python神经网络数字识别算法_Python实现bp神经网络识别MNIST数据集

title: “Python实现bp神经网络识别MNIST数据集”

date: 2018-06-18T14:01:49+08:00

tags: [“”]

categories: [“python”]

前言

训练时读入的是.mat格式的训练集，测试正确率时用的是png格式的图片

代码

#!/usr/bin/env python3

# coding=utf-8

import math

import sys

import os

import numpy as np

from PIL import Image

import scipy.io as sio

def sigmoid(x):

return np.array(list(map(lambda i: 1 / (1 + math.exp(-i)), x)))

def get_train_pattern():

# 返回训练集的特征和标签

# current_dir = os.getcwd()

current_dir = "/home/lxp/F/developing_folder/intelligence_system/bpneuralnet/"

train = sio.loadmat(current_dir + "mnist_train.mat")["mnist_train"]

train_label = sio.loadmat(

current_dir + "mnist_train_labels.mat")["mnist_train_labels"]

train = np.where(train > 180, 1, 0) # 二值化

return train, train_label

def get_test_pattern():

# 返回测试集

# base_url = os.getcwd() + "/test/"

base_url = "/home/lxp/F/developing_folder/intelligence_system/bpneuralnet/mnist_test/"

test_img_pattern = []

for i in range(10):

img_url = os.listdir(base_url + str(i))

t = []

for url in img_url:

img = Image.open(base_url + str(i) + "/" + url)

img = img.convert('1') # 二值化

img_array = np.asarray(img, 'i') # 转化为int数组

img_vector = img_array.reshape(

img_array.shape[0] * img_array.shape[1]) # 展开成一维数组

t.append(img_vector)

test_img_pattern.append(t)

return test_img_pattern

class BPNetwork:

# 神经网络类

def __init__(self, in_count, hiden_count, out_count, in_rate, hiden_rate):

"""

:param in_count: 输入层数

:param hiden_count: 隐藏层数

:param out_count: 输出层数

:param in_rate: 输入层学习率

:param hiden_rate: 隐藏层学习率

"""

# 各个层的节点数量

self.in_count = in_count

self.hiden_count = hiden_count

self.out_count = out_count

# 输入层到隐藏层连线的权重随机初始化

self.w1 = 0.2 * \

np.random.random((self.in_count, self.hiden_count)) - 0.1

# 隐藏层到输出层连线的权重随机初始化

self.w2 = 0.2 * \

np.random.random((self.hiden_count, self.out_count)) - 0.1

# 隐藏层偏置向量

self.hiden_offset = np.zeros(self.hiden_count)

# 输出层偏置向量

self.out_offset = np.zeros(self.out_count)

# 输入层学习率

self.in_rate = in_rate

# 隐藏层学习率

self.hiden_rate = hiden_rate

def train(self, train_img_pattern, train_label):

if self.in_count != len(train_img_pattern[0]):

sys.exit("输入层维数与样本维数不等")

# for num in range(10):

# for num in range(10):

for i in range(len(train_img_pattern)):

if i % 5000 == 0:

print(i)

# 生成目标向量

target = [0] * 10

target[train_label[i][0]] = 1

# for t in range(len(train_img_pattern[num])):

# 前向传播

# 隐藏层值等于输入层*w1+隐藏层偏置

hiden_value = np.dot(

train_img_pattern[i], self.w1) + self.hiden_offset

hiden_value = sigmoid(hiden_value)

# 计算输出层的输出

out_value = np.dot(hiden_value, self.w2) + self.out_offset

out_value = sigmoid(out_value)

# 反向更新

error = target - out_value

# 计算输出层误差

out_error = out_value * (1 - out_value) * error

# 计算隐藏层误差

hiden_error = hiden_value * \

(1 - hiden_value) * np.dot(self.w2, out_error)

# 更新w2，w2是j行k列的矩阵，存储隐藏层到输出层的权值

for k in range(self.out_count):

# 更新w2第k列的值，连接隐藏层所有节点到输出层的第k个节点的边

# 隐藏层学习率×输入层误差×隐藏层的输出值

self.w2[:, k] += self.hiden_rate * out_error[k] * hiden_value

# 更新w1

for j in range(self.hiden_count):

self.w1[:, j] += self.in_rate * \

hiden_error[j] * train_img_pattern[i]

# 更新偏置向量

self.out_offset += self.hiden_rate * out_error

self.hiden_offset += self.in_rate * hiden_error

def test(self, test_img_pattern):

"""

测试神经网络的正确率

:param test_img_pattern[num][t]表示数字num的第t张图片

:return:

"""

right = np.zeros(10)

test_sum = 0

for num in range(10): # 10个数字

# print("正在识别", num)

num_count = len(test_img_pattern[num])

test_sum += num_count

for t in range(num_count): # 数字num的第t张图片

hiden_value = np.dot(

test_img_pattern[num][t], self.w1) + self.hiden_offset

hiden_value = sigmoid(hiden_value)

out_value = np.dot(hiden_value, self.w2) + self.out_offset

out_value = sigmoid(out_value)

# print(out_value)

if np.argmax(out_value) == num:

# 识别正确

right[num] += 1

print("数字%d的识别正确率%f" % (num, right[num] / num_count))

# 平均识别率

print("平均识别率为：", sum(right) / test_sum)

"""

def test1:

"""

def run():

# 读入训练集

train, train_label = get_train_pattern()

# 读入测试图片

test_pattern = get_test_pattern()

# 神经网络配置参数

in_count = 28 * 28

hiden_count = 6

out_count = 10

in_rate = 0.1

hiden_rate = 0.1

bpnn = BPNetwork(in_count, hiden_count, out_count, in_rate, hiden_rate)

bpnn.train(train, train_label)

bpnn.test(test_pattern)

# 单张测试

# 识别单独一张图片，返回识别结果

"""

while True:

img_name = input("输入要识别的图片\n")

base_url = "/home/lxp/F/developing_folder/intelligence_system/bpneuralnet/"

img_url = base_url + img_name

img = Image.open(img_url)

img = img.convert('1') # 二值化

img_array = np.asarray(img, 'i') # 转化为int数组

# 得到图片的特征向量

img_v = img_array.reshape(img_array.shape[0] * img_array.shape[1]) # 展开成一维数组

bpnn.test1(img_v)

"""

if __name__ == "__main__":

run()

# train, train_label = get_train_pattern()

# print(train_label[5][0])

# test = get_test_pattern()