import numpy
import scipy.special
import matplotlib.pyplot as plt
class neuralNetWork:
def __init__(self,inputnodes,hiddennodes,outputnodes,learningrate):
self.inodes = inputnodes
self.hnodes = hiddennodes
self.onodes = outputnodes
self.wih = numpy.random.normal(0.0,pow(self.inodes, -0.5),(self.hnodes,self.inodes))
self.who = numpy.random.normal(0.0,pow(self.hnodes, -0.5),(self.onodes,self.hnodes))
self.lr = learningrate
self.activation_function = lambda x: scipy.special.expit(x) # 激活函数
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))
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 = 200
output_nodes = 10
learing_rate = 0.1
n = neuralNetWork(input_nodes,hidden_nodes,output_nodes,learing_rate)
train_data_file = open('mnist_train.csv', 'r')
train_data_list = train_data_file.readlines()
train_data_file.close()
epochs = 5
for e in range(epochs):
for record in train_data_list:
all_values = record.split(',')
#image_array = numpy.asfarray(all_values[1:]).reshape((28,28))
#plt.imshow(image_array,cmap='Greys',interpolation='None')
#plt.show()
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)
test_data_file = open('mnist_test.csv', 'r')
test_data_list = test_data_file.readlines()
test_data_file.close()
# all_values = test_data_list[0].split(',')
# # image_array = numpy.asfarray(all_values[1:]).reshape((28,28))
# # plt.imshow(image_array,cmap='Greys',interpolation='None')
# # plt.show()
# output = n.query((numpy.asfarray(all_values[1:])/ 255.0 * 0.99)+0.01)
scorecard = []
for record in test_data_list:
all_values = record.split(',')
correct_label = int(all_values[0])
#print(correct_label,'correct_label')
inputs = (numpy.asfarray(all_values[1:])/255.0 *0.99)+0.01
outputs = n.query(inputs)
label = numpy.argmax(outputs)
#print(label,'network answer')
if (label == correct_label):
scorecard.append(1)
else:
scorecard.append(0)
scorecard_array = numpy.asarray(scorecard)
print("performance = ",scorecard_array.sum() / scorecard_array.size)
