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https://github.com/ahammadnafiz/fiztorch
Toy Implementation of PyTorch
https://github.com/ahammadnafiz/fiztorch
algorithms deep-learning deep-neural-networks framework implementation neural-network python python-3 pytorch pytorch-tutorial tensorflow2 torch tutorial
Last synced: 11 days ago
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Toy Implementation of PyTorch
- Host: GitHub
- URL: https://github.com/ahammadnafiz/fiztorch
- Owner: ahammadnafiz
- License: mit
- Created: 2024-09-23T14:48:28.000Z (4 months ago)
- Default Branch: main
- Last Pushed: 2025-01-13T13:42:04.000Z (18 days ago)
- Last Synced: 2025-01-13T14:37:14.652Z (17 days ago)
- Topics: algorithms, deep-learning, deep-neural-networks, framework, implementation, neural-network, python, python-3, pytorch, pytorch-tutorial, tensorflow2, torch, tutorial
- Language: Python
- Homepage:
- Size: 18.4 MB
- Stars: 3
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# FizTorch: A Toy Tensor Library for Machine Learning
[](https://www.python.org/downloads/release/python-3120/)
[](LICENSE)
[](https://travis-ci.org/yourusername/FizTorch)
FizTorch is a lightweight deep learning framework designed for educational purposes and small-scale projects. It provides a simple and intuitive API for building and training neural networks, inspired by popular frameworks like PyTorch.
## Table of Contents
- [Features](#features)
- [Installation](#installation)
- [Usage](#usage)
- [Examples](#examples)
- [Contributing](#contributing)
- [License](#license)
- [Roadmap](#roadmap)
## Features
- **Tensor Operations**: Basic tensor operations with support for automatic differentiation.
- **Neural Network Layers**: Common neural network layers such as Linear and ReLU.
- **Sequential Model**: Easy-to-use sequential model for stacking layers.
- **Functional API**: Functional operations for common neural network functions.
## Installation
To install FizTorch, follow these steps:
1. **Clone the Repository**:
```sh
git clone https://github.com/ahammadnafiz/FizTorch.git
cd FizTorch
```
2. **Set Up a Virtual Environment** (optional but recommended):
```sh
python -m venv fiztorch-env
source fiztorch-env/bin/activate # On Windows, use `fiztorch-env\Scripts\activate`
```
3. **Install Dependencies**:
```sh
pip install -r requirements.txt
```
4. **Install FizTorch**:
```sh
pip install -e .
```
## Usage
Here is a simple example of how to use FizTorch to build and train a neural network:
```python
import sys
import os
sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
import pytest
import numpy as np
from fiztorch import Tensor
from fiztorch.nn import Linear, ReLU, Sequential
from fiztorch.optim.optimizer import SGD
import fiztorch.nn.functional as F
# Define a neural network
model = Sequential(
Linear(2, 3),
ReLU(),
Linear(3, 1)
)
# Create some input data
input = Tensor([[1.0, 2.0]], requires_grad=True)
# Example of training a model
def train_example():
optimizer = SGD(model.parameters(), lr=0.01)
# Dummy data for demonstration
X_train = Tensor(np.random.rand(100, 2), requires_grad=True)
y_train = Tensor(np.random.rand(100, 1))
for epoch in range(5): # Simulate 5 epochs of training
optimizer.zero_grad()
predictions = model(X_train)
loss = F.mse_loss(predictions, y_train)
loss.backward()
optimizer.step()
print(f"Epoch {epoch+1}, Loss: {loss.data}")
if __name__ == "__main__":
train_example()
```
## Examples
### MNIST HAND DIGIT TEST
Neural network training on MNIST digits using FizTorch library with Adam optimizer (configurable learning rate), batch support, real-time accuracy/loss tracking
### California Housing TEST
Neural network training on California Housing Dataset using FizTorch library
### Linear Layer
```python
from fiztorch.tensor import Tensor
from fiztorch.nn import Linear
# Create a linear layer
layer = Linear(2, 3)
# Create some input data
input = Tensor([[1.0, 2.0]])
# Forward pass
output = layer(input)
# Print the output
print(output)
```
### ReLU Activation
```python
from fiztorch.tensor import Tensor
from fiztorch.nn import ReLU
# Create a ReLU activation
relu = ReLU()
# Create some input data
input = Tensor([-1.0, 0.0, 1.0])
# Forward pass
output = relu(input)
# Print the output
print(output)
```
### Sequential Model
```python
from fiztorch.tensor import Tensor
from fiztorch.nn import Linear, ReLU, Sequential
# Define a sequential model
model = Sequential(
Linear(2, 3),
ReLU(),
Linear(3, 1)
)
# Create some input data
input = Tensor([[1.0, 2.0]])
# Forward pass
output = model(input)
# Print the output
print(output)
```
## Roadmap
### Phase 1: Core Features
- Enhance tensor operations with more advanced functionalities (e.g., broadcasting).
- Add support for GPU acceleration (e.g., via CUDA or ROCm).
- Improve the API for ease of use and consistency.
### Phase 2: Neural Network Enhancements
- Add additional layers such as Convolutional, Dropout, and BatchNorm.
- Expand activation functions (e.g.,ELU).
- Integrate pre-trained models for common tasks.
### Phase 3: Training and Optimization
- Implement additional optimizers
- Add learning rate schedulers.
- Enhance support for custom loss functions.
### Phase 4: Dataset and Data Loading
- Provide built-in dataset utilities (e.g., MNIST, CIFAR).
- Create a flexible data loader with augmentation capabilities.
### Phase 5: Visualization and Monitoring
- Add utilities for loss/accuracy visualization.
- Integrate real-time training monitoring (e.g., TensorBoard support).
### Phase 6: Community Contributions
- Establish guidelines for community-driven feature additions.
- Host challenges to encourage usage and development.
## Contributing
Contributions are welcome! Please follow these steps to contribute:
1. Fork the repository.
2. Create a new branch (`git checkout -b feature-branch`).
3. Commit your changes (`git commit -am 'Add new feature'`).
4. Push to the branch (`git push origin feature-branch`).
5. Create a new Pull Request.
## License
FizTorch is licensed under the MIT License. See the [LICENSE](https://github.com/ahammadnafiz/FizTorch/blob/main/LICENSE) file for more information.
## Contact
For any questions or feedback, please open an issue or contact the maintainers.
---
Made with ❤️ by [ahammadnafiz](https://github.com/ahammadnafiz)