tensorflow实现自编码器

简介

• 自编码器是利用神经网络提取出图像中的高阶特征，同时可以利用高阶特征重构自己
• 如果向原图中添加噪声，则可以通过高阶特征的提取，对原始图像进行去噪
• tensorflow实战第四章内容

代码

import numpy as npimport sklearn.preprocessing as prepimport tensorflow as tffrom tensorflow.examples.tutorials.mnist import input_datadef xavier_init( fan_in, fan_out, constant = 1 ):    low  = -constant * np.sqrt( 6.0 / ( fan_in + fan_out ) )    high =  constant * np.sqrt( 6.0 / ( fan_in + fan_out ) )    return tf.random_uniform((fan_in, fan_out), minval=low, maxval=high, dtype=tf.float32 )class AdditiveGaussianNoiseAutoencoder(object):    def __init__(self, n_input, n_hidden, transfer_function=tf.nn.softplus, optimizer = tf.train.AdamOptimizer(), scale=0.1 ):        self.n_input = n_input        self.n_hidden = n_hidden        self.transfer = transfer_function        self.scale = tf.placeholder( tf.float32 )        self.training_scale = scale        network_weights = self._initialize_weights()        self.weights = network_weights        self.x = tf.placeholder( tf.float32, [None, self.n_input] )        self.hidden = self.transfer( tf.add( tf.matmul(self.x + scale * tf.random_normal(( n_input, ) ), self.weights['w1'] ), self.weights['b1'] ))        self.reconstruction = tf.add( tf.matmul( self.hidden, self.weights['w2'] ), self.weights['b2'] )        self.cost = 0.5 * tf.reduce_sum( tf.pow( tf.subtract( self.reconstruction, self.x ), 2 ) )        self.optimizer = optimizer.minimize( self.cost )        init = tf.global_variables_initializer()        self.sess = tf.Session()        self.sess.run( init )        print "begin to run session..."    def _initialize_weights(self):        all_weights = dict()        all_weights['w1'] = tf.Variable( xavier_init( self.n_input, self.n_hidden ) )        all_weights['b1'] = tf.Variable( tf.zeros( [self.n_hidden], dtype = tf.float32 ) )        all_weights['w2'] = tf.Variable( tf.zeros([self.n_hidden, self.n_input], dtype = tf.float32) )        all_weights['b2'] = tf.Variable( tf.zeros( [self.n_input], dtype = tf.float32 ) )        return all_weights    def partial_fit(self, X):        cost, opt = self.sess.run( (self.cost, self.optimizer),                                 feed_dict = { self.x : X, self.scale : self.training_scale } )        return cost    def calc_total_cost( self, X ):        return self.sess.run( self.cost, feed_dict = { self.x : X, self.scale : self.training_scale } )    def transform( self, X ):        return self.sess.run( self.hidden, feed_dict = { self.x : X, self.scale : self.training_scale } )    def generate( self, hidden = None ):        if hidden == None:            hidden = np.random.normal( size = self.weights['b1'] )        return self.sess.run( self.reconstruction, feed_dict = { self.hidden : hidden } )    def reconstruction( self, X ):        return self.sess.run( self.reconstruction, feed_dict = { self.x : X, self.scale : self.training_scale } )    def getWeights( self ):        return self.sess.run( self.weights['w1'] )    def getBiases( self ):        return self.sess.run( self.weights['b1'] )mnist = input_data.read_data_sets( '../MNIST_data', one_hot = True )def standard_scale( X_train, X_test ):    preprocessor = prep.StandardScaler().fit( X_train )    X_train = preprocessor.transform( X_train )    X_test  = preprocessor.transform( X_test )    return X_train, X_testdef get_random_block_from_data( data, batch_size ):    start_index = np.random.randint( 0, len(data) - batch_size )    return data[ start_index : (start_index+batch_size)  ]X_train, X_test  =standard_scale( mnist.train.images, mnist.test.images )n_samples = int( mnist.train.num_examples )training_epochs = 20batch_size = 128display_step = 1autoencoder = AdditiveGaussianNoiseAutoencoder( n_input = 784,                                                                                    n_hidden = 200,                                                                                     transfer_function = tf.nn.softplus,                                                                                    optimizer = tf.train.AdamOptimizer( learning_rate = 0.0001 ),                                                                                    scale = 0.01 )for epoch in range( training_epochs ):    avg_cost = 0    total_batch = int( n_samples / batch_size )    for i in range( total_batch ):        batch_xs = get_random_block_from_data( X_train, batch_size )        cost = autoencoder.partial_fit( batch_xs )        avg_cost = cost / n_samples * batch_size    if epoch % display_step == 0:        print( "epoch : %04d, cost = %.9f" % ( epoch+1, avg_cost ) )print( "Total cost : ",  str( autoencoder.calc_total_cost(X_test) 

说明

• 文件中mnist初始化时需要设置数据集的位置
• 隐藏层的节点数越大，训练后得到的误差越小，200个节点时，测试误差为60万左右，400个节点时，测试误差为20万左右
• 自己又加了一个隐藏层，但是效果好像不明显，随着训练次数的变化，训练误差呈现出发散的状态