https://github.com/sthalles/xor-lstm
Build an LSTM model for learning the XOR function.
https://github.com/sthalles/xor-lstm
Last synced: 3 months ago
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Build an LSTM model for learning the XOR function.
- Host: GitHub
- URL: https://github.com/sthalles/xor-lstm
- Owner: sthalles
- Created: 2018-06-26T16:57:19.000Z (over 7 years ago)
- Default Branch: master
- Last Pushed: 2018-07-05T19:15:50.000Z (about 7 years ago)
- Last Synced: 2025-04-13T22:40:46.912Z (6 months ago)
- Language: Jupyter Notebook
- Size: 179 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# xor-lstm
⭐ Train an LSTM to solve the XOR problem: that is, given a sequence of bits, determine its parity. The LSTM should consume the sequence, one bit at a time, and then output the correct answer at the sequence’s end. Test the two approaches below:
- Generate a dataset of random 100,000 binary strings of length 50. Train the LSTM; what performance do you get?
- Generate a dataset of random 100,000 binary strings, where the length of each string is independently and randomly chosen between 1 and 50. Train the LSTM. Does it succeed? What explains the difference?