一维卷积降噪自编码器代码

以下是一个简单的一维卷积降噪自编码器的代码示例：

import tensorflow as tf

# 定义模型
class Conv1DAutoencoder(tf.keras.Model):
    def __init__(self, filters, kernel_size, pool_size):
        super(Conv1DAutoencoder, self).__init__()
        self.encoder = tf.keras.Sequential([
            tf.keras.layers.Conv1D(filters, kernel_size, activation='relu', padding='same'),
            tf.keras.layers.MaxPooling1D(pool_size, padding='same')
        ])
        self.decoder = tf.keras.Sequential([
            tf.keras.layers.Conv1D(filters, kernel_size, activation='relu', padding='same'),
            tf.keras.layers.UpSampling1D(pool_size)
        ])

    def call(self, x):
        encoded = self.encoder(x)
        decoded = self.decoder(encoded)
        return decoded

# 定义损失函数
def loss(model, x):
    reconstructed = model(x)
    mse = tf.keras.losses.MeanSquaredError()
    return mse(x, reconstructed)

# 定义优化器
def train(model, train_dataset, epochs):
    optimizer = tf.keras.optimizers.Adam()
    for epoch in range(epochs):
        for step, x in enumerate(train_dataset):
            with tf.GradientTape() as tape:
                cost = loss(model, x)
            grads = tape.gradient(cost, model.trainable_variables)
            optimizer.apply_gradients(zip(grads, model.trainable_variables))
            if (step + 1) % 10 == 0:
                print('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'.format(epoch + 1, epochs, step + 1, len(train_dataset), cost.numpy()))

# 测试模型
def test(model, test_dataset):
    for x in test_dataset:
        reconstructed = model(x)
        print('Original:', x.numpy())
        print('Reconstructed:', reconstructed.numpy())
        break

# 加载数据集
(x_train, _), (x_test, _) = tf.keras.datasets.mnist.load_data()
x_train = x_train.astype('float32') / 255.
x_test = x_test.astype('float32') / 255.
x_train = tf.expand_dims(x_train, axis=-1)
x_test = tf.expand_dims(x_test, axis=-1)

# 构建和训练模型
model = Conv1DAutoencoder(filters=32, kernel_size=3, pool_size=2)
train_dataset = tf.data.Dataset.from_tensor_slices(x_train).batch(32)
train(model, train_dataset, epochs=10)

# 测试模型
test_dataset = tf.data.Dataset.from_tensor_slices(x_test).batch(1)
test(model, test_dataset)

这个模型使用了一个卷积层和一个最大池化层来编码输入数据，然后使用一个卷积层和一个上采样层来解码编码后的数据。损失函数使用均方误差，优化器使用Adam。在训练过程中，模型会在每个epoch结束时打印出损失值。在测试过程中，模型会将测试集中的第一个样本重构并显示原始图像和重构图像。