基于深度学习的人脸年龄识别算法全部代码

以下是基于深度学习的人脸年龄识别算法的全部代码：

1. 导入必要的库

import cv2
import numpy as np
import os
import glob
from random import shuffle
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers

2. 读取数据

def load_data():
    data = []
    labels = []
    image_paths = glob.glob("data/*/*.jpg") # 图片路径
    shuffle(image_paths) # 随机打乱顺序
    for img_path in image_paths:
        img = cv2.imread(img_path)
        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
        img = cv2.resize(img, (224, 224)) # 调整大小
        label = int(img_path.split(os.path.sep)[-2]) # 标签为文件夹名称
        data.append(img)
        labels.append(label)
    data = np.array(data, dtype="float32") / 255.0 # 归一化
    labels = np.array(labels, dtype="int")
    return data, labels

3. 构建模型

def build_model():
    model = keras.Sequential([
        layers.Conv2D(32, (3, 3), activation='relu', input_shape=(224, 224, 3)),
        layers.MaxPooling2D(2, 2),
        layers.Conv2D(64, (3, 3), activation='relu'),
        layers.MaxPooling2D(2, 2),
        layers.Conv2D(128, (3, 3), activation='relu'),
        layers.MaxPooling2D(2, 2),
        layers.Conv2D(128, (3, 3), activation='relu'),
        layers.MaxPooling2D(2, 2),
        layers.Flatten(),
        layers.Dense(512, activation='relu'),
        layers.Dense(1)
    ])
    return model

4. 训练模型

def train(model, data, labels):
    model.compile(optimizer='adam',
                  loss=tf.keras.losses.BinaryCrossentropy(from_logits=True),
                  metrics=['accuracy'])
    history = model.fit(data, labels, epochs=10, validation_split=0.2)
    return model, history

5. 测试模型

def test(model, data, labels):
    test_loss, test_acc = model.evaluate(data, labels, verbose=2)
    print('\nTest accuracy:', test_acc)

6. 主函数

def main():
    data, labels = load_data()
    model = build_model()
    model, history = train(model, data, labels)
    test(model, data, labels)

if __name__ == '__main__':
    main()

以上就是基于深度学习的人脸年龄识别算法的全部代码。需要注意的是，该代码仅提供了一个基本框架，具体实现可能需要根据实际情况进行调整和改进。