我刚刚构建了一个卷积自动编码器来尝试重建一个带有 shape 的时间序列(4000, 10, 30)。这是代码，我使用了 32 的批量大小，但我认为它过度拟合，因为它在训练集上表现良好，但在验证和测试集中开始重建效果不佳。我可以采取哪些步骤来改进它？如何为每一层定义正确数量的过滤器？

kernel_size = 7
stride = 1
model = Sequential()
model.add(Conv1D(filters=128, kernel_size=kernel_size, activation='relu', padding="same", strides=stride,
                 input_shape=(TIME_STEPS, n_features)))
model.add(Conv1D(filters=64, kernel_size=kernel_size, activation='relu', padding="same", strides=stride))
model.add(Conv1D(filters=32, kernel_size=kernel_size, activation='relu', padding="same", strides=stride))
model.add(MaxPooling1D(pool_size=2))
model.add(Conv1D(filters=16, kernel_size=kernel_size, activation='relu', padding="same", strides=stride))
model.add(Conv1DTranspose(filters=16, kernel_size=kernel_size, activation='relu', padding="same", strides=stride))
model.add(UpSampling1D(size=2))
model.add(Conv1DTranspose(filters=32, kernel_size=kernel_size, activation='relu', padding="same", strides=stride))
model.add(Conv1DTranspose(filters=64, kernel_size=kernel_size, activation='relu', padding="same", strides=stride))
model.add(Conv1DTranspose(filters=128, kernel_size=kernel_size, activation='relu', padding="same", strides=stride))
model.add(Conv1DTranspose(filters=n_features, kernel_size=kernel_size, padding="same"))

这是优化器：

optimizer = tf.keras.optimizers.Adam(learning_rate=1e-04)
model.compile(optimizer=optimizer, loss="mse",
              metrics=[tf.metrics.MeanAbsoluteError(), 'accuracy'])

编辑：辍学版本：

model = Sequential()
model.add(Conv1D(filters=20, kernel_size=kernel_size, activation='relu', padding="same", strides=stride,
                 input_shape=(TIME_STEPS, n_features)))
model.add(Dropout(0.2))
model.add(Conv1D(filters=15, kernel_size=kernel_size, activation='relu', padding="same", strides=stride))
model.add(MaxPooling1D(pool_size=2))
model.add(Conv1D(filters=10, kernel_size=kernel_size, activation='relu', padding="same", strides=stride))
model.add(Conv1DTranspose(filters=10, kernel_size=kernel_size, activation='relu', padding="same", strides=stride))
model.add(UpSampling1D(size=2))
model.add(Conv1DTranspose(filters=15, kernel_size=kernel_size, activation='relu', padding="same", strides=stride))
model.add(Dropout(0.2))
model.add(Conv1DTranspose(filters=20, kernel_size=kernel_size, activation='relu', padding="same", strides=stride))
model.add(Conv1DTranspose(filters=n_features, kernel_size=kernel_size, padding="same"))