No module named 'keras.applications.resnet50'

抱歉，是我之前的回答有误，正确的加载 ResNet50 模型的方法是使用 `tensorflow.keras.applications.resnet50`。以下是修改后的代码示例：

import os
import numpy as np
from PIL import Image
from tensorflow.keras.applications.resnet50 import ResNet50, preprocess_input
from sklearn.cluster import KMeans

# 加载 ResNet50 模型
model = ResNet50(weights='imagenet', include_top=False)

# 加载并预处理图片，提取特征向量
features = []
for filename in os.listdir('D:/wjd/2'):
    if filename.endswith('.png'):
        img = Image.open(os.path.join('D:/wjd/2', filename))
        img = img.resize((224, 224))
        x = np.array(img)
        x = np.expand_dims(x, axis=0)
        x = preprocess_input(x)
        feature = model.predict(x)
        feature = np.squeeze(feature)
        features.append(feature)

# 将特征向量进行聚类，自动确定聚类的数量
kmeans = KMeans(n_clusters=None, max_iter=1000, n_init=50).fit(features)
n_clusters = len(set(kmeans.labels_))

# 将每张图片分配到对应的聚类中
for i, label in enumerate(kmeans.labels_):
    filename = os.listdir('D:/wjd/2')[i]
    print('{} belongs to cluster {}'.format(filename, label))

请注意，加载 ResNet50 模型的代码已经修改为 `from tensorflow.keras.applications.resnet50 import ResNet50`。如果仍然存在问题，请确认你已正确安装 TensorFlow 和 Keras 库。