BP神經網絡 百度百科：傳送門
BP(back propagation)神經網絡：一種按照誤差逆向傳播算法訓練的多層前饋神經網絡，是目前應用最廣泛的神經網絡
#設置文件工作區間 setwd('D:\\dat') #讀入數據 Gary=read.csv("sales_data.csv")[,2:5] #數據命名 library(nnet) colnames(Gary)<-c("x1","x2","x3","y") ###最終模型 model1=nnet(y~.,data=Gary,size=6,decay=5e-4,maxit=1000) pred=predict(model1,Gary[,1:3],type="class") (P=sum(as.numeric(pred==Gary$y))/nrow(Gary)) table(Gary$y,pred) prop.table(table(Gary$y,pred),1)
實現過程
目的：通過BP神經網絡預測銷量的高低
數據預處理，對數據進行重命名並去除無關項
> #設置文件工作區間 > setwd('D:\\dat') > #讀入數據 > Gary=read.csv("sales_data.csv")[,2:5] > #數據命名 > library(nnet) > colnames(Gary)<-c("x1","x2","x3","y") > Gary x1 x2 x3 y 1 bad yes yes high 2 bad yes yes high 3 bad yes yes high 4 bad no yes high 5 bad yes yes high 6 bad no yes high 7 bad yes no high 8 good yes yes high 9 good yes no high 10 good yes yes high 11 good yes yes high 12 good yes yes high 13 good yes yes high 14 bad yes yes low 15 good no yes high 16 good no yes high 17 good no yes high 18 good no yes high 19 good no no high 20 bad no no low 21 bad no yes low 22 bad no yes low 23 bad no yes low 24 bad no no low 25 bad yes no low 26 good no yes low 27 good no yes low 28 bad no no low 29 bad no no low 30 good no no low 31 bad yes no low 32 good no yes low 33 good no no low 34 good no no low
nnet：包實現了前饋神經網絡和多項對數線性模型。前饋神經網絡是一種常用的神經網絡結構，如下圖所示
前饋網絡中各個神經元按接受信息的先后分為不同的組。每一組可以看作一個神經層。每一層中的神經元接受前一層神經元的輸出，並輸出到下一層神經元。整個網絡中的信息是朝一個方向傳播，沒有反向的信息傳播。前饋網絡可以用一個有向無環路圖表示。前饋網絡可以看作一個函數，通過簡單非線性函數的多次復合，實現輸入空間到輸出空間的復雜映射。這種網絡結構簡單，易於實現。前饋網絡包括全連接前饋網絡和卷積神經網絡等
使用neet()方法創建模型
neet()方法：
decay: 權重衰減parameter for weight decay. Default 0.
maxit: 最大迭代次數
x: 訓練樣本數據集的輸入集合 y: x對應的訓練樣本數據集的標簽（類）集合 weights: size: 隱層節點數， Can be zero if there are skip-layer units. data:訓練數據集. subset: An index vector specifying the cases to be used in the training sample. (NOTE: If given, this argument must be named.) na.action: A function to specify the action to be taken if NAs are found. The default action is for the procedure to fail. An alternative is na.omit, which leads to rejection of cases with missing values on any required variable. (NOTE: If given, this argument must be named.) contrasts:a list of contrasts to be used for some or all of the factors appearing as variables in the model formula. Wts: 邊的權重. If missing chosen at random. mask: logical vector indicating which parameters should be optimized (default all). linout: 是否為邏輯輸出單元，若為FALSE，則為線性輸出單元 entropy: switch for entropy (= maximum conditional likelihood) fitting. Default by least-squares. softmax: switch for softmax (log-linear model) and maximum conditional likelihood fitting. linout, entropy, softmax and censored are mutually exclusive. censored: A variant on softmax, in which non-zero targets mean possible classes. Thus for softmax a row of (0, 1, 1) means one example each of classes 2 and 3, but for censored it means one example whose class is only known to be 2 or 3. skip: switch to add skip-layer connections from input to output. rang: Initial random weights on [-rang, rang]. Value about 0.5 unless the inputs are large, in which case it should be chosen so that rang * max(|x|) is about 1. decay: 權重衰減parameter for weight decay. Default 0. maxit: 最大迭代次數 Hess: If true, the Hessian of the measure of fit at the best set of weights found is returned as component Hessian. trace: switch for tracing optimization. Default TRUE. MaxNWts: The maximum allowable number of weights. There is no intrinsic limit in the code, but increasing MaxNWts will probably allow fits that are very slow and time-consuming. abstol: Stop if the fit criterion falls below abstol, indicating an essentially perfect fit. reltol: Stop if the optimizer is unable to reduce the fit criterion by a factor of at least 1 - reltol.
model1=nnet(y~.,data=Gary,size=6,decay=5e-4,maxit=1000) # weights: 31 initial value 27.073547 iter 10 value 16.080731 iter 20 value 15.038060 iter 30 value 14.937127 iter 40 value 14.917485 iter 50 value 14.911531 iter 60 value 14.908678 iter 70 value 14.907836 iter 80 value 14.905234 iter 90 value 14.904499 iter 100 value 14.904028 iter 110 value 14.903688 iter 120 value 14.903480 iter 130 value 14.903450 iter 130 value 14.903450 iter 130 value 14.903450 final value 14.903450 converged
評估模型
> pred=predict(model1,Gary[,1:3],type="class") > (P=sum(as.numeric(pred==Gary$y))/nrow(Gary)) [1] 0.7647059 > table(Gary$y,pred) pred high low high 14 4 low 4 12 > prop.table(table(Gary$y,pred),1) pred high low high 0.7777778 0.2222222 low 0.2500000 0.7500000
得到混淆矩陣圖后可以看出
檢測樣本為34個，預測正確的個數為26個，預測准確率為76.5%，預測准確率較低
原因：由於神經網絡訓練時需要較多的樣本，而這里訓練集比較少