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https://github.com/3outeille/yaae
Yaae: Yet another autodiff engine (written in Numpy).
https://github.com/3outeille/yaae
autodifferentiation autograd numpy python
Last synced: 2 months ago
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Yaae: Yet another autodiff engine (written in Numpy).
- Host: GitHub
- URL: https://github.com/3outeille/yaae
- Owner: 3outeille
- Created: 2020-05-27T08:51:30.000Z (over 4 years ago)
- Default Branch: master
- Last Pushed: 2022-06-22T02:24:12.000Z (over 2 years ago)
- Last Synced: 2023-03-05T12:57:48.482Z (almost 2 years ago)
- Topics: autodifferentiation, autograd, numpy, python
- Language: Python
- Homepage: https://hackmd.io/@machine-learning/blog-post-yaae
- Size: 77.9 MB
- Stars: 21
- Watchers: 1
- Forks: 0
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
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README
## Introduction
- Yaae (Yet another autodiff engine) is an [automatic differentiation][automatic-diff] engine written in Numpy which comes with a small neural networks library. It supports scalar operations as well as tensors operations and comes with various functions such as exponential, relu, sigmoid ... For educational puprose only.
- Here is my [blog post][blog-post] explaining how an automatic differentiation works and my design/implementation choices.
## Quickstart
- Let's compare a simple example using Yaae and Pytorch.
```python
# Yaae.
w1 = Node(2, requires_grad=True)
w2 = Node(3, requires_grad=True)
w3 = w2 * w1
w4 = w1.sin()
w5 = w3 + w4
z = w5
z.backward()
w1_yaae, w2_yaae, z_yaae = w1, w2, z
# Pytorch.
w1 = torch.Tensor([2]).double()
w1.requires_grad = True
w2 = torch.Tensor([3]).double()
w2.requires_grad = True
w3 = w2 * w1
w4 = w1.sin()
w5 = w3 + w4
z = w5
z.backward()
w1_torch, w2_torch, z_torch = w1, w2, z
# Forward pass.
assert z_yaae.data == z_torch.data.item() # True.
# Backward pass.
assert w1_yaae.grad.data == w1_torch.grad.item() # True.
assert w2_yaae.grad.data == w2_torch.grad.item() # True.
```
## Example of usage
- The files [demo_regression.ipynb][demo_regression] and [demo_classification.ipynb][demo_classification] are simple regression/classification problems solved using Yaae. As shown in the notebooks, here are the the results:
- If you are still skeptical, here is my [GAN][gan] implemented with Yaae.
## Installation
- Create a virtual environment in the root folder using [virtualenv][virtualenv] and activate it.
```bash
# On Linux terminal, using virtualenv.
virtualenv my_yaae_env
# Activate it.
source my_yaee_env/bin/activate
```
- Install **requirements.txt**.
```bash
pip install -r requirements.txt
# Tidy up the root folder.
python3 setup.py clean
```
[automatic-diff]: https://en.wikipedia.org/wiki/Automatic_differentiation
[blog-post]: https://hackmd.io/@machine-learning/blog-post-yaae
[demo_regression]: https://github.com/3outeille/Yaae/blob/master/src/example/demo_regression.ipynb
[demo_classification]: https://github.com/3outeille/Yaae/blob/master/src/example/demo_classification.ipynb
[gan]: https://github.com/3outeille/GANumpy
[virtualenv]: https://packaging.python.org/guides/installing-using-pip-and-virtual-environments/