#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
@date 2018/08/09 20:08:45
"""
import sys
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):
"""
Briefs:
xavier_init
"""
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):
"""
Briefs:自编码器
"""
def __init__(self, n_input, n_hidden, transfer_function=tf.nn.softplus,optimizer=tf.train.AdamOptimizer(), scale=0.1):
"""
Briefs:
init
"""
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):
"""
Briefs:
_initialize weights
"""
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):
"""
Briefs:
partial fit
"""
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):
"""
Briefs:
calc total cost
"""
return self.sess.run(self.cost, feed_dict = {self.x: X, self.scale: self.training_scale})
def transform(self, X):
"""
Briefs:
transform
"""
return self.sess.run(self.hidden, feed_dict = {self.x: X, self.scale: self.training_scale})
def generate(self, hidden=None):
"""
Briefs:
generate
"""
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):
"""
Briefs:
reconstruction
"""
return self.sess.run(self.reconstruction, feed_dict = {self.x: X, self.scale: \
self.training_scale})
def getWeights(self):
"""
Briefs:
get weigths
"""
return self.sess.run(self.weights['w1'])
def getBiases(self):
"""
Briefs:
get biases
"""
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):
"""
Briefs:
standard scale
标准化处理：先减去均值再除以标准差
"""
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):
"""
Briefs:
get random block from data
"""
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
display_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 % display_step== 0:
print("Epoch:", "%04d" % (epoch + 1), "cost=", "{:.9f}".format(avg_cost))
print("Total cost: " + str(autoencoder.calc_total_cost(X_test)))
if __name__ == '__main__':
pass
