BP神经网络

import math
import numpy as np
import pandas as pd
from pandas import DataFrame,Series

def sigmoid(x): #映射函数
    return 1/(1+math.exp(-x))

x1=[0.29,0.50,0.00,0.21,0.10,0.06,0.13,0.24,0.28]
x2=[0.23,0.62,0.53,0.53,0.33,0.15,0.03,0.23,0.03]
y=[0.14,0.64,0.28,0.33,0.12,0.03,0.02,0.11,0.08]
yita=0.1
for i in range(9):
    Net_in =DataFrame(0.6,index=['input1','input2','theata'],columns=['a'])
    Out_in = DataFrame(0,index=['input1','input2','input3','input4','theata'],columns=['a'])
    Net_in.loc['input1'] =x1[i]
    Net_in.loc['input2']=x2[i]
    real=y[i]
    Net_in.loc['theata'] = -1
    Out_in.loc['theata'] = -1
    W_mid=DataFrame(0.7,index=['input1','input2','theata'],columns=['mid1','mid2','mid3','mid4'])
    W_out=DataFrame(0.7,index=['input1','input2','input3','input4','theata'],columns=['a'])
    W_mid_delta=DataFrame(0,index=['input1','input2','theata'],columns=['mid1','mid2','mid3','mid4'])
    W_out_delta=DataFrame(0,index=['input1','input2','input3','input4','theata'],columns=['a'])
    for i in range(0,4):
        Out_in.iloc[i,0] = sigmoid(sum(W_mid.iloc[:,i]*Net_in.iloc[:,0]))
#输出层的输出/网络输出
    res = sigmoid(sum(Out_in.iloc[:,0]*W_out.iloc[:,0]))
    error = abs(res-real)
    W_out_delta.iloc[:,0] = yita*res*(1-res)*(real-res)*Out_in.iloc[:,0]
    W_out_delta.iloc[4,0] = -(yita*res*(1-res)*(real-res))
    W_out = W_out + W_out_delta #输出层权值更新
    for i in range(0,4):
        W_mid_delta.iloc[:,i] = yita*Out_in.iloc[i,0]*(1-Out_in.iloc[i,0])*W_out.iloc[i,0]*res*(1-res)*(real-res)*Net_in.iloc[:,0]
        W_mid_delta.iloc[2,i] = -(yita*Out_in.iloc[i,0]*(1-Out_in.iloc[i,0])*W_out.iloc[i,0]*res*(1-res)*(real-res))
    W_mid = W_mid + W_mid_delta #中间层权值更新
testx1=[0.38,0.29]
testx2=[0.49,0.47]
for i in range(2):
    Net_in =DataFrame(0.6,index=['input1','input2','theata'],columns=['a'])
    Out_in = DataFrame(0,index=['input1','input2','input3','input4','theata'],columns=['a'])
    Net_in.loc['input1'] =testx1[i]
    Net_in.loc['input2']=testx2[i]
    Net_in.loc['theata'] = -1
    Out_in.loc['theata'] = -1
    for i in range(0,4):
        Out_in.iloc[i,0] = sigmoid(sum(W_mid.iloc[:,i]*Net_in.iloc[:,0]))
#输出层的输出/网络输出
    res = sigmoid(sum(Out_in.iloc[:,0]*W_out.iloc[:,0]))
    print(res)

 

 

import numpy
import scipy.special
import scipy.misc
import matplotlib.pyplot
import scipy.ndimage
import math
import pandas as pd
from pandas import DataFrame,Series

class NeuralNetwork():
    def __init__(self,inputnodes,hiddennodes,outputnodes,learningrate):
        self.inodes = inputnodes
        self.hnodes = hiddennodes
        self.onodes = outputnodes
        self.lr = learningrate
        self.wih = numpy.random.normal(0.0,pow(self.hnodes,-0.5),(self.hnodes,self.inodes))
        self.who = numpy.random.normal(0.0, pow(self.onodes, -0.5), (self.onodes, self.hnodes))
        self.activation_function = lambda x: scipy.special.expit(x)
        pass
    def train(self,input_list,target_list):
        inputs=numpy.array(input_list,ndmin=2).T
        targets=numpy.array(target_list,ndmin=2).T
        hidden_inputs=numpy.dot(self.wih,inputs)
        hidden_outputs=self.activation_function(hidden_inputs)
        hidden_outputs1=numpy.append(hidden_outputs,-1)
        final_inputs=numpy.dot(self.who,hidden_outputs)
        final_outputs=self.activation_function(final_inputs)
        output_errors=targets-final_outputs
        hidden_errors=numpy.dot(self.who.T,output_errors)
        self.who+=self.lr*numpy.dot((output_errors*final_outputs*(1.0-final_outputs)),numpy.transpose(hidden_outputs))
        self.wih+=self.lr*numpy.dot((hidden_errors*hidden_outputs*(1.0-hidden_outputs)),numpy.transpose(inputs))
        pass
    def query(self,input_list):
        inputs=numpy.array(input_list,ndmin=2).T
        hidden_inputs=numpy.dot(self.wih,inputs)
        hidden_outputs=self.activation_function(hidden_inputs)
        final_inputs=numpy.dot(self.who,hidden_outputs)
        final_outputs=self.activation_function(final_inputs)
        return final_outputs
print('n')

input_nodes=2
hidden_nodes=3
output_nodes=1
learning_rate=0.5
n=NeuralNetwork(input_nodes,hidden_nodes,output_nodes,learning_rate)
training_data_file=open(r'./3.3 data_tr.txt')
training_data_list=training_data_file.readlines();
#training_data_list=training_data_file.readlines()
training_data_file.close()
for record in training_data_list[1:]:
    all_values=record.split(',')
    inputs = (numpy.asfarray(all_values[0:2]))
    targets = numpy.zeros(output_nodes)
    targets[0] = all_values[2]
    n.train(inputs,targets)
    pass
test_data_file=open(r'./3.3 data_te.txt')
test_data_list=test_data_file.readlines()
test_data_file.close()
scorecard=[]
total=0
correct=0
for record in test_data_list[1:]:
    total+=1
    all_values=record.split(',')
    correct_label=all_values[2]
    inputs=(numpy.asfarray(all_values[0:2]))
    outputs=n.query(inputs)
    print(outputs)