Python製作神經網路，程式碼如下:

import numpy
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
        self.wih = numpy.random.normal(0.0, pow(self.hnodes,-0.5), 
                                       (self.hnodes, self.inodes))
        self.who = numpy.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)
        
        print("初始化權重矩陣wih：")
        print(self.wih)
        print("初始化權重矩陣who：")
        print(self.who)
        
        pass
    
    #train the neural network
    def train(self, inputs_list, targets_list):
        
        # convert inputs list to 2d array
        inputs = numpy.array(inputs_list, ndmin=2).T
        targets = numpy.array(targets_list, ndmin=2).T
        # calculate signals into hidden layer
        hidden_inputs = numpy.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 = numpy.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 = numpy.dot(self.who.T, output_errors)
        # update the weights for the links between the hidden and output layers
        self.who += self.lr * numpy.dot((output_errors*final_outputs * 
                (1.0 - final_outputs)),numpy.transpose(hidden_outputs))
        # update the weights for the links between the input and hidden layers
        self.wih += self.lr * numpy.dot((hidden_errors * hidden_outputs * 
                (1.0 - hidden_outputs)), numpy.transpose(inputs))        
        
        print("訓練後的權重矩陣wih：")
        print(self.wih)
        print("訓練後的權重矩陣who：")
        print(self.who)
        
        pass
    
    #query the neural network
    def query(self,inputs_list):
        # convert inputs list to 2d array
        inputs = numpy.array(inputs_list, ndmin=2).T
        # calculate signals into hidden layer
        hidden_inputs = numpy.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 = numpy.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 0.5
learning_rate = 0.3
# create instance of neural network
n = neuralNetwork(input_nodes,hidden_nodes,output_nodes,learning_rate)

print("未經訓練的輸出層輸出矩陣：")
print(n.query([1.0,0.5,-1.5]))

n.train([1.0,0.5,-1.5],[0.8,0.6,-1.2])

print("訓練後的輸出層輸出矩陣：")
print(n.query([1.0,0.5,-1.5]))
 

執行結果如下：