33.111

import numpy
import scipy.special
import scipy.misc
import matplotlib.pyplot
import scipy.ndimage

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,input_list,target_list):
inputs=numpy.array(input_list,ndmin=2).T
targets=numpy.array(target_list,ndmin=2).T
hidden_inputs=numpy.dot(self.wih,inputs)
hidden_outputs=self.activation_function(hidden_inputs)
hidden_outputs1=numpy.append(hidden_outputs,-1)
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
print('n')
input_nodes=2
hidden_nodes=4
output_nodes=1
learning_rate=0.1
n=NeuralNetwork(input_nodes,hidden_nodes,output_nodes,learning_rate)
training_data_file=open(r'D:\3.3 data_te.txt')
training_data_list=training_data_file.readlines();
training_data_file.close()
print(training_data_list[0])
for record in training_data_list[1:]:
all_values=record.split(',')
inputs=(numpy.asfarray(all_values[0:2]))
targets=numpy.zeros(output_nodes)
targets[0]=all_values[2]
n.train(inputs,targets)
pass
test_data_file=open(r'D:\3.3 data_tr.txt')
test_data_list=test_data_file.readlines()
test_data_file.close()
scorecard=[]
total=0
correct=0
for record in test_data_list[1:]:
total+=1
all_values=record.split(',')
correct_label=all_values[2]
inputs=(numpy.asfarray(all_values[0:2]))
outputs=n.query(inputs)
print(outputs)