20180421 qzd

ch02 - 使用python制作神经网络

构建框架

初始化函数 -- 设定输入层节点、隐藏层节点和输出层节点的数量。

训练 -- 学习给定训练集样本后，优化权重(权重--网络的核心)。

查询 -- 给定输入，从输出节点给出答案。

#neural network class definition

class neuralNetwork:

#initialise the neural network

def __init__():

pass

#train the neural network

def train():

pass

#query the neural network

def query():

pass

初始化网络

def __init__(self,inputnodes,hiddennodes,outputnodes,learningrate ):

权重 -- 网络的核心

在输入层与隐藏层之间的链接权重矩阵W input_hidden,大小为hidden_nodes乘以input_nodes。

在隐藏层和输出层之间的链接权重矩阵W hidden_output,大小为hidden_nodes乘以output_nodes。

初始化权重

#self.wih = (np.random.rand(self.hnodes,self.inodes) - 0.5)

#self.who = (np.random.rand(self.onodes,self.hnodes) - 0.5)

self.wih = np.random.normal(0.0,pow(self.hnodes,-0.5),(self.hnodes,self.inodes))

self.who = np.random.normal(0.0,pow(self.onodes,-0.5),(self.onodes,self.hnodes))

查询网络

def query(self,inputs_list):

训练网络(权重部分)

第一部分，针对给定的训练样本计算输出。这与我们刚刚在query()函数上所做的没什么区别。

第二部分，将计算得到的输出与所需输出对比，使用差值来指导网络权重的更新。

完整的神经网络代码

import numpy as np

#scipy.special for the sigmoid function expit()

import scipy.special

#neural network class definition

class neuralNetwork:

#initialise the neural network

def __init__(self,inputnodes,hiddennodes,outputnodes,learningrate ):

#set number of nodes in each input, hidden, output layer

self.inodes = inputnodes

self.hnodes = hiddennodes

self.onodes = outputnodes

#link weight matrices, wih and who

#weights inside the arrays are w_i_j, where link is from node i to node j in the next layer

# w11 w21

# w12 w22 etc

#self.wih = (np.random.rand(self.hnodes,self.inodes) - 0.5)

#self.who = (np.random.rand(self.onodes,self.hnodes) - 0.5)

self.wih = np.random.normal(0.0,pow(self.hnodes,-0.5),(self.hnodes,self.inodes))

self.who = np.random.normal(0.0,pow(self.onodes,-0.5),(self.onodes,self.hnodes))

#learning rate

self.lr = learningrate

#activation function is the sigmoid function

self.activation_function = lambda x:scipy.special.expit(x)

pass

#train the neural network

def train(self,inputs_list,targets_list):

#convert inputs list to 2d array

inputs = np.array(inputs_list,ndmin=2).T

targets = np.array(targets_list,ndmin=2).T

#calculate signals into hidden layer

hidden_inputs = np.dot(self.wih,inputs)

#calculate the signals emerging from hidden layer

hidden_outputs = self.activation_function(hidden_inputs)

#calculate signals into final output layer

final_inputs = np.dot(self.who,hidden_outputs)

#calculate the signals emerging from final output layer

final_outputs = self.activation_function(final_inputs)

#output layer error is the (target - actual)

output_errors = targets - final_outputs

#hidden layer error is the output_errors, split by weights, recombined at hidden nodes

hidden_errors = np.dot(self.who.T,output_errors)

#update the weights for the links between thehidden and output layers

self.who += self.lr *np.dot((output_errors * final_outputs*(1.0-final_outputs)), np.transpose(hidden_outputs))

#update the weights for the links between the input and hidden layers

self.wih += self.lr *np.dot((hidden_errors * hidden_outputs*(1.0 - hidden_outputs)),np.transpose(inputs))

pass

#query the neural network

def query(self,inputs_list):

#convert inputs list to 2d array

inputs = np.array(inputs_list,ndmin=2).T

#calculate signals into hidden layer

hidden_inputs = np.dot(self.wih,inputs)

#calculate the signals emerging from hidden layer

hidden_outputs = self.activation_function(hidden_inputs)

#calculate signals into final output layer

final_inputs = np.dot(self.who,hidden_outputs)

#calculate the signals emerging from final output layer

final_outputs = self.activation_function(final_inputs)

return final_outputs

#number of input, hidden and output nodes

input_nodes = 3

hidden_nodes = 3

output_nodes = 3

#learning rate is 0.3

learning_rate = 0.3

#create instance of neural network

n = neuralNetwork(input_nodes,hidden_nodes,output_nodes,learning_rate)

n.query([1.0,0.5,-1.5])

输出结果

array([[ 0.46356062],

[ 0.58538226],

[ 0.5905356 ]])