双十二商品智能识别选购

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

# 构建简单的CNN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类商品
    ])
    return model

# 加载预训练模型或重新训练
model = create_model()
model.compile(optimizer='adam',
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])

# 假设X_train和y_train是已准备好的训练数据
model.fit(X_train, y_train, epochs=10)

# 部署时，使用以下方式进行预测
def predict_product(image):
    processed_image = preprocess_image(image)  # 自定义图像预处理函数
    predictions = model.predict(processed_image)
    return decode_predictions(predictions)  # 自定义解码预测结果的函数