Tensorflow加载带有图像标签的图像数据集

import tensorflow as tf
from tensorflow.keras.preprocessing.image import ImageDataGenerator

# 定义图像数据集的路径
train_dir = 'path/to/train/dataset'
test_dir = 'path/to/test/dataset'

# 创建ImageDataGenerator对象，用于加载和预处理图像数据
train_datagen = ImageDataGenerator(rescale=1./255)
test_datagen = ImageDataGenerator(rescale=1./255)

# 使用flow_from_directory方法加载训练集和测试集
train_generator = train_datagen.flow_from_directory(
    train_dir,
    target_size=(224, 224),  # 图像尺寸
    batch_size=32,
    class_mode='categorical'  # 图像标签的类型
)

test_generator = test_datagen.flow_from_directory(
    test_dir,
    target_size=(224, 224),
    batch_size=32,
    class_mode='categorical'
)

# 构建模型并进行训练
model = tf.keras.models.Sequential()
# 添加模型的各层和参数设置

model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
model.fit(train_generator, epochs=10)

# 在测试集上评估模型
model.evaluate(test_generator)