计算机毕业设计中用python神经网络编程实现手写数字识别

本文实例为大家分享了python实现手写数字识别的具体代码，供大家参考，具体内容如下


import numpyimport scipy.special#import matplotlib.pyplotclass neuralNetwork:def __init__(self,inputnodes,hiddennodes,outputnodes,learningrate):self.inodes=inputnodesself.hnodes=hiddennodesself.onodes=outputnodesself.lr=learningrateself.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))self.activation_function=lambda x: scipy.special.expit(x)passdef train(self,inputs_list,targets_list):inputs=numpy.array(inputs_list,ndmin=2).Ttargets=numpy.array(targets_list,ndmin=2).Thidden_inputs=numpy.dot(self.wih,inputs)hidden_outputs=self.activation_function(hidden_inputs)final_inputs=numpy.dot(self.who,hidden_outputs)final_outputs=self.activation_function(final_inputs)output_errors=targets-final_outputshidden_errors=numpy.dot(self.who.T,output_errors)self.who+=self.lr*numpy.dot((output_errors*final_outputs*(1.0-final_outputs)),numpy.transpose(hidden_outputs))self.wih+=self.lr*numpy.dot((hidden_errors*hidden_outputs*(1.0-hidden_outputs)),numpy.transpose(inputs))passdef query(self,input_list):inputs=numpy.array(input_list,ndmin=2).Thidden_inputs=numpy.dot(self.wih,inputs)hidden_outputs=self.activation_function(hidden_inputs)final_inputs=numpy.dot(self.who,hidden_outputs)final_outputs=self.activation_function(final_inputs)return final_outputsinput_nodes=784hidden_nodes=100output_nodes=10learning_rate=0.1n=neuralNetwork(input_nodes,hidden_nodes,output_nodes,learning_rate)training_data_file=open(r"C:\Users\lsy\Desktop\nn\mnist_train.csv","r")training_data_list=training_data_file.readlines()training_data_file.close()#print(n.wih)#print("")epochs=2for e in range(epochs):for record in training_data_list:all_values=record.split(",")inputs=(numpy.asfarray(all_values[1:])/255.0*0.99)+0.01targets=numpy.zeros(output_nodes)+0.01targets[int(all_values[0])]=0.99n.train(inputs,targets)#print(n.wih)#print(len(training_data_list))#for i in training_data_list:# print(i)test_data_file=open(r"C:\Users\lsy\Desktop\nn\mnist_test.csv","r")test_data_list=test_data_file.readlines()test_data_file.close()scorecard=[]for record in test_data_list:all_values=record.split(",")correct_lable=int(all_values[0])inputs=(numpy.asfarray(all_values[1:])/255.0*0.99)+0.01outputs=n.query(inputs)label=numpy.argmax(outputs)if(label==correct_lable):scorecard.append(1)else:scorecard.append(0)scorecard_array=numpy.asarray(scorecard)print(scorecard_array)print("")print(scorecard_array.sum()/scorecard_array.size)#all_value=test_data_list[0].split(",")#input=(numpy.asfarray(all_value[1:])/255.0*0.99)+0.01#print(all_value[0])#image_array=numpy.asfarray(all_value[1:]).reshape((28,28))#matplotlib.pyplot.imshow(image_array,cmap="Greys",interpolation="None")#matplotlib.pyplot.show()#nn=n.query((numpy.asfarray(all_value[1:])/255.0*0.99)+0.01)#for i in nn :# print(i)```《python神经网络编程》中代码，仅做记录，以备后用。 ```pythonimage_file_name=r"*.JPG"img_array=scipy.misc.imread(image_file_name,flatten=True)img_data=255.0-img_array.reshape(784)image_data=(img_data/255.0*0.99)+0.01图片对应像素的读取。因训练集灰度值与实际相反，故用255减取反。import numpyimport scipy.special#import matplotlib.pyplotimport scipy.miscfrom PIL import Imageclass neuralNetwork:def __init__(self,inputnodes,hiddennodes,outputnodes,learningrate):self.inodes=inputnodesself.hnodes=hiddennodesself.onodes=outputnodesself.lr=learningrateself.