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 ]])
