在UTKface数据集上训练inception v2网络代码

以下是在UTKface数据集上训练inception v2网络的Python代码示例：

import tensorflow as tf
from tensorflow.keras.applications.inception_v2 import InceptionV2
from tensorflow.keras.preprocessing.image import ImageDataGenerator

# Set up data generators
train_datagen = ImageDataGenerator(rescale=1./255, validation_split=0.2)
train_generator = train_datagen.flow_from_directory(
    '/path/to/UTKface/dataset',
    target_size=(224, 224),
    batch_size=32,
    class_mode='categorical',
    subset='training'
)
validation_generator = train_datagen.flow_from_directory(
    '/path/to/UTKface/dataset',
    target_size=(224, 224),
    batch_size=32,
    class_mode='categorical',
    subset='validation'
)

# Set up InceptionV2 model
base_model = InceptionV2(weights='imagenet', include_top=False, input_shape=(224, 224, 3))
x = base_model.output
x = tf.keras.layers.GlobalAveragePooling2D()(x)
x = tf.keras.layers.Dense(1024, activation='relu')(x)
predictions = tf.keras.layers.Dense(7, activation='softmax')(x)
model = tf.keras.models.Model(inputs=base_model.input, outputs=predictions)

# Compile the model
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])

# Train the model
model.fit(
    train_generator,
    epochs=10,
    validation_data=validation_generator
)

请注意，上述示例代码中的路径和参数需要根据您的数据集和需求进行修改。