https://github.com/chuvalniy/torchy
Built from scratch neural network library using only NumPy
https://github.com/chuvalniy/torchy
backpropogation from-scratch gradient math neural-net numpy pytorch-like
Last synced: 10 months ago
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Built from scratch neural network library using only NumPy
- Host: GitHub
- URL: https://github.com/chuvalniy/torchy
- Owner: chuvalniy
- License: mit
- Created: 2023-07-24T02:53:37.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2023-10-19T18:13:54.000Z (over 2 years ago)
- Last Synced: 2025-03-18T17:57:41.412Z (11 months ago)
- Topics: backpropogation, from-scratch, gradient, math, neural-net, numpy, pytorch-like
- Language: Python
- Homepage:
- Size: 538 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
[](https://www.python.org/downloads/release/python-360/)
[](https://pypi.org/project/torchy-nn/)
## Overview
Torchy is a neural network framework implemented only using NumPy and based on PyTorch API but with manual backpropogation calculations. The main idea was to build a neural network from scratch for educational purposes.
## Installation
```python
pip install torchy-nn
```
## Getting started
I suggest you to take a look at [currently implemented stuff](https://github.com/chuvalniy/Torchy/blob/main/docs/Implemented.md) to be familiar with current possibilities for building neural network models with Torchy. Also I've created [package structure](https://github.com/chuvalniy/Torchy/blob/main/docs/PackageStructure.md) in case if you stuck where to get specific layers.
## Example usage
First we can define our model using Torchy with its PyTorch-like API
```python
import torchy.nn as nn # Same as torch.nn
# Define 2-layer wtth 100 neurons hidden layer.
model = nn.Sequential(
nn.Linear(n_input=10, n_output=100),
nn.BatchNorm1d(n_output=100),
nn.ReLU(),
nn.Linear(n_input=100, n_output=2)
)
```
Next step is to create instances of optimizer and criterion for loss function and scheduler for fun
```python
import torchy.optimizers as optim
optimizer = optim.SGD(model.params(), lr=1e-3)
criterion = nn.CrossEntropyLoss()
scheduler = optim.StepLR(optimizer, step_size=10)
```
I won't cover whole training process like loops and stuff, just show you main differences while training
```python
...
predictions = model(X) # Nothing changed
loss, grad = criterion(predictions, y) # Now return tuple of (loss, grad) instead of only loss
optimizer.zero_grad()
model.backward(grad) # Call backward on model object and pass gradient from loss as argument
optimizer.forward_step()
```
## Testing
Every neural network module is provided with unit test that verify correctness of forward and backward passes.
Execute the following command in your project directory to run the tests.
```python
pytest -v
```
## Demonstration
The [demo notebook](https://github.com/chuvalniy/Torchy/blob/main/demos/torchy-demo-1.ipynb) showcases what Torchy currently can do.
## Roadmap
It seems to me that I've completed all the modules that I wanted to code, but there are still some unfinished points, here are some of them:
- Dataset, DataLoader & Sampler;
- Transformer;
- Fix regularization;
- Autograd (as new git branch)
## Resources
The opportunity to create such a project was given to me thanks to these people
- [PyTorch](https://github.com/pytorch/pytorch)
- [CS231n - 2016](https://youtube.com/playlist?list=PLkt2uSq6rBVctENoVBg1TpCC7OQi31AlC)
- [Deep Learning на пальцах - 2019](https://youtube.com/playlist?list=PL5FkQ0AF9O_o2Eb5Qn8pwCDg7TniyV1Wb)
- [Neural Networks: Zero to Hero](https://youtube.com/playlist?list=PLAqhIrjkxbuWI23v9cThsA9GvCAUhRvKZ)
## License
[MIT License](https://github.com/chuvalniy/Torchy/blob/main/LICENSE)