手撕Bp神经网络（python实现+附带案例测试）

代码先放在这啦，理论知识以后再来补充

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

start = time.time()

class Bp:
    def __init__(self,num_input, num_hidden, num_out, num_sample):
        # 三个参数分别为输入，隐藏，输出层的节点个数
        self.num_input = num_input
        self.num_hidden = num_hidden
        self.num_out = num_out

        # 初始化权重矩阵
        """
        为什么使用rand函数结果全为1
        """
        # 以正态分布初始化各层权重
        self.weight_in = np.random.normal(0.0, num_input ** - 0.5, (num_input, num_hidden))  #第一层的权重
        self.weight_out = np.random.normal(0.0, num_hidden ** - 0.5,(num_hidden, num_out)) #第二层的权重

        # self.weight_in = np.random.rand(num_input, num_hidden)  # 第一层的权重
        # self.weight_out = np.random.rand(num_hidden, num_out) # 第二层的权重
        # self.weight_in = np.ones((num_input, num_hidden))  #第一层的权重
        # self.weight_out = np.ones((num_hidden, num_out)) #第二层的权重
        # print(self.weight_in.shape)

        #初始化偏置项
        # self.b_in = np.random.rand(num_hidden, 1)
        # self.b_out = np.random.rand(num_out, 1)

        self.b_in = np.zeros((num_hidden, 1))
        self.b_out = np.zeros((num_out, 1))

        #