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))self.activation_function=lambda x: scipy.special.expit(x)passdef train(self,inputs_list,targets_list):inputs=numpy.array(inputs_list,ndmin=2).Ttargets=numpy.array(targets_list,ndmin=2).Thidden_inputs=numpy.dot(self.wih,inputs)hidden_outputs=self.activation_function(hidden_inputs)final_inputs=numpy.dot(self.who,hidden_outputs)final_outputs=self.activation_function(final_inputs)output_errors=targets-final_outputshidden_errors=numpy.dot(self.who.T,output_errors)self.who+=self.lr*numpy.dot((output_errors*final_outputs*(1.0-final_outputs)),numpy.transpose(hidden_outputs))self.wih+=self.lr*numpy.dot((hidden_errors*hidden_outputs*(1.0-hidden_outputs)),numpy.transpose(inputs))passdef query(self,input_list):inputs=numpy.array(input_list,ndmin=2).Thidden_inputs=numpy.dot(self.wih,inputs)hidden_outputs=self.activation_function(hidden_inputs)final_inputs=numpy.dot(self.who,hidden_outputs)final_outputs=self.activation_function(final_inputs)return final_outputsinput_nodes=784hidden_nodes=100output_nodes=10learning_rate=0.1n=neuralNetwork(input_nodes,hidden_nodes,output_nodes,learning_rate)training_data_file=open(r"C:\Users\lsy\Desktop\nn\mnist_train.csv","r")training_data_list=training_data_file.readlines()training_data_file.close()#print(n.wih)#print("")#epochs=2#for e in range(epochs):for record in training_data_list:all_values=record.split(",")inputs=(numpy.asfarray(all_values[1:])/255.0*0.99)+0.01targets=numpy.zeros(output_nodes)+0.01targets[int(all_values[0])]=0.99n.train(inputs,targets)#image_file_name=r"C:\Users\lsy\Desktop\nn\1000-1.JPG"'''img_array=scipy.misc.imread(image_file_name,flatten=True)img_data=255.0-img_array.reshape(784)image_data=(img_data/255.0*0.99)+0.01#inputs=(numpy.asfarray(image_data)/255.0*0.99)+0.01outputs=n.query(image_data)label=numpy.argmax(outputs)print(label)'''#print(n.wih)#print(len(training_data_list))#for i in training_data_list:# print(i)test_data_file=open(r"C:\Users\lsy\Desktop\nn\mnist_test.csv","r")test_data_list=test_data_file.readlines()test_data_file.close()scorecard=[]total=[0,0,0,0,0,0,0,0,0,0]rightsum=[0,0,0,0,0,0,0,0,0,0]for record in test_data_list:all_values=record.split(",")correct_lable=int(all_values[0])inputs=(numpy.asfarray(all_values[1:])/255.0*0.99)+0.01outputs=n.query(inputs)label=numpy.argmax(outputs)total[correct_lable]+=1if(label==correct_lable):scorecard.append(1)rightsum[correct_lable]+=1else:scorecard.append(0)scorecard_array=numpy.asarray(scorecard)print(scorecard_array)print("")print(scorecard_array.sum()/scorecard_array.size)print("")print(total)print(rightsum)for i in range(10):print((rightsum[i]*1.0)/total[i])#all_value=test_data_list[0].split(",")#input=(numpy.asfarray(all_value[1:])/255.0*0.99)+0.01#print(all_value[0])#image_array=numpy.asfarray(all_value[1:]).reshape((28,28))#matplotlib.pyplot.imshow(image_array,cmap="Greys",interpolation="None")#matplotlib.pyplot.show()#nn=n.query((numpy.asfarray(all_value[1:])/255.0*0.99)+0.01)#for i in nn :# print(i)

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计算机毕业设计中用python神经网络编程实现手写数字识别

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