saving a neural net plot using neuralnet package - R
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问题描述
我在R中使用neuralnet软件包，但是在将图保存到磁盘时遇到了问题.
I'm using the neuralnet package in R, however am having problems saving the plot to disk.
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()
曾尝试参考《手册》中的手册. plot.nn()部分说:
Have attempted to refere to the manual, in the section plot.nn() it says:
file a character string naming the plot to write to. If not stated,
the plot will not be saved.
但是，执行以下操作不会保存有效的绘图:
However doing the following yields no valid plot saved:
png("test.png")
plot(nn, file = "test.png")
dev.off()
平台:
Mac OSX 10.7.3
R版本:
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
注意:R是通过macports安装的
Note: R was installed via macports
推荐答案
在与neuralnet的维护者Frauke Guenther交谈后，她解释了问题在于neuralnet能够绘制多个神经网络的能力.同一时间.
After speaking with Frauke Guenther (Maintainer of neuralnet), she explains the problem lies with neuralnet's ability to plot more than one neural net at the same time.
plot(..., file = "")应该已经将文件保存到磁盘，但是似乎不能正常运行，很快就会将修补程序修补到程序包本身，但是与此同时，她还提供了一个快速的plot.nn()修补程序，该修补程序可以允许将图保存到磁盘.
The plot(..., file = "") should have saved the file to disk, however does not seem to function properly, a fix will be patched soon to the package itself, however for the mean time she kindly provided a quick plot.nn() fix that would allow one to save the plot to disk.
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()
}
将上面的代码粘贴到R交互式提示中，然后当您要保存绘图时执行以下操作:
paste the code above into your R interactive prompt, and when you wish to to save your plot do the following:
png("test.png")
plot(nn)
dev.off()
这篇关于使用Neuronet软件包保存神经网络图时遇到问题-R的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！
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