A Fiction Generator Worthy of The New Yorker. Using an RNN to Generate Realistic Short Fiction
Its readership commands millions of the greatest intellectual minds in America, from MFA graduates to English professors at well-known universities. A single published story in _The New Yorker _has been enough to launch careers and catapult writers with slim publications to instant fame.
Often, the fiction editors from the magazine write that they want stories that don’t follow a formula. Nonetheless, it is impossible to argue that The New Yorker carries with it a certain voice — a certain air of metaphors that the majority of its readers will understand, a certain body of knowledge from which educated allusions can be made, a certain sensibility for jokes that can be chuckled at on a subway.
Though this state of existence may be ineffable, as long as a pattern exists it can be found. I decided to build a flash fiction generator, implementing a form of a recurrent neural network (RNN) called a long short-term memory (LSTM) network, which is known for its historically strong results with text generation and summarizing tasks. The LSTM was trained on with current stories from The New Yorker as training data. I used CoLab for scraping data from the magazine’s site and for training the model.
The design of the model is fairly simple. At the time I was working on this project, I hadn’t yet encountered PyTorch (highly recommend as a framework for beginners to deep learning) but TensorFlow luckily does have some pre-built components such as RNN cells.
Once you have the data scraped from the short stories, you’ll want to transform the data into a word embedding, a mapping of words into vectors of real numbers. This allows words with similar meanings to have similar numerical representations and can then be used as an input for your RNN.
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Machine Learning Pipelines performs a complete workflow with an ordered sequence of the process involved in a Machine Learning task. The Pipelines can also