How to Learn to Echo Random Integers with LSTMs in Keras
Last Updated on August 27, 2020
Long Short-Term Memory (LSTM) Recurrent Neural Networks are able to learn the order dependence in long sequence data.
They are a fundamental technique used in a range of state-of-the-art results, such as image captioning and machine translation.
They can also be difficult to understand, specifically how to frame a problem to get the most out of this type of network.
In this tutorial, you will discover how to develop a simple LSTM recurrent neural network to learn how to echo back the number in an ad hoc sequence of random integers. Although a trivial problem, developing this network will provide the skills needed to apply LSTM on a range of sequence prediction problems.
After completing this tutorial, you will know:
- How to develop a LSTM for the simpler problem of echoing any given input.
- How to avoid the beginner’s mistake when applying LSTMs to sequence problems like echoing integers.
- How to develop a robust LSTM to echo the last observation from ad hoc sequences of random integers.
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