深度学习(一):Python神经网络——手写数字识别
2021-05-03 04:30
标签:mat sel 网络编程 cmap 深度学习 ror work sig __init__ 声明:本文章为阅读书籍《Python神经网络编程》而来,代码与书中略有差异,书籍封面: 若要本地运行,请更改源码中图片与数据集的位置,环境为 Python3.6x. 链接:百度网盘 深度学习(一):Python神经网络——手写数字识别 标签:mat sel 网络编程 cmap 深度学习 ror work sig __init__ 原文地址:https://www.cnblogs.com/oasisyang/p/13199480.html源码
1 import numpy as np
2 import scipy.special as ss
3 import matplotlib.pyplot as plt
4 import imageio as im
5 import glob as gl
6
7
8 class NeuralNetwork:
9 # initialise the network
10 def __init__(self, inputnodes, hiddennodes, outputnodes, learningrate):
11 # set number of each layer
12 self.inodes = inputnodes
13 self.hnodes = hiddennodes
14 self.onodes = outputnodes
15 self.wih = np.random.normal(0.0, pow(self.inodes, -0.5), (self.hnodes, self.inodes))
16 self.who = np.random.normal(0.0, pow(self.hnodes, -0.5), (self.onodes, self.hnodes))
17 # learning rate
18 self.lr = learningrate
19 # activation function is sigmoid
20 self.activation_function = lambda x: ss.expit(x)
21 pass
22
23 # train the neural network
24 def train(self, inputs_list, targets_list):
25 inputs = np.array(inputs_list, ndmin=2).T
26 targets = np.array(targets_list, ndmin=2).T
27 hidden_inputs = np.dot(self.wih, inputs)
28 hidden_outputs = self.activation_function(hidden_inputs)
29 final_inputs = np.dot(self.who, hidden_outputs)
30 final_outputs = self.activation_function(final_inputs)
31 # errors
32 output_errors = targets - final_outputs
33 # b-p algorithm
34 hidden_errors = np.dot(self.who.T, output_errors)
35 # update weight
36 self.who += self.lr * np.dot((output_errors * final_outputs * (1.0 - final_outputs)),
37 np.transpose(hidden_outputs))
38 self.wih += self.lr * np.dot((hidden_errors * hidden_outputs * (1.0 - hidden_outputs)), np.transpose(inputs))
39 pass
40
41 # query the neural network
42 def query(self, inputs_list):
43 inputs = np.array(inputs_list, ndmin=2).T
44 hidden_inputs = np.dot(self.wih, inputs)
45 hidden_outputs = self.activation_function(hidden_inputs)
46 final_inputs = np.dot(self.who, hidden_outputs)
47 final_outputs = self.activation_function(final_inputs)
48 return final_outputs
49
50 # numbers
51
52
53 input_nodes = 784
54 hidden_nodes = 100
55 output_nodes = 10
56
57 # learning rate
58 learning_rate = 0.2
59
60 # creat instance of neural network
61 global n
62 n = neuralNetwork(input_nodes, hidden_nodes, output_nodes, learning_rate)
63
64 # file read only ,root of the file
65 training_data_file = open(r"C:\Users\ELIO\Desktop\mnist_train.txt", ‘r‘)
66 training_data_list = training_data_file.readlines()
67 training_data_file.close()
68
69 # train the neural network
70 epochs = 5
71 for e in range(epochs):
72 for record in training_data_list:
73 all_values = record.split(‘,‘)
74 # scale and shift the inputs
75 inputs = (np.asfarray(all_values[1:]) / 255.0 * 0.99) + 0.01
76 targets = np.zeros(output_nodes) + 0.01
77 # all_values[0] is the target label for this record
78 targets[int(all_values[0])] = 0.99
79 n.train(inputs, targets)
80 pass
81 pass
82
83 # load the file into a list
84 test_data_file = open(r"C:\Users\ELIO\Desktop\mnist_train_100.csv.txt", ‘r‘)
85 test_data_list = test_data_file.readlines()
86 test_data_file.close()
87
88 # test the neural network
89 # score for how well the network performs
90 score = []
91
92 # go through all the records
93 for record in test_data_list:
94 all_values = record.split(‘,‘)
95 # correct answer is the first value
96 correct_label = int(all_values[0])
97 # scale and shift the inputs
98 inputs = (np.asfarray(all_values[1:]) / 255.0 * 0.99) + 0.01
99 # query the network
100 outputs = n.query(inputs)
101 # the index of the highest value corresponds to the label
102 label = np.argmax(outputs)
103 # append correct or incorrect to list
104 if (label == correct_label):
105 score.append(1)
106 else:
107 score.append(0)
108 pass
109 pass
110 # module1 CORRECT-RATE
111 # calculate the score, the fraction of correct answers
112 score_array = np.asarray(score)
113 print("performance = ", score_array.sum() / score_array.size)
114
115 # module2 TEST MNIST
116 all_values = test_data_list[0].split(‘,‘)
117 print(all_values[0])
118 image_array = np.asfarray(all_values[1:]).reshape((28, 28))
119 plt.imshow(image_array, cmap=‘Greys‘, interpolation=‘None‘)
120 plt.show()
121
122 # module3 USE YOUR WRITING
123 # own image test data set
124 own_dataset = []
125 for image_file_name in gl.gl(r‘C:\Users\ELIO\Desktop\5.png‘):
126 print("loading ... ", image_file_name)
127 # use the filename to set the label
128 label = int(image_file_name[-5:-4])
129 # load image data from png files into an array
130 img_array = im.imread(image_file_name, as_gray=True)
131 # reshape from 28x28 to list of 784 values, invert values
132 img_data = 255.0 - img_array.reshape(784)
133 # then scale data to range from 0.01 to 1.0
134 img_data = (img_data / 255.0 * 0.99) + 0.01
135 print(np.min(img_data))
136 print(np.max(img_data))
137 # append label and image data to test data set
138 record = np.append(label, img_data)
139 print(record)
140 own_dataset.append(record)
141 pass
142 all_values = own_dataset[0]
143 print(all_values[0])
数据集,实验图片
提取码:1vbq