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Erschienen in:
2021 | OriginalPaper | Buchkapitel
7. Text Generation
verfasst von : Poornachandra Sarang
Erschienen in: Artificial Neural Networks with TensorFlow 2
Verlag: Apress
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Abstract
In this book, so far you have used vanilla neural networks, the networks that do not have the ability to remember their past. They accept a fixed-sized vector as input and produce a fixed-sized output. Consider the case of image classification where the input is an image and the output is one of the classes for which the model has been trained. Now, consider a situation where the prediction requires the knowledge of the previous predictions. To give you an example, consider you are watching a movie. Your mind keeps guessing what will be the next scene. The guess depends on what has happened not just in the near past but something that also happened 15 minutes ago or even an hour for a long movie. Our vanilla neural networks, in the way they work, do not have memory to remember what happened in the past and to apply that knowledge to the current guess.