sorix 1.0.0

A minimalistic framework for building neural networks in Python.

Sorix

sorix: is a library of Artificial Intelligence for beginners.

It provides a NumPy/CuPy-based backend for handling tensors on both CPU and GPU, making it ideal for beginners who want to learn how frameworks like PyTorch work internally.

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Install

• With pip

pip install sorix

Or with Poetry:

poetry add sorix

Or with UV

uv add sorix

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⚡ Quick Start

Autograd Example

from sorix import tensor

# Create tensors with gradient tracking
x = tensor([2.0], requires_grad=True)
w = tensor([3.0], requires_grad=True)
b = tensor([1.0], requires_grad=True)

# Define a simple function: y = w*x + b
y = w * x + b

# Compute gradients via backpropagation
y.backward()

print("dy/dx:", x.grad)   # → should be w = 3
print("dy/dw:", w.grad)   # → should be x = 2
print("dy/db:", b.grad)   # → should be 1

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🔢 Linear Regression Example

import numpy as np
from sorix import tensor
from sorix.nn import Linear, MSELoss
from sorix.optim import SGD

# 🎯 Generate synthetic data (y = 3x + 2 + noise)
X = np.linspace(-1, 1, 100).reshape(-1, 1)
y = 3 * X + 2 + 0.1 * np.random.randn(*X.shape)

# Convert to sorix tensors (CPU, use device="cuda" for GPU)
X_tensor = tensor(X, device="cpu")
y_tensor = tensor(y, device="cpu")

# Define model, loss, and optimizer
features, outputs = 1, 1
model = Linear(features, outputs)
criterion = MSELoss()
optimizer = SGD(model.parameters(), lr=0.1)

# 🏋️ Training loop
for epoch in range(200):
    # Forward pass
    y_pred = model(X_tensor)
    loss = criterion(y_pred, y_tensor)

    # Backward pass
    optimizer.zero_grad()
    loss.backward()
    optimizer.step()

    # Print progress every 20 epochs
    if (epoch + 1) % 20 == 0:
        print(f"Epoch [{epoch+1}/200] - Loss: {loss.item():.4f}")

# ✅ Final learned parameters
print("Learned weight:", model.coef_)
print("Learned bias:", model.intercept_)

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📖 Documentation & Examples

Explore the interactive examples:

• 1 - Tensor Basics
• 2 - Regression
• 3 - Neural Network Layers

👉 More examples available in the examples folder.

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🛠️ Project Status

sorix is under active development 🚧. New features are being added frequently, including:

• More neural network layers.
• Better GPU support.
• Extended autograd functionality.

You can contribute by:

• Reporting issues
• Adding new features
• Improving documentation
• Writing tests

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📌 Links

• PyPI Package
• GitHub Repository

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