BP神经网络原理及Python实现代码

 # -*- coding: utf-8 -*- import numpy as np from random import random as rdn ''' 说明：我们构造1000条数据，每条数据有三个属性(用a1 , a2 , a3表示) a1 离散型 取值 1 到 10 ， 均匀分布 a2 离散型 取值 1 到 10 ， 均匀分布 a3 连续型 取值 1 到 100 ， 且符合正态分布 各属性之间独立。 共2个分类(0 , 1)，属性值与类别之间的关系如下， 0 : a1 in [1 , 3] and a2 in [4 , 10] and a3 <= 50 1 : a1 in [1 , 3] and a2 in [4 , 10] and a3 > 50 0 : a1 in [1 , 3] and a2 in [1 , 3] and a3 > 30 1 : a1 in [1 , 3] and a2 in [1 , 3] and a3 <= 30 0 : a1 in [4 , 10] and a2 in [4 , 10] and a3 <= 50 1 : a1 in [4 , 10] and a2 in [4 , 10] and a3 > 50 0 : a1 in [4 , 10] and a2 in [1 , 3] and a3 > 30 1 : a1 in [4 , 10] and a2 in [1 , 3] and a3 <= 30 ''' def genData() : #为a3生成符合正态分布的数据 a3_data = np.random.randn(1000) * 30 + 50 data = [] for i in range(1000) : #生成a1 a1 = int(rdn()*10) + 1 if a1 > 10 : a1 = 10 #生成a2 a2 = int(rdn()*10) + 1 if a2 > 10 : a2 = 10 #取a3 a3 = a3_data[i] #计算这条数据对应的类别 c_id = 0 if a1 <= 3 and a2 >= 4 and a3 <= 50 : c_id = 0 elif a1 <= 3 and a2 >= 4 and a3 > 50 : c_id = 1 elif a1 <= 3 and a2 <4 and a3> 30 : c_id = 0 elif a1 <= 3 and a2 <4 and a3 <=30 : c_id=1 elif a1> 3 and a2 >= 4 and a3 <= 50 : c_id = 0 elif a1 > 3 and a2 >= 4 and a3 > 50 : c_id = 1 elif a1 > 3 and a2 <4 and a3> 30 : c_id = 0 elif a1 > 3 and a2 <4 and a3 <=30 : c_id=1 else print('error') #拼合成字串 str_line=str(i) + ',' str(a1) str(a2) str(a3) str(c_id) data.append(str_line) return '\n'.join(data)< pre>

 # -*- coding: utf-8 -*- """ Created on Sun Dec 2 14:49:31 2018 @author: congpeiqing """ import numpy as np #sigmoid函数的导数为 f(x)*(1-f(x)) def sigmoid(x) : return 1/(1 + np.exp(-x))

 # -*- coding: utf-8 -*- """ Created on Sun Dec 2 14:49:31 2018 @author: congpeiqing """ from activation_funcs import sigmoid from random import random class InputNode(object) : def __init__(self , idx) : self.idx = idx self.output = None def setInput(self , value) : self.output = value def getOutput(self) : return self.output def refreshParas(self , p1 , p2) : pass class Neurode(object) : def __init__(self , layer_name , idx , input_nodes , activation_func = None , powers = None , bias = None) : self.idx = idx self.layer_name = layer_name self.input_nodes = input_nodes if activation_func is not None : self.activation_func = activation_func else : #默认取 sigmoid self.activation_func = sigmoid if powers is not None : self.powers = powers else : self.powers = [random() for i in range(len(self.input_nodes))] if bias is not None : self.bias = bias else : self.bias = random() self.output = None def getOutput(self) : self.output = self.activation_func(sum(map(lambda x : x[0].getOutput()*x[1] , zip(self.input_nodes, self.powers))) + self.bias) return self.output def refreshParas(self , err , learn_rate) : err_add = self.output * (1 - self.output) * err for i in range(len(self.input_nodes)) : #调用子节点 self.input_nodes[i].refreshParas(self.powers[i] * err_add , learn_rate) #调节参数 power_delta = learn_rate * err_add * self.input_nodes[i].output self.powers[i] += power_delta bias_delta = learn_rate * err_add self.bias += bias_delta class SimpleBP(object) : def __init__(self , input_node_num , hidden_layer_node_num , trainning_data , test_data) : self.input_node_num = input_node_num self.input_nodes = [InputNode(i) for i in range(input_node_num)] self.hidden_layer_nodes = [Neurode('H' , i , self.input_nodes) for i in range(hidden_layer_node_num)] self.output_node = Neurode('O' , 0 , self.hidden_layer_nodes) self.trainning_data = trainning_data self.test_data = test_data #逐条训练 def trainByItem(self) : cnt = 0 while True : cnt += 1 learn_rate = 1.0/cnt sum_diff = 0.0 #对于每一条训练数据进行一次训练过程 for item in self.trainning_data : for i in range(self.input_node_num) : self.input_nodes[i].setInput(item[i]) item_output = item[-1] nn_output = self.output_node.getOutput() #print('nn_output:' , nn_output) diff = (item_output-nn_output) sum_diff += abs(diff) self.output_node.refreshParas(diff , learn_rate) #print('refreshedParas') #结束条件 print(round(sum_diff / len(self.trainning_data) , 4)) if sum_diff / len(self.trainning_data) <0.1 : break def getAccuracy(self) : cnt = 0 for item in self.test_data : for i in range(self.input_node_num) : self.input_nodes[i].setInput(item[i]) item_output = item[-1] nn_output = self.output_node.getOutput() if (nn_output > 0.5 and item_output > 0.5) or (nn_output <0.5 and item_output <0.5) : cnt += 1 return cnt/(len(self.test_data) + 0.0)

 # -*- coding: utf-8 -*- """ Created on Sun Dec 2 14:49:31 2018 @author: congpeiqing """ import os from SimpleBP import SimpleBP from GenData import genData if not os.path.exists('data'): os.makedirs('data') #构造训练和测试数据 data_file = open('data/trainning_data.dat' , 'w') data_file.write(genData()) data_file.close() data_file = open('data/test_data.dat' , 'w') data_file.write(genData()) data_file.close() #文件格式：rec_id,attr1_value,attr2_value,attr3_value,class_id #读取和解析训练数据 trainning_data_file = open('data/trainning_data.dat') trainning_data = [] for line in trainning_data_file : line = line.strip() fld_list = line.split(',') trainning_data.append(tuple([float(field) for field in fld_list[1:]])) trainning_data_file.close() #读取和解析测试数据 test_data_file = open('data/test_data.dat') test_data = [] for line in test_data_file : line = line.strip() fld_list = line.split(',') test_data.append(tuple([float(field) for field in fld_list[1:]])) test_data_file.close() #构造一个二分类网络 输入节点3个，隐层节点10个，输出节点一个 simple_bp = SimpleBP(3 , 10 , trainning_data , test_data) #训练网络 simple_bp.trainByItem() #测试分类准确率 print('Accuracy : ' , simple_bp.getAccuracy()) #训练时长比较长，准确率可以达到97%