这是一个技术问题。我有10个不同信号源产生的正交IQ波形数据，数据点用复数表示，每个信号源的波形数据有200个，分别保存在共2000个.mat文件中，来自同一个信号源的数据文件保存在同一个文件夹中。现在我要首先将所有mat文件分别投入一个卷积神经网络以提取特征，接下来分别从这十个信号源的数据文件夹中依次执行取20个.mat文件的波形数据并用这些文件提取到的特征求得20个信号的聚类中心的操作，执行十次后提取到十个聚类中心，再将这十个文件夹中所有的2000个文件的信号提取出的特征整合依据它们与之前求得的十个聚类中心的欧式距离得到完整聚类结果。最后，将所有数据聚类结果与它们原本的类别对比，得到聚类结果的准确率并输出。此外，需要设计损失函数以优化模型。请使用python3.10实现并给出参考代码

这是一个技术问题，我可以回答。以下是参考代码：

import os
import numpy as np
import tensorflow as tf
from sklearn.cluster import KMeans

# 定义卷积神经网络
def create_cnn():
    model = tf.keras.Sequential([
        tf.keras.layers.Conv2D(32, (3, 3), activation='relu', input_shape=(200, 2, 1)),
        tf.keras.layers.MaxPooling2D((2, 2)),
        tf.keras.layers.Conv2D(64, (3, 3), activation='relu'),
        tf.keras.layers.MaxPooling2D((2, 2)),
        tf.keras.layers.Conv2D(64, (3, 3), activation='relu'),
        tf.keras.layers.Flatten(),
        tf.keras.layers.Dense(64, activation='relu'),
        tf.keras.layers.Dense(10)
    ])
    return model

# 定义损失函数
def custom_loss(y_true, y_pred):
    return tf.reduce_mean(tf.square(y_true - y_pred))

# 加载数据
def load_data(folder):
    data = []
    for file in os.listdir(folder):
        if file.endswith('.mat'):
            path = os.path.join(folder, file)
            mat = scipy.io.loadmat(path)
            data.append(mat['data'])
    return np.array(data)

# 提取特征
def extract_features(data):
    cnn = create_cnn()
    cnn.compile(optimizer='adam', loss=custom_loss)
    features = cnn.predict(data)
    return features

# 聚类
def cluster(features):
    kmeans = KMeans(n_clusters=20)
    kmeans.fit(features)
    centers = kmeans.cluster_centers_
    return centers

# 计算欧式距离
def euclidean_distance(x, y):
    return np.sqrt(np.sum(np.square(x - y)))

# 聚类结果
def cluster_result(features, centers):
    result = []
    for feature in features:
        distances = [euclidean_distance(feature, center) for center in centers]
        label = np.argmin(distances)
        result.append(label)
    return result

# 计算准确率
def accuracy(labels_true, labels_pred):
    correct = np.sum(labels_true == labels_pred)
    total = len(labels_true)
    return correct / total

# 主函数
if __name__ == '__main__':
    data_folders = ['data1', 'data2', 'data3', 'data4', 'data5', 'data6', 'data7', 'data8', 'data9', 'data10']
    data = []
    for folder in data_folders:
        folder_path = os.path.join('data', folder)
        folder_data = load_data(folder_path)
        folder_features = extract_features(folder_data)
        data.append(folder_features)
    data = np.concatenate(data, axis=0)
    centers = []
    for i in range(10):
        folder_features = data[i*200:i*200+200]
        folder_centers = cluster(folder_features)
        centers.append(folder_centers)
    centers = np.concatenate(centers, axis=0)
    labels_true = np.repeat(np.arange(200), 10)
    labels_pred = cluster_result(data, centers)
    acc = accuracy(labels_true, labels_pred)
    print('Accuracy:', acc)

注意：以上代码仅供参考，可能需要根据具体情况进行修改。