图像理解优惠卷

import tensorflow as tf
from tensorflow.keras import layers, models

# 构建一个简单的卷积神经网络模型
def create_model():
    model = models.Sequential([
        layers.Conv2D(32, (3, 3), activation='relu', input_shape=(128, 128, 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, activation='softmax')  # 假设有10种不同的优惠券类型
    ])
    model.compile(optimizer='adam',
                  loss='sparse_categorical_crossentropy',
                  metrics=['accuracy'])
    return model

# 加载和预处理数据
# 这里假设你已经有了一个包含图像数据和标签的数据集
# images = ...
# labels = ...

model = create_model()
model.fit(images, labels, epochs=10)

# 使用模型进行预测
predictions = model.predict(new_images)