用python写一维卷积降噪自编码器

好的，下面是一维卷积降噪自编码器的Python实现代码：

import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf

# 加载数据集
from tensorflow.keras.datasets import mnist
(x_train, _), (x_test, _) = mnist.load_data()

# 数据预处理
x_train = x_train.astype('float32') / 255.
x_test = x_test.astype('float32') / 255.
x_train = np.reshape(x_train, (len(x_train), 28, 28, 1)) 
x_test = np.reshape(x_test, (len(x_test), 28, 28, 1)) 

# 添加高斯噪声
noise_factor = 0.5
x_train_noisy = x_train + noise_factor * np.random.normal(loc=0.0, scale=1.0, size=x_train.shape) 
x_test_noisy = x_test + noise_factor * np.random.normal(loc=0.0, scale=1.0, size=x_test.shape) 
x_train_noisy = np.clip(x_train_noisy, 0., 1.) 
x_test_noisy = np.clip(x_test_noisy, 0., 1.) 

# 定义模型
input_img = tf.keras.layers.Input(shape=(28, 28, 1)) 
x = tf.keras.layers.Conv2D(32, (3, 3), activation='relu', padding='same')(input_img) 
x = tf.keras.layers.MaxPooling2D((2, 2), padding='same')(x) 
x = tf.keras.layers.Conv2D(32, (3, 3), activation='relu', padding='same')(x) 
encoded = tf.keras.layers.MaxPooling2D((2, 2), padding='same')(x) 
x = tf.keras.layers.Conv2D(32, (3, 3), activation='relu', padding='same')(encoded) 
x = tf.keras.layers.UpSampling2D((2, 2))(x) 
x = tf.keras.layers.Conv2D(32, (3, 3), activation='relu', padding='same')(x) 
x = tf.keras.layers.UpSampling2D((2, 2))(x) 
decoded = tf.keras.layers.Conv2D(1, (3, 3), activation='sigmoid', padding='same')(x) 

autoencoder = tf.keras.models.Model(input_img, decoded) 
autoencoder.compile(optimizer='adam', loss='binary_crossentropy') 

# 训练模型
autoencoder.fit(x_train_noisy, x_train, epochs=10, batch_size=128, shuffle=True, validation_data=(x_test_noisy, x_test))

# 显示结果
decoded_imgs = autoencoder.predict(x_test_noisy) 

n = 10 
plt.figure(figsize=(20, 4)) 
for i in range(n): 
    # 原始图像
    ax = plt.subplot(2, n, i + 1) 
    plt.imshow(x_test_noisy[i].reshape(28, 28)) 
    plt.gray() 
    ax.get_xaxis().set_visible(False) 
    ax.get_yaxis().set_visible(False) 

    # 降噪后的图像
    ax = plt.subplot(2, n, i + n + 1) 
    plt.imshow(decoded_imgs[i].reshape(28, 28)) 
    plt.gray() 
    ax.get_xaxis().set_visible(False) 
    ax.get_yaxis().set_visible(False) 
plt.show()

代码中使用了MNIST手写数字数据集，首先对数据进行预处理，然后在数据上添加高斯噪声。然后定义了一个卷积神经网络自编码器模型，包括编码器和解码器两个部分。编码器部分包括两个卷积层和两个最大池化层，解码器部分包括两个卷积层和两个上采样层。最后，使用Adam优化器和二进制交叉熵损失函数编译并训练模型。最后，显示降噪前后的图像。