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https://github.com/hips/autograd
Efficiently computes derivatives of NumPy code.
https://github.com/hips/autograd
Last synced: 12 days ago
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Efficiently computes derivatives of NumPy code.
- Host: GitHub
- URL: https://github.com/hips/autograd
- Owner: HIPS
- License: mit
- Created: 2014-11-24T15:50:23.000Z (almost 10 years ago)
- Default Branch: master
- Last Pushed: 2024-10-21T20:04:07.000Z (19 days ago)
- Last Synced: 2024-10-22T14:07:03.446Z (18 days ago)
- Language: Python
- Homepage:
- Size: 15.5 MB
- Stars: 6,985
- Watchers: 211
- Forks: 911
- Open Issues: 180
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: license.txt
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- awesome-mojo - Autograd
README
# Autograd [![Checks status][checks-badge]][checks-url] [![Tests status][tests-badge]][tests-url] [![Publish status][publish-badge]][publish-url] [![asv][asv-badge]](#)
[publish-badge]: https://github.com/HIPS/autograd/actions/workflows/publish.yml/badge.svg
[checks-badge]: https://github.com/HIPS/autograd/actions/workflows/check.yml/badge.svg
[tests-badge]: https://github.com/HIPS/autograd/actions/workflows/test.yml/badge.svg
[asv-badge]: http://img.shields.io/badge/benchmarked%20by-asv-green.svg?style=flat
[publish-url]: https://github.com/HIPS/autograd/actions/workflows/publish.yml
[checks-url]: https://github.com/HIPS/autograd/actions/workflows/check.yml
[tests-url]: https://github.com/HIPS/autograd/actions/workflows/test.yml
Autograd can automatically differentiate native Python and Numpy code. It can
handle a large subset of Python's features, including loops, ifs, recursion and
closures, and it can even take derivatives of derivatives of derivatives. It
supports reverse-mode differentiation (a.k.a. backpropagation), which means it
can efficiently take gradients of scalar-valued functions with respect to
array-valued arguments, as well as forward-mode differentiation, and the two can
be composed arbitrarily. The main intended application of Autograd is
gradient-based optimization. For more information, check out the
[tutorial](docs/tutorial.md) and the [examples directory](examples/).
Example use:
```python
>>> import autograd.numpy as np # Thinly-wrapped numpy
>>> from autograd import grad # The only autograd function you may ever need
>>>
>>> def tanh(x): # Define a function
... return (1.0 - np.exp((-2 * x))) / (1.0 + np.exp(-(2 * x)))
...
>>> grad_tanh = grad(tanh) # Obtain its gradient function
>>> grad_tanh(1.0) # Evaluate the gradient at x = 1.0
np.float64(0.419974341614026)
>>> (tanh(1.0001) - tanh(0.9999)) / 0.0002 # Compare to finite differences
np.float64(0.41997434264973155)
```
We can continue to differentiate as many times as we like, and use numpy's
vectorization of scalar-valued functions across many different input values:
```python
>>> from autograd import elementwise_grad as egrad # for functions that vectorize over inputs
>>> import matplotlib.pyplot as plt
>>> x = np.linspace(-7, 7, 700)
>>> plt.plot(x, tanh(x),
... x, egrad(tanh)(x), # first derivative
... x, egrad(egrad(tanh))(x), # second derivative
... x, egrad(egrad(egrad(tanh)))(x), # third derivative
... x, egrad(egrad(egrad(egrad(tanh))))(x), # fourth derivative
>>> plt.show()
```
See the [tanh example file](examples/tanh.py) for the code.
## Documentation
You can find a tutorial [here.](docs/tutorial.md)
## End-to-end examples
* [Simple neural net](examples/neural_net.py)
* [Convolutional neural net](examples/convnet.py)
* [Recurrent neural net](examples/rnn.py)
* [LSTM](examples/lstm.py)
* [Neural Turing Machine](https://github.com/DoctorTeeth/diffmem/blob/512aadeefd6dbafc1bdd253a64b6be192a435dc3/ntm/ntm.py)
* [Backpropagating through a fluid simulation](examples/fluidsim/fluidsim.py)
* [Variational inference in Bayesian neural network](examples/bayesian_neural_net.py)
* [Gaussian process regression](examples/gaussian_process.py)
* [Sampyl, a pure Python MCMC package with HMC and NUTS](https://github.com/mcleonard/sampyl)
## How to install
Install Autograd using Pip:
```shell
pip install autograd
```
Some features require SciPy, which you can install separately or as an
optional dependency along with Autograd:
```shell
pip install "autograd[scipy]"
```
## Authors and maintainers
Autograd was written by [Dougal Maclaurin](https://dougalmaclaurin.com),
[David Duvenaud](https://www.cs.toronto.edu/~duvenaud/),
[Matt Johnson](http://people.csail.mit.edu/mattjj/),
[Jamie Townsend](https://github.com/j-towns)
and many other contributors. The package is currently being maintained by
[Agriya Khetarpal](https://github.com/agriyakhetarpal),
[Fabian Joswig](https://github.com/fjosw) and
[Jamie Townsend](https://github.com/j-towns).
Please feel free to submit any bugs or
feature requests. We'd also love to hear about your experiences with Autograd
in general. Drop us an email!
We want to thank Jasper Snoek and the rest of the HIPS group (led by Prof. Ryan
P. Adams) for helpful contributions and advice; Barak Pearlmutter for
foundational work on automatic differentiation and for guidance on our
implementation; and Analog Devices Inc. (Lyric Labs) and Samsung Advanced Institute
of Technology for their generous support.