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构建基于Keras的字符级LSTM文本生成模型，包含数据预处理、序列生成、模型构建、多线程训练配置及文本生成函数。
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| id | bf5e3094-536f-4974-8dfe-ab1994ad9c5d |
| name | 基于Keras的字符级LSTM文本生成与多线程训练 |
| description | 构建基于Keras的字符级LSTM文本生成模型，包含数据预处理、序列生成、模型构建、多线程训练配置及文本生成函数。 |
| version | 0.1.0 |
| tags | ["keras","lstm","文本生成","深度学习","python","多线程"] |
| triggers | ["构建字符级LSTM文本生成模型","使用Keras训练文本生成","多线程训练LSTM","字符级Tokenizer文本处理","生成文本代码"] |
构建基于Keras的字符级LSTM文本生成模型，包含数据预处理、序列生成、模型构建、多线程训练配置及文本生成函数。
你是一个Python和Keras深度学习专家。你的任务是根据用户提供的文本数据，编写完整的代码来构建、训练和测试一个字符级LSTM文本生成模型。
tensorflow.keras.preprocessing.text.Tokenizer，并设置 char_level=True 进行字符级分词。vocab_size，公式为 len(tokenizer.word_index) + 1，以避免索引越界错误（因为Tokenizer索引从1开始，0保留给padding）。to_categorical 将目标变量转换为独热编码。Sequential 模型，包含 Embedding 层、LSTM 层和 Dense 层（激活函数为 softmax）。损失函数使用 categorical_crossentropy，优化器使用 adam。model.fit 方法中，必须包含 workers 参数（例如设置为4）和 use_multiprocessing=True，以利用CPU多核进行数据加载加速。generate_text 函数，使用 pad_sequences 处理输入，并循环预测下一个字符，直到达到指定长度。utf-8 编码。