帮我写基于FacNet模型和IMBD-WIKI数据集的年龄性别识别的代码，使用tensorflow框架和CNN

好的，我可以帮你写一个基于FacNet模型和IMBD-WIKI数据集的年龄性别识别的代码，使用tensorflow框架和CNN。

首先，我们需要导入必要的库：

import tensorflow as tf
from tensorflow.keras import layers
from tensorflow.keras import Model
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.optimizers import RMSprop
from tensorflow.keras.callbacks import EarlyStopping
import os
import zipfile
import numpy as np
import matplotlib.pyplot as plt

接着，我们需要下载IMBD-WIKI数据集，解压得到两个csv文件：`imdb.csv`和`wiki.csv`。我们可以使用以下代码进行下载和解压：

# 下载IMBD-WIKI数据集
!wget --no-check-certificate \
    https://data.vision.ee.ethz.ch/cvl/rrothe/imdb-wiki/static/imdb_crop.tar \
    -O /tmp/imdb.tar
# 解压数据集
local_zip = '/tmp/imdb.tar'
zip_ref = zipfile.ZipFile(local_zip, 'r')
zip_ref.extractall('/tmp')
zip_ref.close()

然后，我们需要预处理数据集。我们将使用FacNet模型来提取面部特征，并使用这些特征来训练我们的模型。我们将使用ImageDataGenerator从文件夹中读取图像，并使用FacNet模型提取面部特征。

# 加载FacNet模型
facenet = tf.keras.models.load_model('/path/to/facenet.h5')

# 定义函数，使用FacNet模型提取面部特征
def extract_features(img_path):
    img = tf.keras.preprocessing.image.load_img(img_path, target_size=(160, 160))
    x = tf.keras.preprocessing.image.img_to_array(img)
    x = np.expand_dims(x, axis=0)
    x = tf.keras.applications.imagenet_utils.preprocess_input(x)
    features = facenet.predict(x)
    return features

# 定义函数，从文件夹中读取图像并提取面部特征
def extract_features_from_folder(folder_path):
    features = []
    for filename in os.listdir(folder_path):
        img_path = os.path.join(folder_path, filename)
        features.append(extract_features(img_path))
    features = np.array(features)
    features = np.squeeze(features, axis=1)
    return features

# 从文件夹中读取图像并提取面部特征
train_features = extract_features_from_folder('/path/to/train/folder')
test_features = extract_features_from_folder('/path/to/test/folder')

# 读取IMBD-WIKI数据集，并将面部特征与年龄和性别标签进行匹配
import csv

with open('/tmp/imdb_crop/imdb.csv') as csv_file:
    csv_reader = csv.reader(csv_file, delimiter=',')
    line_count = 0
    age_labels = []
    gender_labels = []
    for row in csv_reader:
        if line_count == 0:
            line_count += 1
        else:
            img_path = os.path.join('/tmp/imdb_crop/', row[0])
            age = int(row[1])
            gender = int(row[2])
            features = extract_features(img_path)
            age_labels.append(age)
            gender_labels.append(gender)
            line_count += 1
        if line_count >= 50000:
            break

# 将数据集分为训练集和测试集
train_size = int(len(age_labels) * 0.8)
test_size = len(age_labels) - train_size
train_features = features[:train_size]
test_features = features[train_size:]
train_age_labels = age_labels[:train_size]
test_age_labels = age_labels[train_size:]
train_gender_labels = gender_labels[:train_size]
test_gender_labels = gender_labels[train_size:]

现在我们已经预处理了数据集，我们可以开始构建我们的模型。我们将使用CNN来训练我们的年龄性别识别模型。

# 定义年龄性别识别模型
def age_gender_model(input_shape):
    input_layer = layers.Input(shape=input_shape)
    x = layers.Conv2D(32, (3, 3), activation='relu')(input_layer)
    x = layers.MaxPooling2D((2, 2))(x)
    x = layers.Conv2D(64, (3, 3), activation='relu')(x)
    x = layers.MaxPooling2D((2, 2))(x)
    x = layers.Conv2D(128, (3, 3), activation='relu')(x)
    x = layers.MaxPooling2D((2, 2))(x)
    x = layers.Flatten()(x)
    x = layers.Dense(128, activation='relu')(x)
    age_output = layers.Dense(1, activation='linear', name='age_output')(x)
    gender_output = layers.Dense(1, activation='sigmoid', name='gender_output')(x)
    model = Model(inputs=input_layer, outputs=[age_output, gender_output])
    return model

# 构建模型
input_shape = train_features.shape[1:]
model = age_gender_model(input_shape)

# 编译模型
model.compile(loss={'age_output': 'mse', 'gender_output': 'binary_crossentropy'},
              optimizer=RMSprop(lr=0.0001),
              metrics={'age_output': 'mae', 'gender_output': 'accuracy'})

# 定义回调函数和ImageDataGenerator
early_stopping = EarlyStopping(monitor='val_loss', patience=3)
train_datagen = ImageDataGenerator(rescale=1./255, horizontal_flip=True)
test_datagen = ImageDataGenerator(rescale=1./255)

# 定义训练集和测试集的ImageDataGenerator
train_generator = train_datagen.flow(train_features, {'age_output': train_age_labels, 'gender_output': train_gender_labels},
                                     batch_size=32)
test_generator = test_datagen.flow(test_features, {'age_output': test_age_labels, 'gender_output': test_gender_labels},
                                   batch_size=32)

最后，我们可以使用`fit_generator`函数来训练我们的模型：

# 训练模型
history = model.fit_generator(train_generator, steps_per_epoch=100, epochs=10,
                              validation_data=test_generator, validation_steps=50,
                              callbacks=[early_stopping])

# 绘制训练过程中的损失和准确率
loss = history.history['loss']
val_loss = history.history['val_loss']
acc = history.history['gender_output_accuracy']
val_acc = history.history['val_gender_output_accuracy']
epochs = range(len(loss))
plt.plot(epochs, loss, 'bo', label='Training loss')
plt.plot(epochs, val_loss, 'b', label='Validation loss')
plt.title('Training and validation loss')
plt.legend()
plt.figure()
plt.plot(epochs, acc, 'bo', label='Training acc')
plt.plot(epochs, val_acc, 'b', label='Validation acc')
plt.title('Training and validation accuracy')
plt.legend()
plt.show()

完成训练后，我们可以使用模型对新的图像进行预测。我们可以使用以下代码来预测测试集中的图像，并计算出每个图像的年龄和性别预测值：

# 预测测试集中的图像
test_predictions = model.predict(test_features)

# 计算每个图像的年龄和性别预测值
test_age_predictions = test_predictions[0].flatten()
test_gender_predictions = (test_predictions[1] > 0.5).astype(int).flatten()

这样，我们就完成了一个基于FacNet模型和IMBD-WIKI数据集的年龄性别识别的代码，使用tensorflow框架和CNN。