Python 神经网络实现：基于 Sigmoid 函数的分类模型

import numpy as np

def loaddataset(filename): fp = open(filename) #存放数据 dataset = [] #存放标签 labelset = [] for i in fp.readlines(): a = i.strip().split() #每个数据行的最后一个是标签数据 dataset.append([float(j) for j in a[:len(a)-1]]) labelset.append(int(float(a[-1]))) return dataset, labelset

#x为输入层神经元个数，y为隐层神经元个数，z输出层神经元个数 def parameter_initialization(x, y, z): #隐层阈值 value1 = np.random.randint(-5, 5, (1, y)).astype(np.float64) #输出层阈值 value2 = np.random.randint(-5, 5, (1, z)).astype(np.float64) #输入层与隐层的连接权重 weight1 = np.random.randint(-5, 5, (x, y)).astype(np.float64) #隐层与输出层的连接权重 weight2 = np.random.randint(-5, 5, (y, z)).astype(np.float64) return weight1, weight2, value1, value2

#返回sigmoid函数值 def sigmoid(z): return 1 / (1 + np.exp(-z))

''' weight1:输入层与隐层的连接权重 weight2:隐层与输出层的连接权重 value1:隐层阈值 value2:输出层阈值 ''' def trainning(dataset, labelset, weight1, weight2, value1, value2): #dataset：数据集 labelset：标签数据 #x为步长 x = 0.01 for i in range(len(dataset)): #输入数据 inputset = np.mat(dataset[i]).astype(np.float64) #数据标签 outputset = np.mat(labelset[i]).astype(np.float64) #隐层输入 input1 = np.dot(inputset, weight1).astype(np.float64) #隐层输出 output2 = sigmoid(input1 - value1).astype(np.float64) #输出层输入 input2 = np.dot(output2, weight2).astype(np.float64) #输出层输出 output3 = sigmoid(input2 - value2).astype(np.float64) #更新公式由矩阵运算表示 a = np.multiply(output3, 1 - output3) g = np.multiply(a, outputset - output3) b = np.dot(g, np.transpose(weight2)) c = np.multiply(output2, 1 - output2) e = np.multiply(b, c) value1_change = -x * e value2_change = -x * g weight1_change = x * np.dot(np.transpose(inputset), e) weight2_change = x * np.dot(np.transpose(output2), g) #更新连接权重、阈值参数value1、value2、weight1、weight2 weight1 = weight1 - weight1_change weight2 = weight2 - weight2_change value1 = value1 - value1_change value2 = value2 - value2_change return weight1, weight2, value1, value2

def testing(dataset, labelset, weight1, weight2, value1, value2): #记录预测正确的个数 rightcount = 0 for i in range(len(dataset)): #计算每一个样例通过该神经网路后的预测值 inputset = np.mat(dataset[i]).astype(np.float64) outputset = np.mat(labelset[i]).astype(np.float64) output2 = sigmoid(np.dot(inputset, weight1) - value1) output3 = sigmoid(np.dot(output2, weight2) - value2) #确定其预测标签：输出大于 0.5 置 flag 为 1，否则置 flag 为 0 if output3 > 0.5: flag = 1 else: flag = 0 if labelset[i] == flag: rightcount += 1 #返回正确率 return rightcount / len(dataset)