Denoising Autoencoder

去噪自动编码器（Denoising Autoencoder）是一种特殊的自动编码器，主要用于去除输入数据中的噪声。在图像、语音、文本等信号处理领域，噪声是很常见的问题。去噪自动编码器的主要目标是通过学习信号的特征，然后利用这些特征去除噪声。
以下是使用去噪自动编码器的一般步骤：

1. 准备数据：首先，需要收集包含噪声的原始数据。对于图像数据，可以使用公开数据集或专业图像库。
2. 构建编码器：根据输入数据的维度和类型，选择适当的神经网络结构作为编码器。常见的编码器结构包括卷积神经网络（CNN）和循环神经网络（RNN）。
3. 构建解码器：根据原始数据的维度和编码器的输出尺寸，选择适当的神经网络结构作为解码器。解码器的任务是将编码器生成的隐向量还原回原始数据空间。
4. 训练自动编码器：将编码器和解码器连接在一起，形成一个端到端的神经网络。使用无监督学习方法（如随机梯度下降法或变分自编码器）训练该网络，使其在重建输入数据时达到最小损失。在训练过程中，可以将原始数据和包含噪声的数据同时输入自动编码器，以便网络学习到去噪任务。
5. 应用自动编码器：训练好的去噪自动编码器可以用于去除原始数据中的噪声。将噪声数据输入自动编码器，输出隐向量，然后使用解码器将隐向量还原回原始数据空间，从而实现去噪。
   总之，去噪自动编码器是一种有效的去除噪声的方法。通过训练编码器和解码器，自动编码器可以学习到输入数据的主要特征，并将这些特征用于去除噪声。在图像、语音、文本等信号处理领域，去噪自动编码器具有广泛的应用前景。

Autoencoder
All we'll need is TensorFlow and NumPy:

import tensorflow as tf
import numpy as np
Instead of feeding all the training data to the training op, we will feed data in small batches:

def get_batch(X, size):
    a = np.random.choice(len(X), size, replace=False)
    return X[a]
Define the autoencoder class:

class Autoencoder:
    def __init__(self, input_dim, hidden_dim, epoch=500, batch_size=10, learning_rate=0.001):
        self.epoch = epoch
        self.batch_size = batch_size
        self.learning_rate = learning_rate

        # Define input placeholder
        x = tf.placeholder(dtype=tf.float32, shape=[None, input_dim])

        # Define variables
        with tf.name_scope('encode'):
            weights = tf.Variable(tf.random_normal([input_dim, hidden_dim], dtype=tf.float32), name='weights')
            biases = tf.Variable(tf.zeros([hidden_dim]), name='biases')
            encoded = tf.nn.sigmoid(tf.matmul(x, weights) + biases)
        with tf.name_scope('decode'):
            weights = tf.Variable(tf.random_normal([hidden_dim, input_dim], dtype=tf.float32), name='weights')
            biases = tf.Variable(tf.zeros([input_dim]), name='biases')
            decoded = tf.matmul(encoded, weights) + biases

        self.x = x
        self.encoded = encoded
        self.decoded = decoded

        # Define cost function and training op
        self.loss = tf.sqrt(tf.reduce_mean(tf.square(tf.subtract(self.x, self.decoded))))

        self.all_loss = tf.sqrt(tf.reduce_mean(tf.square(tf.subtract(self.x, self.decoded)), 1))
        self.train_op = tf.train.AdamOptimizer(self.learning_rate).minimize(self.loss)

        # Define a saver op
        self.saver = tf.train.Saver()

    def train(self, data):
        with tf.Session() as sess:
            sess.run(tf.global_variables_initializer())
            for i in range(self.epoch):
                for j in range(500):
                    batch_data = get_batch(data, self.batch_size)
                    l, _ = sess.run([self.loss, self.train_op], feed_dict={self.x: batch_data})
                if i % 50 == 0:
                    print('epoch {0}: loss = {1}'.format(i, l))
                    self.saver.save(sess, './model.ckpt')
            self.saver.save(sess, './model.ckpt')

    def test(self, data):
        with tf.Session() as sess:
            self.saver.restore(sess, './model.ckpt')
            hidden, reconstructed = sess.run([self.encoded, self.decoded], feed_dict={self.x: data})
        print('input', data)
        print('compressed', hidden)
        print('reconstructed', reconstructed)
        return reconstructed

    def get_params(self):
        with tf.Session() as sess:
            self.saver.restore(sess, './model.ckpt')
            weights, biases = sess.run([self.weights1, self.biases1])
        return weights, biases

    def classify(self, data, labels):
        with tf.Session() as sess:
            sess.run(tf.global_variables_initializer())
            self.saver.restore(sess, './model.ckpt')
            hidden, reconstructed = sess.run([self.encoded, self.decoded], feed_dict={self.x: data})
            reconstructed = reconstructed[0]
            # loss = sess.run(self.all_loss, feed_dict={self.x: data})
            print('data', np.shape(data))
            print('reconstructed', np.shape(reconstructed))
            loss = np.sqrt(np.mean(np.square(data - reconstructed), axis=1))
            print('loss', np.shape(loss))
            horse_indices = np.where(labels == 7)[0]
            not_horse_indices = np.where(labels != 7)[0]
            horse_loss = np.mean(loss[horse_indices])
            not_horse_loss = np.mean(loss[not_horse_indices])
            print('horse', horse_loss)
            print('not horse', not_horse_loss)
            return hidden[7,:]

    def decode(self, encoding):
        with tf.Session() as sess:
            sess.run(tf.global_variables_initializer())
            self.saver.restore(sess, './model.ckpt')
            reconstructed = sess.run(self.decoded, feed_dict={self.encoded: encoding})
        img = np.reshape(reconstructed, (32, 32))
        return img
The Iris dataset is often used as a simple training dataset to check whether a classification algorithm is working. The sklearn library comes with it, pip install sklearn.

from sklearn import datasets

hidden_dim = 1
data = datasets.load_iris().data
input_dim = len(data[0])
ae = Autoencoder(input_dim, hidden_dim)
ae.train(data)
ae.test([[8, 4, 6, 2]])
epoch 0: loss = 3.8637373447418213
epoch 50: loss = 0.25829368829727173
epoch 100: loss = 0.3230888843536377
epoch 150: loss = 0.3295430839061737
epoch 200: loss = 0.24636892974376678
epoch 250: loss = 0.22375555336475372
epoch 300: loss = 0.19688692688941956
epoch 350: loss = 0.2520211935043335
epoch 400: loss = 0.29669439792633057
epoch 450: loss = 0.2794385552406311
input [[8, 4, 6, 2]]
compressed [[ 0.72223264]]
reconstructed [[ 6.87640762  2.79334426  6.23228502  2.21386957]]
array([[ 6.87640762,  2.79334426,  6.23228502,  2.21386957]], dtype=float32)