集成学习的方法对lfw (labeled faces in the wild)数据集进行分类。给我解决的代码‘’
时间: 2024-10-13 21:06:05 浏览: 5
集成学习在LFW数据集上进行人脸识别分类通常涉及到使用深度学习模型,如卷积神经网络(CNN),并结合多种集成策略,如投票、平均等。这里是一个基本的例子,我们将使用`scikit-learn`库中的`VotingClassifier`来进行集成。首先,我们需要安装必要的库,然后加载预训练的CNN模型(例如FaceNet)以及LFW数据集。
```python
!pip install scikit-image numpy face_recognition
import os
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder
from sklearn.ensemble import VotingClassifier
from keras_vggface.vggface import VGGFace
from keras_vggface.utils import preprocess_input
# 加载LFW数据集
lfw_dir = 'path_to_lfw_dataset'
labels, paths = [], []
for root, dirs, files in os.walk(lfw_dir):
for file in files:
if '.jpg' in file:
labels.append(os.path.basename(root))
paths.append(os.path.join(root, file))
# 编码标签
le = LabelEncoder()
labels = le.fit_transform(labels)
# 分割数据集
X = [preprocess_input(face_recognition.load_image_file(path)) for path in paths]
y = labels
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# 定义CNN模型
base_estimators = [
('vgg16', VGGFace(include_top=False)),
('resnet50', VGGFace(model_name='resnet50', include_top=False)),
# 添加更多模型...
]
# 创建VotingClassifier实例
voting_classifier = VotingClassifier(estimators=base_estimators, voting='soft')
# 训练模型
voting_classifier.fit(X_train, y_train)
# 预测和评估
y_pred = voting_classifier.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print(f'Accuracy on LFW: {accuracy * 100:.2f}%')
```
注意:这段代码假设你已经安装了`keras_vggface`库,用于加载预先训练的FaceNet模型。实际操作中,你可能还需要下载预训练模型,并调整路径。此外,`face_recognition`库用于读取图片,也需要安装。