给我一个CNN图像识别的代码案例

以下是一个使用CNN进行图像识别的Python代码案例：

import tensorflow as tf
from tensorflow.keras import datasets, layers, models
import matplotlib.pyplot as plt

# 下载并加载数据集
(train_images, train_labels), (test_images, test_labels) = datasets.cifar10.load_data()

# 对数据进行预处理
train_images, test_images = train_images / 255.0, test_images / 255.0

# 定义模型
model = models.Sequential()
model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(32, 32, 3)))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(64, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(64, (3, 3), activation='relu'))
model.add(layers.Flatten())
model.add(layers.Dense(64, activation='relu'))
model.add(layers.Dense(10))

# 编译模型
model.compile(optimizer='adam',
              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
              metrics=['accuracy'])

# 训练模型
history = model.fit(train_images, train_labels, epochs=10, 
                    validation_data=(test_images, test_labels))

# 评估模型
test_loss, test_acc = model.evaluate(test_images,  test_labels, verbose=2)
print(test_acc)

该代码实现了一个简单的CNN模型，在CIFAR-10数据集上进行图像分类任务。运行结果会输出最终测试准确率。