在使用神經網絡包保存神經網絡圖時遇到問題 - R

Question

我在R中使用neuralnet包，但是在將繪圖保存到磁盤時遇到問題。

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() 

曾試圖參考，到manual，在部分plot.nn()它說：

file  a character string naming the plot to write to. If not stated, 
the plot will not be saved. 

但是做以下收益率沒有有效的情節保存：

png("test.png") 
plot(nn, file = "test.png") 
dev.off() 

平臺：

Mac OSX 10.7.3 

ř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 

註釋：R經由MacPorts的

Answer 1

與Frauke岡瑟（的neuralnet維護者）溝通後，她解釋說，問題在於在同一時間繪製多個神經網絡neuralnet的能力。

的plot(..., file = "")應該將文件保存到磁盤，但是似乎並沒有因爲她慷慨地提供一個快速plot.nn()修復的平均時間，將允許一個正常運行，修復將很快修補包本身，但將繪圖保存到磁盤。

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() 
} 

粘貼上面的代碼到你的[R交互提示，當你想保存你的情節執行以下操作：

png("test.png") 
plot(nn) 
dev.off() 

Answer 2

封裝neuralnet使用網格圖形安裝。這意味着您需要在非交互模式下使用print您的地塊。

試試這個：

png("iris.png") 
print(plot(nn)) 
dev.off()