BP神经网络的Python实现

来源：互联网发布：阿里域名备案编辑：程序博客网时间：2024/05/16 06:30
测试代码和更多机器学习算法详见 https://github.com/WiseDoge/ML-by-Python
import numpy as npclass BPNetWork(object):    """    全连接神经网络，采用BP算法训练。    """    def __init__(self, layers, act_func='tanh'):        """        :param layers: 神经网络的结构        :param act_func: 激励函数        输入样例：        ann = BPNN((2, 3, 1))        表示一个输入层，一个隐含层，一个输出层，输入层有2个结点，        隐含层有3个结点，输出层有1个结点        ann = BPNN((2, 3, 3, 1))        表示一个输入层，二个隐含层，一个输出层，输入层有2个结点，        第一层隐含层有3个结点，第二层隐含层有3个结点，输出层有1个结点        """        # 初始化神经元的值        self.networks = []        # 初始化神经元权重        self.weights = []        for i in range(len(layers) - 1):            weight = 2 * np.random.random((layers[i], layers[i + 1])) - 1            network = [1.0] * layers[i]            self.networks.append(network)            self.weights.append(weight)        self.networks.append([1.0] * layers[-1])        self.networks = np.array(self.networks)        # 初始化神经元阈值        self.thresholds = []        for i in range(1, len(layers)):            threshold = 2 * np.random.random(layers[i]) - 1            self.thresholds.append(threshold)        # 选择激励函数和它的导函数        if act_func == 'tanh':            self.act_func = self.tanh            self.dact_func = self.dthanh        else:            self.act_func = self.sigmoid            self.dact_func = self.dsigmoid    def sigmoid(self, x):        return 1 / (1 + np.exp(-x))    def dsigmoid(self, x):        return self.sigmoid(x) * (1 - self.sigmoid(x))    def tanh(self, x):        return np.tanh(x)    def dthanh(self, x):        return 1.0 - np.tanh(x) ** 2    def fit(self, train_x, train_y, epochs, learn_rate):        '''        :param train_x: 训练集X        :param train_y: 训练集Y        :param epochs: 迭代次数        :param learn_rate: 学习率，步长        :return: None        拟合神经网络        '''        for i in range(epochs):            i = np.random.randint(train_x.shape[0], high=None)            self.update(train_x[i])            self.back_propagate(train_y[i], learn_rate)    def predict(self, test_x):        '''        :param test_x: 测试集合        :return: 预测值        '''        self.update(test_x)        return self.networks[-1].copy()    def update(self, inputs):        '''        :param inputs: X的输入值        :return: None        更新一次神经元的值        '''        self.networks[0] = inputs.copy()        for i in range(len(self.weights)):            count = np.dot(self.networks[i], self.weights[i]) - self.thresholds[i]            self.networks[i + 1] = self.act_func(count)    def back_propagate(self, y, rate):        '''        :param y: target        :param rate: 学习率        :return: None        BP算法，对神经网络的权值和阈值进行更新        '''        errors = y - self.networks[-1]        gradients = [self.dact_func(self.networks[-1]) * errors]        self.thresholds[-1] += (-1) * rate * gradients[-1]        for i in range(len(self.weights) - 1, 0, -1):            gradients.append(gradients[-1].dot(self.weights[i].T) * self.dact_func(self.networks[i]))            self.thresholds[i - 1] += (-1) * rate * gradients[-1]        gradients.reverse()        for i in range(len(self.weights)):            self.weights[i] += rate * self.networks[i].reshape((-1, 1)) * gradients[i]
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