AutoEncoder.py

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(shape=(fan_in,fan_out),minval=low,maxval=high,dtype=tf.float32)

class Autoencoder(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.tranfer = 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.tranfer(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),2.0))
        self.optimizer = optimizer.minimize(self.cost)
        init = tf.global_variables_initializer()
        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 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 getWeight(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 standar_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 = standar_scale(mnist.train.images,mnist.test.images)

n_samples = int(mnist.train.num_examples)
train_epochs = 20
batch_size = 128
display_step = 1

autoencoder = Autoencoder(n_input=784,n_hidden=200,transfer_function=tf.nn.softplus,
                          optimizer=tf.train.AdamOptimizer(learning_rate=0.001),scale=0.1)

for epoch in range(train_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_size)
        avg_cost += cost/n_samples*batch_size

    if epoch % display_step == 0:
        print ("Epoch:",'%04d'%(epoch+1),"cost=","{:.9f".format(avg_cost))