请生成一段可执行的python代码，实现：应用cnn方法对人脸图像提取特征，根据提取结果构建一个机器学习模型对人脸图像进行分类，达成识别性别的目的

首先，我们需要导入所需的库，如TensorFlow、Keras用于深度学习，以及OpenCV用于预处理图像数据。以下是一个简单的例子，展示如何使用CNN（卷积神经网络）提取人脸特征并训练一个模型进行性别识别：

import tensorflow as tf
from tensorflow.keras.applications import VGG16
from tensorflow.keras.layers import GlobalAveragePooling2D, Dense, Flatten
from tensorflow.keras.models import Sequential
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from keras_face_recognition import face_detection

# 预设参数
img_width, img_height = 224, 224
batch_size = 32
epochs = 50
num_classes = 2  # 二分类任务，男性和女性

# 加载预训练的VGG16模型，并去掉顶层
base_model = VGG16(weights='imagenet', include_top=False, input_shape=(img_width, img_height, 3))

# 添加新的全连接层
x = base_model.output
x = Flatten()(x)
x = Dense(256, activation='relu')(x)  # 输出维度调整为256维
predictions = Dense(num_classes, activation='softmax')(x)

# 构建完整的模型
model = Model(inputs=base_model.input, outputs=predictions)

# 冻结预训练模型的权重
for layer in base_model.layers:
    layer.trainable = False

# 编译模型
model.compile(optimizer=tf.keras.optimizers.Adam(), loss='sparse_categorical_crossentropy', metrics=['accuracy'])

# 数据增强
train_datagen = ImageDataGenerator(rescale=1./255,
                                   rotation_range=20,
                                   width_shift_range=0.2,
                                   height_shift_range=0.2,
                                   shear_range=0.2,
                                   zoom_range=0.2,
                                   horizontal_flip=True)

# 获取人脸图像路径和对应的标签
train_generator = train_datagen.flow_from_directory('path_to_train_faces',
                                                    target_size=(img_height, img_width),
                                                    batch_size=batch_size,
                                                    class_mode='binary')

# 训练模型
history = model.fit(train_generator,
                    steps_per_epoch=len(train_generator),
                    epochs=epochs,
                    validation_data=train_generator,
                    validation_steps=len(train_generator))

# 保存模型
model.save('gender_recognition_model.h5')

# 使用模型进行预测
def predict_gender(image_path):
    image = face_detection.detect_face_and_align(image_path, (img_width, img_height))
    predictions = model.predict(image.reshape(-1, img_width, img_height, 3))
    return tf.argmax(predictions[0])

# 示例预测
image_path = 'path_to_test_image.jpg'
predicted_gender = predict_gender(image_path)
print(f"Predicted gender: {['Male', 'Female'][predicted_gender]}")