<reponame>a25765564/MLtest<gh_stars>0
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 AdditivaGaussianNoiseAutoencoder(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),2.0))
		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 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 getBisses(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_tain,X_test = standard_scale(mnist.train.images,mnist.test.images)

n_samples = int(mnist.train.num_examples)
training_epochs = 200
batch_size = 128
display_step = 1

autoencoder = AdditivaGaussianNoiseAutoencoder(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_tain,batch_size)
		cost = autoencoder.partial_fit(batch_xs)
		avg_cost += cost /n_samples * batch_size
	if epoch % display_step == 0:
		print("Epoch:",'%04d'%(epoch+1),"cost=","{:.9f}".format(avg_cost))
print ("total cost:" + str(autoencoder.calc_total_cost(X_test)))

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 AdditivaGaussianNoiseAutoencoder(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),2.0))
		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 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 getBisses(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_tain,X_test = standard_scale(mnist.train.images,mnist.test.images)

n_samples = int(mnist.train.num_examples)
training_epochs = 200
batch_size = 128
display_step = 1

autoencoder = AdditivaGaussianNoiseAutoencoder(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_tain,batch_size)
		cost = autoencoder.partial_fit(batch_xs)
		avg_cost += cost /n_samples * batch_size
	if epoch % display_step == 0:
		print("Epoch:",'%04d'%(epoch+1),"cost=","{:.9f}".format(avg_cost))
print ("total cost:" + str(autoencoder.calc_total_cost(X_test)))