TensoFlow官方教程代码（2）

# TensorFlow实现自编码器
# 以MNIST数据集为例

#导入模块
import numpy as np
import sklearn.preprocessing as prep
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data

# 定义xavier初始化器
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)

# 定义自编码器类
class AdditiveGaussionNoiseAutoencoder(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
        self.weights = self._initialize_weights()

        # 构建网络结构
        self.x = tf.placeholder(tf.float32, [None, self.n_input])
        self.hidden = self.transfer(tf.add(tf.matmul(self.x + self.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)

        # 初始化+构建会话
        self.sess = tf.Session()
        self.sess.run(tf.global_variables_initializer())

    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"])

# 定义数据标准化处理函数

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 + 0:batch_size]

# 载入数据
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
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

# 实例化一个自编码器
autoencoder = AdditiveGaussionNoiseAutoencoder(n_input=784, n_hidden=200, 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)))

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