降噪自编码器python 代码

降噪自编码器（Denoising Autoencoder）是一种无监督学习算法，用于学习输入数据的低维表示。它通过在输入数据中引入噪声，并尝试从噪声数据中重构原始数据来训练模型。以下是一个简单的降噪自编码器的Python代码示例：

import numpy as np
import tensorflow as tf

# 定义降噪自编码器类
class DenoisingAutoencoder:
    def __init__(self, input_dim, hidden_dim):
        self.input_dim = input_dim
        self.hidden_dim = hidden_dim
        
        # 定义模型的输入和输出占位符
        self.x = tf.placeholder(tf.float32, [None, input_dim])
        self.y = tf.placeholder(tf.float32, [None, input_dim])
        
        # 定义编码器和解码器的权重和偏置变量
        self.weights = {
            'encoder': tf.Variable(tf.random_normal([input_dim, hidden_dim])),
            'decoder': tf.Variable(tf.random_normal([hidden_dim, input_dim]))
        }
        self.biases = {
            'encoder': tf.Variable(tf.random_normal([hidden_dim])),
            'decoder': tf.Variable(tf.random_normal([input_dim]))
        }
        
    def encoder(self, x):
        # 编码器的前向传播过程
        encoded = tf.nn.sigmoid(tf.add(tf.matmul(x, self.weights['encoder']), self.biases['encoder']))
        return encoded
    
    def decoder(self, x):
        # 解码器的前向传播过程
        decoded = tf.nn.sigmoid(tf.add(tf.matmul(x, self.weights['decoder']), self.biases['decoder']))
        return decoded
    
    def train(self, X_train, X_noise, learning_rate, batch_size, num_epochs):
        # 构建模型
        encoded = self.encoder(self.x)
        decoded = self.decoder(encoded)
        
        # 定义损失函数和优化器
        loss = tf.reduce_mean(tf.square(self.y - decoded))
        optimizer = tf.train.AdamOptimizer(learning_rate).minimize(loss)
        
        # 创建会话并初始化变量
        with tf.Session() as sess:
            sess.run(tf.global_variables_initializer())
            
            # 迭代训练
            for epoch in range(num_epochs):
                avg_loss = 0.0
                total_batch = int(X_train.shape[0] / batch_size)
                
                # 遍历所有的批次数据
                for i in range(total_batch):
                    batch_x = X_train[i * batch_size: (i + 1) * batch_size]
                    batch_noise = X_noise[i * batch_size: (i + 1) * batch_size]
                    
                    # 运行优化器和损失函数
                    _, l = sess.run([optimizer, loss], feed_dict={self.x: batch_noise, self.y: batch_x})
                    
                    avg_loss += l / total_batch
                
                # 每个epoch打印损失
                print("Epoch:", epoch+1, "loss =", "{:.3f}".format(avg_loss))
            
            # 返回训练好的模型参数
            trained_weights, trained_biases = sess.run([self.weights, self.biases])
        
        return trained_weights, trained_biases

# 示例用法
# 假设输入数据是一个二维向量，隐藏层维度为1
input_dim = 2
hidden_dim = 1

# 创建降噪自编码器对象
autoencoder = DenoisingAutoencoder(input_dim, hidden_dim)

# 生成训练数据和噪声数据
X_train = np.array([[1, 2], [3, 4], [5, 6], [7, 8]])
X_noise = X_train + np.random.normal(0, 1, X_train.shape)

# 训练模型
trained_weights, trained_biases = autoencoder.train(X_train, X_noise, learning_rate=0.01, batch_size=2, num_epochs=10)

这是一个简单的降噪自编码器的实现示例，你可以根据自己的需求进行修改和扩展。希望对你有帮助！