用BP算法实现神经网络.zip

import numpy # S型激活函数引入 import scipy.special # 矩阵运算 import matplotlib.pyplot # import shelve # 神经网络class定义 class neuralNetwork : # 初始化 def __init__(self, inputnodes, hiddennodes, outputnodes, learningrate): # 初始化神经网络输入层，隐藏层，输出层节点 self.inodes = inputnodes self.hnodes = hiddennodes self.onodes = outputnodes # 初始化输入层到隐藏层，隐藏层到输出层的权重 self.wih = numpy.random.normal(0.0, pow(self.hnodes, -0.5), (self.hnodes, self.inodes)) self.who = numpy.random.normal(0.0, pow(self.onodes, -0.5), (self.onodes, self.hnodes)) # 学习率 self.lr = learningrate # S型激活函数 self.activation_function = lambda x : scipy.special.expit(x) pass # 训练函数 def train(self, input_list, targets_list): # 转换成二维矩阵并转置 inputs = numpy.array(input_list, ndmin=2).T targets = numpy.array(targets_list, ndmin=2).T # 隐藏层输入 hidden_inputs = numpy.dot(self.wih, inputs) # 隐藏层输出 hidden_outputs = self.activation_function(hidden_inputs) # 输出层输入 final_inputs = numpy.dot(self.who, hidden_outputs) # 输出层输出（最终输出） final_outputs = self.activation_function(final_inputs) # 输出层误差 output_errors = targets - final_outputs # 隐藏层误差 hidden_errors = numpy.dot(self.who.T, output_errors) # 隐含层到输出层权重调整 self.who += self.lr * numpy.dot((output_errors * final_outputs * (1.0 - final_outputs)), numpy.transpose(hidden_outputs)) # 输入层到隐藏层权重调整 self.wih += self.lr * numpy.dot((hidden_errors * hidden_outputs * (1.0 - hidden_outputs)), numpy.transpose(inputs)) pass # 检验神经网络 def check(self, inputs_list): # 转换成二维矩阵并转置 inputs = numpy.array(inputs_list, ndmin=2).T # 隐藏层输入 hidden_inputs = numpy.dot(self.wih, inputs) # 隐藏层输出 hidden_outputs = self.activation_function(hidden_inputs) # 输出层输入 final_inputs = numpy.dot(self.who, hidden_outputs) # 输出层输出 final_outputs = self.activation_function(final_inputs) return final_outputs # 性能 def performance(hidden_nodes, learning_rate, epochs) : # 设置各层节点 input_nodes = 784 # hidden_nodes = 200 output_nodes = 10 # 学习率 # learning_rate = 0.1 # 创建神经网络模型 n = neuralNetwork(input_nodes, hidden_nodes, output_nodes, learning_rate) # 读取训练数据 training_data_file = open("C:/Users/DELL/Desktop/Python神经网络/mnist_train.csv", 'r') train_data_list = training_data_file.readlines() training_data_file.close() # 迭代5次 # epochs = 5 # 训练模型 for e in range(epochs): # 循环读取记录 for record in train_data_list: # 解析数据 all_values = record.split(',') # 归一化处理（标准化处理） inputs = (numpy.asfarray(all_values[1:]) / 255.0 * 0.99) + 0.01 targets = numpy.zeros(output_nodes) + 0.01 targets[int(all_values[0])] = 0.99 n.train(inputs, targets) pass pass # 读取测试数据 test_data_file = open("C:/Users/DELL/Desktop/Python神经网络/mnist_test.csv", 'r') test_data_list = test_data_file.readlines() test_data_file.close() # 测试结果数据 scorecard = [] # 测试模型 for record in test_data_list: # 解析数据 all_values = record.split(',') # 正确结果 correct_label = int(all_values[0]) # 输入数据 inputs = (numpy.asfarray(all_values[1:]) / 255.0 * 0.99) + 0.01 # 输出数据 outputs = n.check(inputs) # 取输出节点中的最大值 label = numpy.argmax(outputs) if (label == correct_label): scorecard.append(1) else: scorecard.append(0) pass pass scorecard_array = numpy.asarray(scorecard) return scorecard_array.sum() / scorecard_array.size # print("正确率：", scorecard_array.sum() / scorecard_array.size) # db = shelve.open("C:/Users/DELL/Desktop/Python神经网络/compare/200-") # db['wih'] = n.wih # db['who'] = n.who # db.close( )