写一份卷积收缩自动编码器的代码

好的，以下是使用 TensorFlow 实现卷积收缩自动编码器的代码：

import tensorflow as tf

# 定义卷积收缩自动编码器的类
class ConvolutionalContractiveAutoencoder(object):
    
    def __init__(self, input_shape, code_size, alpha=1e-4):
        # 定义模型的输入输出和编码器参数
        self.input_shape = input_shape
        self.code_size = code_size
        self.alpha = alpha
        self.x = tf.placeholder(dtype=tf.float32, shape=[None, *input_shape], name='input')
        self.encoded = None
        self.decoded = None
        self.loss = None
        self.train_op = None
        
        # 构建模型
        self._build_model()
    
    def _build_model(self):
        # 定义编码器
        with tf.variable_scope('encoder'):
            x = self.x
            x = tf.layers.conv2d(x, filters=32, kernel_size=(3, 3), strides=(2, 2), activation=tf.nn.relu, padding='same')
            x = tf.layers.conv2d(x, filters=64, kernel_size=(3, 3), strides=(2, 2), activation=tf.nn.relu, padding='same')
            x = tf.layers.flatten(x)
            self.encoded = tf.layers.dense(x, units=self.code_size, activation=tf.nn.relu)
        
        # 定义解码器
        with tf.variable_scope('decoder'):
            x = self.encoded
            x = tf.layers.dense(x, units=(self.input_shape[0]//4)*(self.input_shape[1]//4)*64, activation=tf.nn.relu)
            x = tf.reshape(x, [-1, self.input_shape[0]//4, self.input_shape[1]//4, 64])
            x = tf.layers.conv2d_transpose(x, filters=32, kernel_size=(3, 3), strides=(2, 2), activation=tf.nn.relu, padding='same')
            self.decoded = tf.layers.conv2d_transpose(x, filters=self.input_shape[2], kernel_size=(3, 3), strides=(2, 2), activation=None, padding='same')
        
        # 定义损失函数
        with tf.variable_scope('loss'):
            self.loss = tf.reduce_mean(tf.square(self.x - self.decoded))
            jacobian = tf.reshape(self.encoded, [-1, self.code_size, 1])
            jacobian = tf.matmul(jacobian, jacobian, transpose_b=True)
            jacobian = tf.reshape(jacobian, [-1, self.code_size*self.code_size])
            self.loss += self.alpha * tf.reduce_mean(tf.reduce_sum(tf.square(jacobian), axis=1))
            
        # 定义优化器
        with tf.variable_scope('optimizer'):
            self.train_op = tf.train.AdamOptimizer().minimize(self.loss)

这个代码实现了一个基于卷积神经网络的收缩自动编码器，其中包括编码器、解码器、损失函数和优化器。在构建模型时，我们使用了 TensorFlow 中的 `tf.layers` 模块来定义卷积和全连接层，使用了 `tf.reduce_mean` 来计算损失函数，使用了 `tf.train.AdamOptimizer` 来优化模型。