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https://github.com/cpury/lstm-math
Neural network that solves math equations on the character level
https://github.com/cpury/lstm-math
deep-learning keras lstm machine-learning python seq2seq tensorflow
Last synced: 14 days ago
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Neural network that solves math equations on the character level
- Host: GitHub
- URL: https://github.com/cpury/lstm-math
- Owner: cpury
- License: mit
- Created: 2017-07-28T14:13:18.000Z (over 7 years ago)
- Default Branch: master
- Last Pushed: 2023-03-25T01:01:59.000Z (over 1 year ago)
- Last Synced: 2024-10-04T12:51:08.357Z (about 1 month ago)
- Topics: deep-learning, keras, lstm, machine-learning, python, seq2seq, tensorflow
- Language: Jupyter Notebook
- Homepage: http://cpury.github.io/learning-math/
- Size: 1.81 MB
- Stars: 27
- Watchers: 3
- Forks: 8
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# lstm-math
See the accompanying blog post here: http://cpury.github.io/learning-math/
Simply try it yourself in Google Colab: https://colab.research.google.com/github/cpury/lstm-math/blob/master/lstm_math.ipynb (**NEW**)
Train a LSTM-based Seq2Seq network to predict the result of math equations on
the character level.
Configuration through global variables because I'm lazy.
Written by Max Schumacher (@cpury) in Summer 2017. Updated in Summer 2019.
## Preview
Will learn how to complete simple math formulas. E.g. during training, you will
get samples like this:
```
Examples:
67 + 38 = 108 (expected: 105)
15 + 49 = 69 (expected: 64)
84 - 91 = -5 (expected: -7)
71 + 53 = 123 (expected: 124)
72 - 1 = 75 (expected: 71)
```
And ideally, after an half an hour CPU-time or so, it will learn (almost)
perfectly to get the right results :)
Maybe something like:
```
Examples:
36 + 55 = 91 (expected: 91)
32 + 45 = 77 (expected: 77)
15 + 93 = 108 (expected: 108)
41 + 82 = 123 (expected: 123)
4 + 89 = 93 (expected: 93)
```
## Details
This uses a Seq2Seq model based on LSTMs in Keras. Depending on the complexity
of equations you choose, it will train on some small percentage of the complete
equation space and validate on another small percentage. So all the equations you
see in the example above have not been seen by the network before.
## Running it yourself
Please note that I wrote this for Python 3.6+. It will probably work with 2.7+,
but I don't offer any guarantees on that.
0. Set up a virtual env: `virtualenv venv; source venv/bin/activate`
1. Install the requirements: `pip install -r requirements.txt`
2. *Optional:* Open `main.py` and play around with the global config values.
3. `python main.py`
You can cancel the training any time with `ctrl+c`. It will always output some
examples at the end and store the model in `model.h5`, even if you cancel.
Actually, since it's not stopping when overfitting has begun, it might actually
be good to hit `ctr+c` once you're happy with the results.
## Playing with the config
All the config values at the top of `main.py` should be fairly
self-explanatory. You could e.g.
* Change the math formulas to be trained on:
* Change `MAX_NUMBER` to allow higher values in the equations
* Work with negative numbers by setting `MIN_NUMBER` to something negative
* Add multiplication by adding `'*'` to `OPERATIONS`
* Have more operations per equation by increasing `N_OPERATIONS`
* etc.
* Change the model to be trained:
* Change the size of the hidden layer(s) with `HIDDEN_SIZE`
* Make the encoder deeper with `ENCODER_DEPTH`
* Make the decoder deeper with `DECODER_DEPTH`
* Use dropout with `DROPOUT`
* etc.
* Reverse all strings to overcome the digit ordering problem with `REVERSE`
(see the blog post for more info)
## Feedback
Feel free to submit issues if you find bugs or room for improvement.