【机器学习——BP神经网络】——Python实现、模型保存与调用

#!/usr/bin/env python
# -*- coding: utf-8 -*-
# @Time    : 2021/12/31 21:46
# @Author  : @linlianqin
# @Site    : 
# @File    : bp.py
# @Software: PyCharm
# @description:

import pandas as pd
import numpy as np
import datetime
from sklearn.utils import shuffle
import json
from paths import abs_path

# 1.初始化参数
def initialize_parameters(n_x, n_h, n_y):
	np.random.seed(2)

	# 权重和偏置矩阵
	w1 = np.random.randn(n_h, n_x) * 0.01
	b1 = np.zeros(shape=(n_h, 1))
	w2 = np.random.randn(n_y, n_h) * 0.01
	b2 = np.zeros(shape=(n_y, 1))

	# 通过字典存储参数
	parameters = {'w1': w1, 'b1': b1, 'w2': w2, 'b2': b2}

	return parameters


# 2.前向传播
def forward_propagation(X, parameters):
	w1 = parameters['w1']
	b1 = parameters['b1']
	w2 = parameters['w2']
	b2 = parameters['b2']

	# 通过前向传播来计算a2
	z1 = np.dot(w1, X) + b1  # 这个地方需注意矩阵加法：虽然(