生成堆叠降噪自编码器的python代码

以下是一个简单的堆叠降噪自编码器的Python代码示例：

import numpy as np
import tensorflow as tf

# 定义堆叠降噪自编码器类
class StackedDenoisingAutoencoder():
    def __init__(self, input_size, hidden_sizes):
        self.input_size = input_size
        self.hidden_sizes = hidden_sizes
        self.autoencoders = []

        # 创建所有自编码器
        for i in range(len(hidden_sizes)):
            if i == 0:
                input_shape = input_size
            else:
                input_shape = hidden_sizes[i-1]

            autoencoder = Autoencoder(input_shape, hidden_sizes[i])
            self.autoencoders.append(autoencoder)

    # 训练所有自编码器
    def train(self, X, noise_factor=0.2, learning_rate=0.1, batch_size=128, num_epochs=100):
        for i in range(len(self.autoencoders)):
            print("Training autoencoder #{}...".format(i+1))

            # 添加噪音
            X_noisy = X + noise_factor * np.random.randn(*X.shape)

            # 训练自编码器
            self.autoencoders[i].train(X_noisy, learning_rate, batch_size, num_epochs)

            # 获取编码器输出（隐藏层）
            encoder_output = self.autoencoders[i].get_encoder_output(X)

            # 更新输入数据
            X = encoder_output

    # 获取编码器输出
    def get_encoder_output(self, X):
        for i in range(len(self.autoencoders)):
            encoder_output = self.autoencoders[i].get_encoder_output(X)
            X = encoder_output
        return encoder_output

# 定义自编码器类
class Autoencoder():
    def __init__(self, input_size, hidden_size):
        self.input_size = input_size
        self.hidden_size = hidden_size
        self.weights = {}
        self.biases = {}

        # 初始化权重和偏差
        self.weights['encoder'] = tf.Variable(tf.random_normal([input_size, hidden_size]))
        self.biases['encoder'] = tf.Variable(tf.random_normal([hidden_size]))
        self.weights['decoder'] = tf.Variable(tf.random_normal([hidden_size, input_size]))
        self.biases['decoder'] = tf.Variable(tf.random_normal([input_size]))

        # 定义输入和输出占位符
        self.input = tf.placeholder(tf.float32, [None, input_size])
        self.encoded = tf.matmul(self.input + 0.3 * tf.random_normal(tf.shape(self.input)), self.weights['encoder']) + self.biases['encoder']
        self.decoded = tf.matmul(self.encoded, self.weights['decoder']) + self.biases['decoder']

        # 定义损失函数和优化器
        self.loss = tf.reduce_mean(tf.square(self.input - self.decoded))
        self.optimizer = tf.train.AdamOptimizer().minimize(self.loss)

        # 初始化Tensorflow变量
        init = tf.global_variables_initializer()
        self.sess = tf.Session()
        self.sess.run(init)

    # 训练自编码器
    def train(self, X, learning_rate, batch_size, num_epochs):
        num_batches = X.shape[0] // batch_size
        for epoch in range(num_epochs):
            for i in range(num_batches):
                batch = X[i*batch_size:(i+1)*batch_size]
                self.sess.run(self.optimizer, feed_dict={self.input: batch})

    # 获取编码器输出（隐藏层）
    def get_encoder_output(self, X):
        return self.sess.run(self.encoded, feed_dict={self.input: X})

这是一个简单的实现，仅供参考。您可以根据自己的需求进行修改和优化。