Metadata-Version: 2.3 Name: autograd Version: 1.7.0 Summary: Efficiently computes derivatives of NumPy code. Project-URL: Source, https://github.com/HIPS/autograd Author-email: Dougal Maclaurin , David Duvenaud , Matthew Johnson , Jamie Townsend Maintainer-email: Jamie Townsend , Fabian Joswig , Agriya Khetarpal License: The MIT License (MIT) Copyright (c) 2014 by the President and Fellows of Harvard University Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. Keywords: Automatic differentiation,NumPy,Python,SciPy,backpropagation,gradients,machine learning,neural networks,optimization Classifier: Development Status :: 4 - Beta Classifier: Intended Audience :: Information Technology Classifier: Intended Audience :: Science/Research Classifier: License :: OSI Approved :: MIT License Classifier: Programming Language :: Python :: 3.8 Classifier: Programming Language :: Python :: 3.9 Classifier: Programming Language :: Python :: 3.10 Classifier: Programming Language :: Python :: 3.11 Classifier: Programming Language :: Python :: 3.12 Classifier: Topic :: Scientific/Engineering Requires-Python: >=3.8 Requires-Dist: numpy Provides-Extra: scipy Requires-Dist: scipy; extra == 'scipy' Description-Content-Type: text/markdown # 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 ... y = np.exp(-2.0 * x) ... return (1.0 - y) / (1.0 + y) ... >>> grad_tanh = grad(tanh) # Obtain its gradient function >>> grad_tanh(1.0) # Evaluate the gradient at x = 1.0 0.41997434161402603 >>> (tanh(1.0001) - tanh(0.9999)) / 0.0002 # Compare to finite differences 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, 200) >>> 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 ... x, egrad(egrad(egrad(egrad(egrad(tanh)))))(x), # fifth derivative ... x, egrad(egrad(egrad(egrad(egrad(egrad(tanh))))))(x)) # sixth 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.