Learn to Add Numbers with an Encoder-Decoder LSTM Recurrent Neural Network
Last Updated on August 27, 2020
Long Short-Term Memory (LSTM) networks are a type of Recurrent Neural Network (RNN) that are capable of learning the relationships between elements in an input sequence.
A good demonstration of LSTMs is to learn how to combine multiple terms together using a mathematical operation like a sum and outputting the result of the calculation.
A common mistake made by beginners is to simply learn the mapping function from input term to the output term. A good demonstration of LSTMs on such a problem involves learning the sequenced input of characters (“50+11”) and predicting the sequence output in characters (“61”). This hard problem can be learned with LSTMs using the sequence-to-sequence, or seq2seq (encoder-decoder), stacked LSTM configuration.
In this tutorial, you will discover how to address the problem of adding sequences of randomly generated integers using LSTMs.
After completing this tutorial, you will know:
- How to learn the naive mapping function of input terms to output terms for addition.
- How to frame the addition problem (and similar problems) and suitably encode inputs and outputs.
- How to address the true sequence-prediction addition problem using the seq2seq paradigm.
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