python网格编程 mnist

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))#这里的乘矩阵法是对应元素项城
        self.wih+=self.lr*numpy.dot((hidden_errors*hidden_outputs*(1.0-hidden_outputs)),numpy.transpose(inputs))
        pass
     
    def query(self,inputs_list) :
        inputs=numpy.array(inputs_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=0.3
n=neuralNetwork(input_nodes,hidden_nodes,output_nodes,learning_rate)
data_file=open("mnist_train_100.csv",'r')
data_list=data_file.readlines()
data_file.close()
for record in 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)
    pass
test_data=open("mnist_test_10.csv",'r')
test_list=test_data.readlines()
test_data.close()
all_values=test_list[0].split(',')
print(all_values[0])
image_array=numpy.asfarray(all_values[1:]).reshape((28,28))
matplotlib.pyplot.imshow(image_array,cmap='Greys',interpolation='None')
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))#这里的乘矩阵法是对应元素项城
        self.wih+=self.lr*numpy.dot((hidden_errors*hidden_outputs*(1.0-hidden_outputs)),numpy.transpose(inputs))
        pass
     
    def query(self,inputs_list) :
        inputs=numpy.array(inputs_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=0.3
n=neuralNetwork(input_nodes,hidden_nodes,output_nodes,learning_rate)
data_file=open("mnist_train_100.csv",'r')
data_list=data_file.readlines()
data_file.close()
for record in 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)
    pass
test_data=open("mnist_test_10.csv",'r')
test_list=test_data.readlines()
test_data.close()
all_values=test_list[0].split(',')
print(all_values[0])
image_array=numpy.asfarray(all_values[1:]).reshape((28,28))
matplotlib.pyplot.imshow(image_array,cmap='Greys',interpolation='None')