写带有一维收缩残差去噪效果的卷积自编码器代码

下面是一个使用一维收缩残差去噪的卷积自编码器的示例代码：

import tensorflow as tf
from tensorflow.keras.layers import Conv1D, Conv1DTranspose, Input, Reshape

# 定义卷积自编码器
def autoencoder(input_shape):
    # 编码器
    inputs = Input(shape=input_shape)
    encoded = Conv1D(32, 3, activation='relu', padding='same')(inputs)
    encoded = Conv1D(16, 3, activation='relu', padding='same')(encoded)

    # 解码器
    decoded = Conv1DTranspose(16, 3, activation='relu', padding='same')(encoded)
    decoded = Conv1DTranspose(32, 3, activation='relu', padding='same')(decoded)
    decoded = Conv1D(1, 3, activation='sigmoid', padding='same')(decoded)

    # 构建自编码器模型
    autoencoder = tf.keras.Model(inputs, decoded)
    return autoencoder

# 定义残差连接
def residual_block(x, filters, kernel_size):
    residual = x
    x = Conv1D(filters, kernel_size, activation='relu', padding='same')(x)
    x = Conv1D(filters, kernel_size, activation=None, padding='same')(x)
    x = tf.keras.layers.add([x, residual])
    x = tf.keras.layers.Activation('relu')(x)
    return x

# 定义带有一维收缩残差的卷积自编码器
def residual_autoencoder(input_shape):
    # 编码器
    inputs = Input(shape=input_shape)
    encoded = Conv1D(32, 3, activation='relu', padding='same')(inputs)
    encoded = residual_block(encoded, 16, 3)

    # 解码器
    decoded = Conv1DTranspose(16, 3, activation='relu', padding='same')(encoded)
    decoded = residual_block(decoded, 32, 3)
    decoded = Conv1D(1, 3, activation='sigmoid', padding='same')(decoded)

    # 构建自编码器模型
    autoencoder = tf.keras.Model(inputs, decoded)
    return autoencoder

# 创建卷积自编码器模型
input_shape = (256, 1) # 输入形状
model = residual_autoencoder(input_shape)
model.summary()

上述代码中，我们定义了一个带有一维收缩残差的卷积自编码器模型。编码器部分由两个卷积层组成，解码器部分也是两个卷积层。在编码器中使用了一维残差连接来增加网络的深度和学习能力。最后，通过构建自编码器模型，并打印模型摘要，可以查看模型的结构和参数数量。请根据你的需求进行适当的修改和调整。