智能识别怎么创建

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

# 加载预训练模型
model = models.Sequential([
    layers.Conv2D(32, (3, 3), activation='relu', input_shape=(64, 64, 3)),
    layers.MaxPooling2D((2, 2)),
    layers.Conv2D(64, (3, 3), activation='relu'),
    layers.MaxPooling2D((2, 2)),
    layers.Conv2D(64, (3, 3), activation='relu'),
    layers.Flatten(),
    layers.Dense(64, activation='relu'),
    layers.Dense(10)
])

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

# 加载数据
(train_images, train_labels), (test_images, test_labels) = tf.keras.datasets.cifar10.load_data()

# 数据归一化
train_images, test_images = train_images / 255.0, test_images / 255.0

# 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('\nTest accuracy:', test_acc)

# 预测
predictions = model.predict(test_images)