BP神经网络算法 Python实现_bp神经网络python代码

作者：我家自动化 | 2024-02-16 15:42:07
踩
bp神经网络python代码
BP神经网络算法
实验要求：
掌握BP神经网络算法的实现
掌握利用BP神经网络对样本数据分类，并观察训练时间及迭代次数对结果的影响
Python 代码
# -*- coding: utf-8 -*-
# @Time    : 2022/10/25 22:48
# @Author  : Norman Mises
# @FileName: train.py
# @Software: PyCharm

import math
import time

import numpy as np
import pandas as pd
import yaml


def train(dat, id):
    x = dat
    global d
    d = [-1 for _ in range(num_layers[-1])]  # 希望输出值
    d[id] = 1
    # print(d)

    global y
    y[0] = x
    for i in range(1, n):  # 每一层
        for j in range(num_layers[i]):  # 每一个
            net = sum([weights[i][j][k] * y[i - 1][k] for k in range(num_layers[i - 1])])
            f = lambda net_: 1 / (1 + math.e ** ((theta[i][j] - net_) / h0))
            y[i][j] = f(net)

    # for i in y[-1]:
    #     print(f'{i:.6f}', end=',')

    Ep = 0.5 * sum([(d[i] - y[-1][i]) ** 2 for i in range(num_layers[-1])])
    # print(f'{Ep}')
    if Ep > 0.50001:
        return False
    else:
        return True


def correct():
    global delta
    for i in range(n - 1, 0, -1):  # 每一层
        for j in range(num_layers[i]):
            if i == n - 1:
                delta[i][j] = y[i][j] * (1 - y[i][j]) * (d[j] - y[-1][j])
            else:
                delta[i][j] = y[i][j] * (1 - y[i][j]) * sum(
                    [delta[i + 1][q] * weights[i + 1][q][j] for q in range(num_layers[i + 1])])
            for k in range(num_layers[i - 1]):
                weights[i][j][k] += eta * delta[i][j] * y[i - 1][k]


def test(x):
    out = y
    out[0] = x
    for i in range(1, n):  # 每一层
        for j in range(num_layers[i]):  # 每一个
            net = sum([weights[i][j][k] * out[i - 1][k] for k in range(num_layers[i - 1])])
            f = lambda net_: 1 / (1 + math.e ** ((theta[i][j] - net_) / h0))
            out[i][j] = f(net)

    for i in range(num_layers[-1]):
        print(f'第 {i} 类：{out[-1][i]:.6f}')


if __name__ == "__main__":
    with open('testcases/training_data.yaml', 'r', encoding='utf-8') as f:
        data_yaml = yaml.load(f, yaml.FullLoader)
        print(data_yaml['data'][0])

    label = data_yaml['label'][0]
    df = pd.DataFrame(data_yaml['data'][0])
    data = (df - df.min()) / (df.max() - df.min())
    data = data.values
    print(data)
    print(label)

    h0 = float(input(f'h0: '))
    eta = float(input(f'eta: '))
    n = int(input('层数：'))
    num_layers = []
    weights = []
    theta = []  # 神经元阈值
    delta = []
    d = []
    y = []
    print('每层神经元数：')
    for i in range(n):
        num_layers.append(int(input(f'第 {i} 层：')))
        theta.append(np.random.rand(num_layers[i]))
        delta.append(np.zeros(num_layers[i]))
        y.append(np.zeros(num_layers[i]))
        # y.append([] for _ in range(num_layers[i]))
        if i > 0:
            weights.append(np.random.rand(num_layers[i], num_layers[i - 1]))
        else:
            weights.append([])

    print(f'每层神经元与其低一层的连接权：')
    for i in weights:
        print(i)

    print(f'神经元阈值：')
    for i in theta:
        print(i)
    times = int(input(f'训练次数上限（-1 为无上限）：'))
    start = time.time()
    cnt = 1
    while not train(data[cnt % len(data)], label[cnt % len(data)]):
        if times != -1 and cnt >= times:
            break
        cnt += 1
        correct()
    end = time.time()

    print(f'训练结束后每层神经元与其低一层的连接权：')
    for i in weights:
        print(i)
    print(f'训练次数：{cnt}')
    print(f'训练时间：{(end - start):.6f} s')

    try:
        while True:
            print(f'输入测试数据：')
            t = input().split(',')
            t = [float(i.strip()) for i in t]
            print(t)
            test(t)
    except:
        print('测试结束')

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132