Tesorflow-自动编码器(AutoEncoder)

直接附上代码：

 import numpy as np
 import sklearn.preprocessing as prep
 import tensorflow as tf
 from tensorflow.examples.tutorials.mnist import input_data

 def 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.sub(self.reconstruction,self.x),2.0))
         self.optimizer=optimizer.minimize(self.cost)

         init=tf.initialize_all_variables()
         self.sess=tf.Session()
         self.sess.run(init)

     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 is None:
             hidden=np.random.normal(size=self.weights["b1"])
         return self.sess.run(self.reconstruction,feed_dict={self.hidden:hidden})

     def reconstruct(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_test

 def 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=20
 batch_size=128
 diaplay_step=1
 autoencoder=AdditiveGaussianNoiseAutoencoder(n_input=784,n_hidden=200,transfer_function=tf.nn.softplus,optimizer=tf.train.AdamOptimizer(learning_rate=0.001),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%diaplay_step==0:
         print("Epoch:",'%04d'%(epoch+1),"cost=","{:.9f}".format(avg_cost))

 print("Total cost: "+str(autoencoder.calc_total_cost(X_test)))