BP神经网络用于数据预测（Python源码+数据集）

#库的导入 import numpy as np import pandas as pd #激活函数tanh def tanh(x): return (np.exp(x)-np.exp(-x))/(np.exp(x)+np.exp(-x)) #反激活函数 def de_tanh(x): return (1-x**2) #输入数据的导入 df = pd.read_csv("train.csv") df.columns = ["Co", "Cr", "Mg", "Pb", "Ti"] Co = df["Co"] Co = np.array(Co) Cr = df["Cr"] Cr = np.array(Cr) Mg=df["Mg"] Mg=np.array(Mg) Pb = df["Pb"] Pb =np.array(Pb) Ti = df["Ti"] Ti = np.array(Ti) samplein = np.mat([Co,Cr,Mg,Pb]) #数据归一化，将输入数据压缩至0到1之间，便于计算，后续通过反归一化恢复原始值 sampleinminmax = np.array([samplein.min(axis=1).T.tolist()[0],samplein.max(axis=1).T.tolist()[0]]).transpose() sampleout = np.mat([Ti]) sampleoutminmax = np.array([sampleout.min(axis=1).T.tolist()[0],sampleout.max(axis=1).T.tolist()[0]]).transpose() sampleinnorm = (2*(np.array(samplein.T)-sampleinminmax.transpose()[0])/(sampleinminmax.transpose()[1]-sampleinminmax.transpose()[0])-1).transpose() sampleoutnorm = (2*(np.array(sampleout.T)-sampleoutminmax.transpose()[0])/(sampleoutminmax.transpose()[1]-sampleoutminmax.transpose()[0])-1).transpose() noise = 0.03*np.random.rand(sampleoutnorm.shape[0],sampleoutnorm.shape[1]) sampleoutnorm += noise maxepochs = 5000 #训练次数 learnrate = 0.001 #学习率 errorfinal = 0.65*10**(-3) #停止训练误差阈值 samnum = 72 #输入数据数量 indim = 4 #输入层节点数 outdim = 1 #输出层节点数 hiddenunitnum = 8 #隐含层节点数 #随机生成隐含层与输出层的权值w和阈值b scale = np.sqrt(3/((indim+outdim)*0.5)) #最大值最小值范围为-1.44~1.44 w1 = np.random.uniform(low=-scale, high=scale, size=[hiddenunitnum,indim]) b1 = np.random.uniform(low=-scale, high=scale, size=[hiddenunitnum,1]) w2 = np.random.uniform(low=-scale, high=scale, size=[outdim,hiddenunitnum]) b2 = np.random.uniform(low=-scale, high=scale, size=[outdim,1]) #errhistory存储误差 errhistory = np.mat(np.zeros((1,maxepochs))) #开始训练 for i in range(maxepochs): print("The iteration is : ", i) #前向传播，计算隐含层、输出层输出 hiddenout = tanh((np.dot(w1,sampleinnorm).transpose()+b1.transpose())).transpose() networkout = tanh((np.dot(w2,hiddenout).transpose()+b2.transpose())).transpose() #计算误差值 err = sampleoutnorm - networkout loss = np.sum(err**2)/2 print("the loss is :",loss) errhistory[:,i] = loss #判断是否停止训练 if loss < errorfinal: break #反向传播，利用结果误差进行误差项的计算 delta2 = err*de_tanh(networkout) delta1 = np.dot(w2.transpose(),delta2)*de_tanh(hiddenout) #计算输出层的误差项 dw2 = np.dot(delta2,hiddenout.transpose()) dw2 = dw2 / samnum db2 = np.dot(delta2,np.ones((samnum,1))) db2 = db2 / samnum #计算隐含层的误差项 dw1 = np.dot(delta1,sampleinnorm.transpose()) dw1 = dw1 / samnum db1 = np.dot(delta1,np.ones((samnum,1))) db1 = db1/samnum #对权值、阈值进行更新 w2 += learnrate*dw2 b2 += learnrate*db2 w1 += learnrate*dw1 b1 += learnrate*db1 print('更新的权重w1:',w1) print('更新的偏置b1:',b1) print('更新的权重w2:',w2) print('更新的偏置b2:',b2) print("The loss after iteration is ：",loss) #保持训练结束后的权值、阈值，用于测试 np.save("w1.npy",w1) np.save("b1.npy",b1) np.save("w2.npy",w2) np.save("b2.npy",b2)