商品识别如何搭建

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

# 构建卷积神经网络模型
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(128, (3, 3), activation='relu'),
        layers.MaxPooling2D((2, 2)),
        layers.Flatten(),
        layers.Dense(128, activation='relu'),
        layers.Dense(10, activation='softmax')  # 假设有10类商品
    ])
    return model

# 加载数据集（假设已经处理好）
train_images, train_labels = load_dataset()

# 创建并编译模型
model = create_model()
model.compile(optimizer='adam',
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])

# 训练模型
model.fit(train_images, train_labels, epochs=10)

# 保存模型
model.save('product_recognition_model.h5')