手写神经网络Python深度学习

Posted 2020-12-01 Erick - LONG

import numpy
import scipy.special
import matplotlib.pyplot as plt
import scipy.misc
import glob
import imageio
import scipy.ndimage

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
    self.inverse_activation_function = lambda x: scipy.special.logit(x) # 反向查询log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
  def backquery(self, targets_list):
    final_outputs = numpy.array(targets_list, ndmin=2).T
  
    final_inputs = self.inverse_activation_function(final_outputs)
    hidden_outputs = numpy.dot(self.who.T, final_inputs)
    
    hidden_outputs -= numpy.min(hidden_outputs)
    hidden_outputs /= numpy.max(hidden_outputs)
    hidden_outputs *= 0.98
    hidden_outputs += 0.01

    hidden_inputs = self.inverse_activation_function(hidden_outputs)
    inputs = numpy.dot(self.wih.T, hidden_inputs)
    inputs -= numpy.min(inputs)
    inputs /= numpy.max(inputs)
    inputs *= 0.98
    inputs += 0.01
    
    return inputs
  

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)

    #手写字体倾斜10度作为测试数据
    inputs_plusx_img = scipy.ndimage.interpolation.rotate(inputs.reshape(28,28), 10, cval=0.01, order=1, reshape=False)
    n.train(inputs_plusx_img.reshape(784), targets)
    inputs_minusx_img = scipy.ndimage.interpolation.rotate(inputs.reshape(28,28), -10, cval=0.01, order=1, reshape=False)
    n.train(inputs_minusx_img.reshape(784), 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)

# 识别自己手写字
our_own_dataset = []

for image_file_name in glob.glob(‘2828_my_own_?.png‘):
  label = int(image_file_name[-5:-4])
  
  print ("loading ... ", image_file_name)
  img_array = imageio.imread(image_file_name, as_gray=True)
  img_data  = 255.0 - img_array.reshape(784)
  
  img_data = (img_data / 255.0 * 0.99) + 0.01
  print(numpy.min(img_data))
  print(numpy.max(img_data))
  
  record = numpy.append(label,img_data)
  our_own_dataset.append(record)

item = 2
plt.imshow(our_own_dataset[item][1:].reshape(28,28), cmap=‘Greys‘, interpolation=‘None‘)
correct_label = our_own_dataset[item][0]
inputs = our_own_dataset[item][1:]

outputs = n.query(inputs)
print (outputs)

label = numpy.argmax(outputs)
print("network says ", label)
if (label == correct_label):
    print ("match!")
else:
    print ("no match!")

# 反向生成图像
label = 0
targets = numpy.zeros(output_nodes) + 0.01
targets[label] = 0.99
print(targets)

image_data = n.backquery(targets)

plt.imshow(image_data.reshape(28,28), cmap=‘Greys‘, interpolation=‘None‘)