Tesorflow-自动编码器(AutoEncoder)

 1 import numpy as np 2 import sklearn.preprocessing as prep 3 import tensorflow as tf 4 from tensorflow.examples.tutorials.mnist import input_data 5 ?6 def xavier_init(fan_in,fan_out,constant=1): 7 ????low=-constant*np.sqrt(6.0/(fan_in+fan_out)) 8 ????high=constant*np.sqrt(6.0/(fan_in+fan_out)) 9 ????return tf.random_uniform((fan_in,fan_out),minval=low,maxval=high,dtype=tf.float32)10 11 class AdditiveGaussianNoiseAutoencoder(object):12 ????def __init__(self,n_input,n_hidden,transfer_function=tf.nn.softplus,optimizer=tf.train.AdamOptimizer(),scale=0.1):13 ????????self.n_input=n_input14 ????????self.n_hidden=n_hidden15 ????????self.transfer=transfer_function16 ????????self.scale=tf.placeholder(tf.float32)17 ????????self.training_scale=scale18 ????????network_weights=self._initialize_weights()19 ????????self.weights=network_weights20 21 ????????self.x=tf.placeholder(tf.float32,[None,self.n_input])22 ????????self.hidden=self.transfer(tf.add(tf.matmul(self.x+scale*tf.random_normal((n_input,)),self.weights[‘w1‘]),self.weights[‘b1‘]))23 ????????self.reconstruction=tf.add(tf.matmul(self.hidden,self.weights[‘w2‘]),self.weights[‘b2‘])24 ????????self.cost=0.5*tf.reduce_sum(tf.pow(tf.sub(self.reconstruction,self.x),2.0))25 ????????self.optimizer=optimizer.minimize(self.cost)26 27 ????????init=tf.initialize_all_variables()28 ????????self.sess=tf.Session()29 ????????self.sess.run(init)30 31 ????def _initialize_weights(self):32 ????????all_weights=dict()33 ????????all_weights[‘w1‘]=tf.Variable(xavier_init(self.n_input,self.n_hidden))34 ????????all_weights[‘b1‘]=tf.Variable(tf.zeros([self.n_hidden],dtype=tf.float32))35 ????????all_weights[‘w2‘]=tf.Variable(tf.zeros([self.n_hidden,self.n_input],dtype=tf.float32))36 ????????all_weights[‘b2‘]=tf.Variable(tf.zeros([self.n_input],dtype=tf.float32))37 38 ????????return all_weights39 40 ????def partial_fit(self,X):41 42 ????????cost,opt=self.sess.run((self.cost,self.optimizer),feed_dict={self.x:X,self.scale:self.training_scale})43 44 ????????return cost45 46 ????def calc_total_cost(self,X):47 ????????return self.sess.run(self.cost,feed_dict={self.x:X,self.scale:self.training_scale})48 49 ????def transform(self,X):50 ????????return self.sess.run(self.hidden,feed_dict={self.x:X,self.scale:self.training_scale})51 52 ????def generate(self,hidden=None):53 ????????if hidden is None:54 ????????????hidden=np.random.normal(size=self.weights["b1"])55 ????????return self.sess.run(self.reconstruction,feed_dict={self.hidden:hidden})56 57 ????def reconstruct(self,X):58 ????????return self.sess.run(self.reconstruction,feed_dict={self.x:X,self.scale:self.training_scale})59 60 ????def getWeights(self):61 ????????return self.sess.run(self.weights[‘w1‘])62 63 ????def getBiases(self):64 ????????return self.sess.run(self.weights[‘b1‘])65 66 mnist=input_data.read_data_sets(‘MNIST_data‘,one_hot=True)67 68 def standard_scale(X_train,X_test):69 ????preprocessor=prep.StandardScaler().fit(X_train)70 ????X_train=preprocessor.transform(X_train)71 ????X_test=preprocessor.transform(X_test)72 ????return X_train,X_test73 74 def get_random_block_from_data(data,batch_size):75 ????start_index=np.random.randint(0,len(data)-batch_size)76 ????return data[start_index:(start_index+batch_size)]77 78 X_train,X_test=standard_scale(mnist.train.images,mnist.test.images)79 n_samples=int(mnist.train.num_examples)80 training_epochs=2081 batch_size=12882 diaplay_step=183 autoencoder=AdditiveGaussianNoiseAutoencoder(n_input=784,n_hidden=200,transfer_function=tf.nn.softplus,optimizer=tf.train.AdamOptimizer(learning_rate=0.001),scale=0.01)84 for epoch in range(training_epochs):85 ????avg_cost=086 ????total_batch=int(n_samples/batch_size)87 ????for i in range(total_batch):88 ????????batch_xs=get_random_block_from_data(X_train,batch_size)89 90 ????????cost=autoencoder.partial_fit(batch_xs)91 ????????avg_cost+=cost/n_samples*batch_size92 93 ????if epoch%diaplay_step==0:94 ????????print("Epoch:",‘%04d‘%(epoch+1),"cost=","{:.9f}".format(avg_cost))95 96 print("Total cost: "+str(autoencoder.calc_total_cost(X_test)))