【自编码器】降噪自编码器实现

注意：代码源自[1][2]

# 这里以最具代表性的去噪自编码器为例。
# 导入MNIST数据集
import numpy as np
import sklearn.preprocessing as prep
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
# 这里使用一种参数初始化方法xavier initialization，需要对此做好定义工作。
# Xaiver初始化器的作用就是让权重大小正好合适。
# 这里实现的是标准均匀分布的Xaiver初始化器。
def xavier_init(fan_in, fan_out, constant=1):
    """
    目的是合理初始化权重。
    参数：
    fan_in --行数；
    fan_out -- 列数；
    constant --常数权重，条件初始化范围的倍数。
    return 初始化后的权重tensor.
    """
    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)
# 定义一个去噪的自编码类
class AdditiveGaussianNoiseAutoencoder(object):
    """
    __init__() :构建函数；
    n_input : 输入变量数；
    n_hidden : 隐含层节点数；
    transfer_function: 隐含层激活函数，默认是softplus；
    optimizer : 优化器，默认是Adam;
    scale : 高斯噪声系数，默认是0.1；
    """
    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
        # 定义网络结构，为输入x创建一个维度为n_input的placeholder，然后
        # 建立一个能提取特征的隐含层。
        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'])
        # 首先，定义自编码器的损失函数，在此直接使用平方误差(SquaredError)作为cost。
        # 然后，定义训练操作作为优化器self.optimizer对损失self.cost进行优化。
        # 最后，创建Session，并初始化自编码器全部模型参数。
        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})
# 定义一个transform函数，以便返回自编码器隐含层的输出结果，目的是提供一个接口来获取抽象后的特征。
    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):  # 获取隐含层的权重w1.
        return self.sess.run(self.weights['w1'])

    def getBiases(self):  # 获取隐含层的偏执系数b1.
        return self.sess.run(self.weights['b1'])

# 利用TensorFlow提供的读取示例数据的函数载入MNIST数据集。

mnist = input_data.read_data_sets('MNIST_data', one_hot=True)


# 定义一个对训练、测试数据进行标准化处理的函数。

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:(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)
# Loop over all batches
for i in range(total_batch):
    batch_xs = get_random_block_from_data(X_train, batch_size)
# Fit training using batch data
cost = autoencoder.partial_fit(batch_xs)
# Compute average loss
avg_cost += cost / n_samples * batch_size
# Display logs per epoch step
if epoch % display_step == 0:
    print("Epoch:", '%04d' % (epoch + 1), "cost=", "{:.9f}".format(avg_cost))
# 最后对训练完的模型进行性能测试。
print("Total cost: " + str(autoencoder.calc_total_cost(X_test)))

[1] 黄文坚.TensorFlow实战.北京：电子工业出版社

[2] https://blog.csdn.net/qq_37608890/article/details/79352212