机器学习第五章 5.5 标准BP PYTHON

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt



class BpNeuralNetwork:

    def __init__(self,x,label,input_neuron_num,output_neuron_num,hidden_neuron_num,learning_rate,iteration):

        """
        x:input data
        y:input label
        d:number of input_neuron_num
        l:number of output_neuron_num
        q:number of hidden_neuron_num
        theta_j:output layer threshold
        r_h:hidden layer threshold
        v_ih:input layer and hidden layer weight
        w_hj:hidden layer and output layer weight
        """

        self.x = x
        self.label = label
        self.d = input_neuron_num
        self.l = output_neuron_num
        self.q = hidden_neuron_num
        self.theta_j = np.random.random(self.l)
        self.r_h = np.random.random(self.q)
        self.v_ih = np.random.rand(self.q,self.d)
        self.w_hj = np.random.rand(self.l,self.q)
        self.learning_rate = learning_rate
        self.maxIter = iteration

    def activate_function(self,x):

        sigmoid = 1 / (1 + np.exp(-x))

        return sigmoid

    def output_function(self,x):

        alpha_h = np.sum(np.array(self.v_ih) * np.array(x,dtype=float))
        self.b_h = self.activate_function(np.array(alpha_h - self.r_h,dtype=float)) # hidden layer neuron output

        beta_j = np.sum(self.w_hj * self.b_h) #ouput latyer neuron input
        predict_y = self.activate_function(np.array(beta_j - self.theta_j,dtype=float))

        return predict_y

    def update_param(self,x,predict_y):

        x = x.astype(float)
        predict_y = self.output_function(x)

        g_j = predict_y * (1 - predict_y) * (self.y - predict_y)
        g_j = np.array(g_j, dtype=float)
        self.w_hj += self.learning_rate * g_j * self.b_h
        self.theta_j += - self.learning_rate * g_j

        e_h = self.b_h * (1 - self.b_h) * np.sum(self.w_hj * g_j)
        e_h = np.array(e_h,dtype=float)

        self.v_ih += self.learning_rate * np.dot(e_h.reshape(self.q,1),x.reshape(1,self.d))
        self.r_h += - self.learning_rate * e_h

    def objective_function(self,predict_y):

        Ek = np.sum((predict_y - self.y) ** 2) / 2

        return Ek

    def train_process(self):

        E = []
        for n in range(self.maxIter):
            Ek = []
            for i in range(self.x.shape[0]):
                self.y = self.label[i]
                predict_y = self.output_function(self.x[i])
                Ek.append(self.objective_function(predict_y))
                self.update_param(self.x[i],predict_y)

            E.append(sum(Ek) / self.x.shape[0])

            # if n % 10 == 0:
            #     print('{}th iteration, cost is {}'.format(n,E[n]))

    def predict(self,x):

        alpha_h = np.sum(np.array(self.v_ih) * np.array(x,dtype=float))
        self.b_h = self.activate_function(np.array(alpha_h - self.r_h,dtype=float)) # hidden layer neuron output

        beta_j = np.sum(self.w_hj * self.b_h) #ouput latyer neuron input
        predict_y = self.activate_function(np.array(beta_j - self.theta_j,dtype=float))

        return predict_y

if __name__ == "__main__":

    data = pd.read_excel("F:/pythonpj/西瓜3.0.xlsx").values

    x  =data[:,7:-1]
    y = data[:,-1]

    bp = BpNeuralNetwork(x,y,x.shape[1],1,10,0.1,5000)
    bp.train_process()

    for i in range(17):
        print(bp.predict(x[i]))

代码里面的公式标识按照书里面的来的，这里只用了密度和含糖率
结果：

[0.79391396]
[0.79249972]
[0.7680025]
[0.79187919]
[0.71656208]
[0.78923283]
[0.34058789]
[0.77318892]
[0.0010799]
[0.79373239]
[0.08965418]
[0.21713429]
[0.03211429]
[0.29668821]
[0.79392006]
[0.00090568]
[0.00104169]

结果不太好，后续会改进，基本逻辑实现是这样的
前8个正例有1个预测成反例
后9个反例有2个预测成正例

精度：14/17
错误率：3/17
查准率：7/9
查全率：7/8

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