从零开始用Python搭建超级简单的点击率预估模型

矫情吗；* 2023-06-11 15:30 3阅读 0赞

简书《从零开始用Python搭建超级简单的点击率预估模型》  
作者：流川枫AI  
链接：[https://www.jianshu.com/p/131e6701f7ec][https_www.jianshu.com_p_131e6701f7ec]

# coding:utf-8
    import numpy as np 
    
    class NeuralNetwork(): 
        # 随机初始化权重
        def __init__(self): 
            np.random.seed(1) 
            self.synaptic_weights = 2 * np.random.random((3, 1)) - 1 
        
        # 定义激活函数：这里使用sigmoid
        def sigmoid(self, x):  
            return 1 / (1 + np.exp(-x)) 
        
        #计算Sigmoid函数的偏导数 
        def sigmoid_derivative(self, x): 
            return x * (1 - x)
        
        # 训练模型 
        def train(self, training_inputs, training_outputs,learn_rate, training_iterations): 
            # 迭代训练
            for iteration in range(training_iterations): 
                #前向计算 
                output = self.think(training_inputs) 
                # 计算误差 
                error = training_outputs - output 
                # 反向传播-BP-微调权重 
                adjustments = np.dot(training_inputs.T, error * self.sigmoid_derivative(output)) 
                self.synaptic_weights += learn_rate*adjustments 
        
        def think(self, inputs): 
            # 输入通过网络得到输出 
            # 转化为浮点型数据类型 
            inputs = inputs.astype(float) 
            output = self.sigmoid(np.dot(inputs, self.synaptic_weights)) 
            return output 
    
    if __name__ == "__main__": 
        # 初始化前馈神经网络类 
        neural_network = NeuralNetwork() 
        print "随机初始化的权重矩阵W"
        print neural_network.synaptic_weights
        # 模拟训练数据X
        train_data=[[0,0,1], [1,1,1], [1,0,1], [0,1,1]]
        training_inputs = np.array(train_data) 
        # 模拟训练数据Y
        training_outputs = np.array([[0,1,1,0]]).T 
        # 定义模型的参数：
        # 参数学习率
        learn_rate=0.1
        # 模型迭代的次数
        epoch=150000
        neural_network.train(training_inputs, training_outputs, learn_rate, epoch) 
        print "迭代计算之后权重矩阵W: "
        print neural_network.synaptic_weights
        # 模拟需要预测的数据X
        pre_data=[0,0,1]
        # 使用训练的模型预测该微博被点击的概率
        print "该微博被点击的概率："
        print neural_network.think(np.array(pre_data))
    
    """ 终端输出的结果： 随机初始化的权重矩阵W [[-0.16595599] [ 0.44064899] [-0.99977125]] 迭代计算之后权重矩阵W: [[12.41691302] [-0.20410552] [-6.00463275]] 该微博被点击的概率： [0.00246122] [Finished in 20.2s] """

[https_www.jianshu.com_p_131e6701f7ec]: https://www.jianshu.com/p/131e6701f7ec