1 #coding=utf-8
2 import numpy as np
3 import sklearn.preprocessing as prep
4 import tensorflow as tf
5 from tensorflow.examples.tutorials.mnist import input_data
6
7 def xavier_init(fan_in,fan_out, constant = 1):
8 '''Yoshua Bengio指出深度学习模型的权重初始化太小，那么信号将在每层传递缩小而失去作用；
9 太大将导致发散.Xavier初始化器就是让权重被初始化得不大不小，正好合适。
10 从数学的角度看，Xavier就是满足均值为：0，方差为2/(n_in+n_out)的均匀或高斯分布'''
11 low = -constant*np.sqrt(6.0/(fan_in+fan_out))
12 high = constant*np.sqrt(6.0/(fan_in+fan_out))
13 return tf.random_uniform((fan_in,fan_out), minval=low, maxval=high,dtype = tf.float32)
14 #去噪声自编码class
15 class AdditiveGaussianNoiseAutoencoder(object):
16
17 def __init__(self, n_input, n_hidden, transfer_function=tf.nn.softplus,
18 optimizer=tf.train.AdamOptimizer(), scale=0.1):
19 ''' n_input:输入变量数
20 n_hidden:隐含层节点数
21 transfer_function:隐含层激活函数,默认为softplus
22 optimizer:优化器，默认为Adam
23 scale:高斯噪声系数，默认为0.1'''
24 self.n_input = n_input
25 self.n_hidden = n_hidden
26 self.transfer = transfer_function
27 self.scale = tf.placeholder(tf.float32)
28 self.training_scale = scale
29 #参数初始化使用_initialize_weights()
30 network_weights = self._initialize_weights()
31 self.weights = network_weights
32
33 #输入x
34 self.x = tf.placeholder(tf.float32,[None,self.n_input])
35
36 '''隐藏层hidden,首先输入x加上噪声：self.x+scale*tf.random_normal((n_input,))
37 然后tf.matmul上式与隐含层权重w1，
38 tf.add加上隐含层biases：b1，
39 最后使用self.transfer对结果进行激活函数处理'''
40 self.hidden = self.transfer(tf.add(tf.matmul(
41 self.x+scale*tf.random_normal((n_input,)),
42 self.weights['w1']), self.weights['b1']))
43 '''输出层重构：reconstruction,不用激活函数
44 tf.matmul隐含层输出和输出层权重w2再加上输出层偏置b2'''
45 self.reconstruction = tf.add(tf.matmul(self.hidden,
46 self.weights['w2']), self.weights['b2'])
47
48 '''cost:直接使用平方误差即tf.substract计算输出self.reconstruction与self.x之差，
49 再使用tf.pow求差的平方'''
50 self.cost = 0.5*tf.reduce_sum(tf.pow(tf.subtract(
51 self.reconstruction, self.x), 2.0))
52
53
54 self.optimizer = optimizer.minimize(self.cost)
55
56 init = tf.global_variables_initializer()
57 self.sess = tf.Session()
58 self.sess.run(init)
59
60 def _initialize_weights(self):
61 all_weights = dict()
62 '''w1使用xavier_init函数初始化，传入输入节点数和隐含层节点数，
63 它将返回一个比较适合softplus激活函数的权重初始分布'''
64 all_weights['w1'] = tf.Variable(xavier_init(self.n_input,
65 self.n_hidden))
66 #b1，w2，b2使用tf.zeros全部为0
67 all_weights['b1'] = tf.Variable(tf.zeros([self.n_hidden],
68 dtype = tf.float32))
69
70 all_weights['w2'] = tf.Variable(tf.zeros([self.n_hidden,
71 self.n_input], dtype = tf.float32))
72 all_weights['b2'] = tf.Variable(tf.zeros([self.n_input], dtype = tf.float32))
73
74 return all_weights
75
76 def partial_fit(self, X):
77
78 '''trian每一个batch数据并返回当年batch的cost
79 Session执行两个计算图的节点，cost和训练过程optimizer,
80 输入的feed_dict：输入数据x和噪声系数：scale'''
81
82 cost, opt = self.sess.run((self.cost, self.optimizer),
83 feed_dict = {self.x:X, self.scale: self.training_scale})
84
85 return cost
86
87
88 def calc_total_cost(self, X):
89 #计算cost
90 return self.sess.run(self.cost, feed_dict = {self.x:X,
91 self.scale:self.training_scale
92 })
93
94 def transform(self, X):
