BP神经网络mnist手写数字识别Python实现

#!/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() + "/mnist_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()