python神经网络编程实现手写数字识别
作者：wenmiao_
这篇文章主要为大家详细介绍了python神经网络编程实现手写数字识别，文中示例代码介绍的非常详细，具有一定的参考价值，感兴趣的小伙伴们可以参考一下
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 numpy
import scipy.special
#import matplotlib.pyplot
class neuralNetwork:
def __init__(self,inputnodes,hiddennodes,outputnodes,learningrate):
self.inodes=inputnodes
self.hnodes=hiddennodes
self.onodes=outputnodes
self.lr=learningrate
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))
self.activation_function=lambda x: scipy.special.expit(x)
pass
def train(self,inputs_list,targets_list):
inputs=numpy.array(inputs_list,ndmin=2).T
targets=numpy.array(targets_list,ndmin=2).T
hidden_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_outputs
hidden_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))
pass
def query(self,input_list):
inputs=numpy.array(input_list,ndmin=2).T
hidden_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_outputs
input_nodes=784
hidden_nodes=100
output_nodes=10
learning_rate=0.1
n=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)+0.01
targets=numpy.zeros(output_nodes)+0.01
targets[int(all_values[0])]=0.99
n.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)+0.01
outputs=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。
image_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 numpy
import scipy.special
#import matplotlib.pyplot
import scipy.misc
from PIL import Image
class neuralNetwork:
def __init__(self,inputnodes,hiddennodes,outputnodes,learningrate):
self.inodes=inputnodes
self.hnodes=hiddennodes
self.onodes=outputnodes
self.lr=learningrate
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))
self.activation_function=lambda x: scipy.special.expit(x)
pass
def train(self,inputs_list,targets_list):
inputs=numpy.array(inputs_list,ndmin=2).T
targets=numpy.array(targets_list,ndmin=2).T
hidden_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_outputs
hidden_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))
pass
def query(self,input_list):
inputs=numpy.array(input_list,ndmin=2).T
hidden_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_outputs
input_nodes=784
hidden_nodes=100
output_nodes=10
learning_rate=0.1
n=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)+0.01
targets=numpy.zeros(output_nodes)+0.01
targets[int(all_values[0])]=0.99
n.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)+0.01
outputs=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)+0.01
outputs=n.query(inputs)
label=numpy.argmax(outputs)
total[correct_lable]+=1
if(label==correct_lable):
scorecard.append(1)
rightsum[correct_lable]+=1
else:
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)
尝试统计了对于各个数据测试数量及正确率。
原本想验证书后向后查询中数字‘9'识别模糊是因为训练数量不足或错误率过高而产生，然最终结果并不支持此猜想。
另书中只能针对特定像素的图片进行学习，真正手写的图片并不能满足训练条件，实际用处仍需今后有时间改进。
以上就是本文的全部内容，希望对大家的学习有所帮助，也希望大家多多支持脚本之家。