BP神经网络在python下的自主搭建梳理，,本实验使用mnist

BP神经网络在python下的自主搭建梳理，,本实验使用mnist

本实验使用mnist数据集完成手写数字识别的测试。识别正确率认为是95%

完整代码如下：

  1 #!/usr/bin/env python  2 # coding: utf-8  3   4 # In[1]:  5   6   7 import numpy  8 import scipy.special  9 import matplotlib.pyplot 10  11  12 # In[2]: 13  14  15 class neuralNetwork: 16     def __init__(self, inputNodes, hiddenNodes, outputNodes,learningRate): 17         self.iNodes = inputNodes 18         self.oNodes = outputNodes 19         self.hNodes = hiddenNodes 20         self.lr = learningRate 21         self.wih = numpy.random.normal (0.0, pow(self.hNodes,-0.5), (self.hNodes, self.iNodes)) 22         self.who = numpy.random.normal (0.0, pow(self.oNodes,-0.5), (self.oNodes, self.hNodes)) 23          24         self.activation_function = lambda x: scipy.special.expit(x) 25         #print(self.wih) 26         pass 27      28     def train(self,inputs_list, target_list): 29         inputs = numpy.array(inputs_list, ndmin=2).T 30         targets = numpy.array(target_list, ndmin=2).T 31         #print(inputs) 32         #print(targets) 33         hidden_inputs = numpy.dot(self.wih,inputs) 34         #print(self.wih.shape) 35         #print(inputs.shape) 36         hidden_outputs = self.activation_function(hidden_inputs) 37         #print(hidden_inputs) 38         final_inputs = numpy.dot(self.who,hidden_outputs) 39         #print(hidden_outputs) 40         final_outputs = self.activation_function(final_inputs) 41          42         output_errors = targets - final_outputs 43         hidden_errors = numpy.dot(self.who.T,output_errors) 44         self.who += self.lr * numpy.dot((output_errors * final_outputs * (1.0 - final_outputs)),numpy.transpose(hidden_outputs)) 45         self.wih += self.lr * numpy.dot((hidden_errors * hidden_outputs * (1.0 - hidden_outputs)),numpy.transpose(inputs)) 46         pass 47      48     def query(self, inputs_list): 49         inputs = numpy.array(inputs_list, ndmin=2).T 50         hidden_inputs = numpy.dot(self.wih,inputs) 51         hidden_outputs = self.activation_function(hidden_inputs) 52         final_inputs = numpy.dot(self.who,hidden_outputs) 53         final_outpus = self.activation_function(final_inputs) 54         return final_outpus 55         pass 56      57  58  59 # In[3]: 60  61  62 inputNodes = 784 63 outputNodes = 10 64 hiddenNodes = 100 65 learningRate = 0.1 66 nN = neuralNetwork(inputNodes, hiddenNodes, outputNodes, learningRate) 67  68  69 # In[4]: 70  71  72 data_file = open("mnist_train.csv",‘r‘) 73 data_list = data_file.readlines() 74 data_file.close() 75  76  77 # In[5]: 78  79  80 epochs = 1 81 for e in range(epochs) : 82     for record in data_list: 83         all_values = record.split(‘,‘) 84         inputs = numpy.asfarray( all_values [1:])/255.0*0.99+0.01 85         targets = numpy.zeros(outputNodes) + 0.01 86         targets[int (all_values[0])] = 0.99 87         nN.train(inputs,targets) 88         pass 89     pass 90  91  92 # In[6]: 93  94  95 test_data_file = open("mnist_test.csv",‘r‘) 96 test_data_list = test_data_file.readlines() 97 test_data_file.close() 98  99 100 # In[7]:101 102 103 scorecard = []104 for record in test_data_list:105     all_values = record.split(‘,‘)106     correct_label = int(all_values[0])107     inputs = numpy.asfarray( all_values [1:])/255.0*0.99+0.01108     outputs = nN.query(inputs)109     label = numpy.argmax(outputs)110     if(label == correct_label):111         scorecard.append(1)112     else:113         scorecard.append(0)114         pass115     pass116 117 118 # In[8]:119 120 121 scorecard_array = numpy.asarray(scorecard)122 print ("performance = " ,scorecard_array.sum()/scorecard_array.size)123 124 125 # In[9]:126 127 128 import scipy.misc129 img_array = scipy.misc.imread(‘test.png‘,flatten="True")130 img_data = 255.0 - img_array . reshape(784)131 img_data = (img_data /255.0 * 0.99 ) + 0.01132 op=nN.query(img_data)133 print(op)134 print(numpy.argmax(op))135 136 137 # In[10]:138 139 140 all_values = data_list[1].split(‘,‘)141 image_array = numpy.asfarray( all_values [1:]).reshape((28,28))142 matplotlib.pyplot.imshow(image_array, cmap = ‘Greys‘,interpolation=‘None‘)

IN[9]到IN[10]的代码分别用于测试自己制作的数字识别效果和显示图像。

代码运行过程需要mnist数据集，链接：https://pan.baidu.com/s/120GTdZ8Tivkp1KD9VQ_XeQ

BP神经网络在python下的自主搭建梳理