双十一实时字幕推荐

import tensorflow as tf
from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.preprocessing.sequence import pad_sequences

# 假设我们有一些商品描述数据
texts = ["优质商品，快速发货", "限时折扣，不容错过", ...]

# 文本预处理
tokenizer = Tokenizer()
tokenizer.fit_on_texts(texts)
sequences = tokenizer.texts_to_sequences(texts)
padded = pad_sequences(sequences, maxlen=10)

# 构建模型
model = tf.keras.Sequential([
    tf.keras.layers.Embedding(input_dim=len(tokenizer.word_index) + 1, output_dim=64),
    tf.keras.layers.LSTM(128),
    tf.keras.layers.Dense(len(tokenizer.word_index) + 1, activation='softmax')
])

model.compile(loss='sparse_categorical_crossentropy', optimizer='adam')

# 训练模型
model.fit(padded, padded[:, -1], epochs=10)

# 使用模型生成字幕
def generate_caption(prompt):
    seq = tokenizer.texts_to_sequences([prompt])
    padded_seq = pad_sequences(seq, maxlen=10)
    prediction = model.predict(padded_seq)
    return tokenizer.index_word[np.argmax(prediction[0])]

# 示例
new_caption = generate_caption("新款上市")
print(new_caption)