95 #计算抽象的特征，返回隐含层的输出结果
96 return self.sess.run(self.hidden, feed_dict = {self.x:X,
97 self.scale:self.training_scale
98 })
99
100 def generate(self, hidden = None):
101 #将高阶抽象特征复原为原始数据
102 if hidden is None:
103 hidden = bp.random_normal(size = self.weights['b1'])
104
105 return self.sess.run(self.reconstruction, feed_dict = {self.hidden:hidden})
106
107
108 def reconstruction(self, X):
109 '''整体运行一遍复原过程，包括提取高阶特征和用高阶特征复原原始数据
110 输入：原数据 输出：复原后的数据'''
111 return self.sess.run(self.reconstruction, feed_dict = {self.x:X,
112 self.scale:self.training_scale
113 })
114
115
116 def getWeights(self):
117 #获取隐含层权重w1
118 return self.sess.run(self.weights['w1'])
119
120 def getBiases(self):
121
122 #获取隐含层的偏置系数b1
123 return self.sess.run(self.weights['b1'])
124
125 mnist = input_data.read_data_sets('MNIST_data', one_hot = True)
126
127 def standard_scale(X_train, X_test):
128 '''对训练、测试data进行标准化处理（让数据变成均值为0,标准差为1的分布）'''
129 preprocessor = prep.StandardScaler().fit(X_train)
130 X_train = preprocessor.transform(X_train)
131 X_test = preprocessor.transform(X_test)
132 return X_train, X_test
133
134 def get_random_block_from_data(data, batch_size):
135 '''随机获取block数据：取一个0到len(data)-batch_size之间的随机整数
136 再以这个随机数作为block的起始位置，然后顺序取batch_size的数据'''
137 start_index = np.random.randint(0,len(data)-batch_size)
138
139 return data[start_index:(start_index+batch_size)]
140
141 X_train, X_test = standard_scale(mnist.train.images, mnist.test.images)
142
143 n_samples = int(mnist.train.num_examples)
144 training_epochs = 20
145 batch_size = 128
146 display_step = 1
147
148
149 autoencoder = AdditiveGaussianNoiseAutoencoder(n_input = 784,
150 n_hidden = 200,
151 transfer_function = tf.nn.softplus,
152 optimizer = tf.train.AdamOptimizer(learning_rate = 0.001),
153 scale = 0.01
154 )
155
156 for epoch in range(training_epochs):
157
158 avg_cost = 0.
159 total_batch = int(n_samples/batch_size)
160
161 for i in range(total_batch):
162 batch_xs = get_random_block_from_data(X_train, batch_size)
163
164
165 cost = autoencoder.partial_fit(batch_xs)
166 avg_cost += cost/n_samples*batch_size
167
168 if epoch%display_step == 0:
169 print("Epoch:", '%04d'%(epoch+1), "cost = ",
170 "{:.9f}".format(avg_cost))
171
172 print("Total cost:"+str(autoencoder.calc_total_cost(X_test)))
Epoch: 0001 cost = 20001.253988636
Epoch: 0002 cost = 12866.668271591
Epoch: 0003 cost = 10933.510055682
Epoch: 0004 cost = 9885.109014205
Epoch: 0005 cost = 10337.800752273
Epoch: 0006 cost = 9621.243082386
Epoch: 0007 cost = 8365.464159659
Epoch: 0008 cost = 8419.876629545
Epoch: 0009 cost = 8918.941588636
Epoch: 0010 cost = 8069.571899432
Epoch: 0011 cost = 8420.874543182
Epoch: 0012 cost = 8921.646592614
Epoch: 0013 cost = 8175.836280682
Epoch: 0014 cost = 7681.573177273
Epoch: 0015 cost = 7809.688360227
Epoch: 0016 cost = 8018.361381250
Epoch: 0017 cost = 7809.863368750
Epoch: 0018 cost = 8121.875443750
Epoch: 0019 cost = 9078.917564773
Epoch: 0020 cost = 8115.000298295
Total cost:668358.0
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