手写神经网络Python深度学习

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互联网集市收集整理的这篇技术教程文章主要介绍了手写神经网络Python深度学习，小编现在分享给大家，供广大互联网技能从业者学习和参考。文章包含2869字，纯文字阅读大概需要5分钟。

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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)

 

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