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
# 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] """