Tensorflow实现AGN自编码器

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#coding: utf-8
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)  # 定义成一个placeholder
        self.training_scale = scale
        network_weights = self._initialize_weights()
        self.weights = network_weights

        # 定义模型，也就是输入层，隐含层，输出层以及之间的映射矩阵
        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'])

        # 定义损失函数，这里我们使用平方差，因为下面的激活函数选择的是恒等
        self.cost = 0.5 * tf.reduce_sum(tf.pow(tf.subtract(self.reconstruction, self.x), 2.0))
        self.optimizer = optimizer.minimize(self.cost)  # 优化器为求损失极小化

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

    def _initialize_weights(self):
        # 字典类型
        all_weights = dict()
        # 输入层到隐含层矩阵
        all_weights['w1'] = tf.Variable(xavier_init(self.n_input, self.n_hidden))
        # 输入层到隐含层偏置向量
        all_weights['b1'] = tf.Variable(tf.zeros([self.n_hidden], dtype=tf.float32))
        # 隐含层到输出层矩阵，可以看出是w1的逆
        all_weights['w2'] = tf.Variable(tf.zeros([self.n_hidden, self.n_input], dtype=tf.float32))
        # 隐含层到输出层偏置向量
        all_weights['b2'] = tf.Variable(tf.zeros([self.n_input], dtype=tf.float32))
        return all_weights

    def partial_fit(self, X):  # 执行损失＋执行一步训练
        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):  # 只执行损失
        return self.sess.run(self.cost, feed_dict = {self.x: X, self.scale: self.training_scale})

    def transform(self,X):  # 为了看隐含层输出结果
        return self.sess.run(self.hidden, feed_dict = {self.x: X, self.scale: self.training_scale})

    def generate(self, hidden = None):  # 将隐含层输出的高阶特征还原为原始数据
        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):  # transform + generator
        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):  # 标准化 均值０　标准差１
    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):  # 随机取数
    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 #每轮epoch显示一次cost

#创建一个AGN自编码器的实例
autoencoder = AdditiveGaussianNoiseAutoencoder(n_input = 784,
                                               n_hidden = 200,
                                               transfer_function = tf.nn.softplus,
                                               optimizer = tf.train.AdamOptimizer(learning_rate = 0.001),
                                               scale = 0.01)

#训练
for epoch in range(training_epochs):
    avg_cost = 0.
    total_batch = int(n_samples / batch_size)
    # 循环载入训练数据
    for i in range(total_batch):
        batch_xs = get_random_block_from_data(X_train, batch_size)

        # 使用batch喂入训练数据
        cost = autoencoder.partial_fit(batch_xs)
        # Compute average loss
        avg_cost += cost / n_samples * batch_size

        # 展示损失
    if epoch % display_step == 0:
        print("Epoch:", '%04d' % (epoch + 1), "cost=", "{:.9f}".format(avg_cost))
#训练完的模型性能测试　平方误差
print("Total cost: " + str(autoencoder.calc_total_cost(X_test)))