将Tensorflow数据集提供给scikit分类器

import tensorflow as tf
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.svm import SVC

# 加载TensorFlow数据集（以MNIST手写数字数据集为例）
(train_images, train_labels), (test_images, test_labels) = tf.keras.datasets.mnist.load_data()

# 将图像数据转换为一维向量
train_images = train_images.reshape((60000, 784))
test_images = test_images.reshape((10000, 784))

# 对图像数据进行归一化处理
scaler = StandardScaler()
train_images = scaler.fit_transform(train_images)
test_images = scaler.transform(test_images)

X_train, X_test, y_train, y_test = train_test_split(train_images, train_labels, test_size=0.2, random_state=42)

# 加载预训练的TensorFlow模型（以ResNet50为例）
base_model = tf.keras.applications.ResNet50(include_top=False, weights='imagenet')

# 提取训练集和测试集的特征
train_features = base_model.predict(X_train)
test_features = base_model.predict(X_test)

# 创建SVM分类器
classifier = SVC()

# 在提取的特征上训练分类器
classifier.fit(train_features, y_train)

# 在测试集上进行预测
predictions = classifier.predict(test_features)

# 计算准确率等评估指标
accuracy = classifier.score(test_features, y_test)