Here's a word-level text generation example with LSTM, starting from raw text files, in less than 50 lines of Keras & TensorFlow.https://colab.research.google.com/drive/1B9yLXcJ7Q76EUoim-2Xy7Dk1gC1pFdU1 …
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Of course, I should point out it's not 50 lines because Keras has some kind of built-in solution for text generation (it doesn't). It's 50 lines because Keras makes it easy to implement anything. It only uses generic features.
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It uses a utility to read text files, a text vectorization layer (useful for any NLP), the LSTM layer and the functional API, the callbacks infrastructure, and the default training loop.
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All of the problem-specific logic fits in 50 lines because language models are conceptually simple. A simple API means making conceptually simple things easy to implement
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magic
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