🔥 CGrad

⏭️🥅 Next goal:

• Grad engine -> new task: matmul/div autograd. -> scaler part still remaining.
• randn Generator -> with seed
• Make the Tensor fast: Check the tensor.c and Tensorwrapper.pyx files again, and try to optimize them to make them faster.
• stop using numpy -> add the reshape, and other stuff.
• Build a Tensor for Int, Double, Long, etc.
• Use the Fast matrix multiplication algorithm to reduce the time complexity.
• Make loss dir and make loss like "Tenh, ReLU, sigmoid, softmax" in a more optimistic way. -> Make the loss folder, but you also need to make the backward pass for it.
• Make Optimizer start with SGD in C not in pyx (aka cython) -> after SGD -> Adam ...

✨ Overview

Lightweight library for performing tensor operations. CGrad is a module designed to handle all gradient computations, and most matrix manipulation and numerical work generally required for tasks in machine learning and deep learning. 🤖📚

💡 Features

• 🌀 Support for n-dimensional tensor operations.
• 🤖 Automatic differentiation for gradient computation.
• 🛠️ Built-in functions for common tensor operations like addition, multiplication, dot product, etc.

⚙️ Installation

For user:

pip install cgrad==0.0.1 

For Contributers

1. install MinGW for Windows user install latest MinGW.

2. install gcc for Mac or Linux user install latest gcc.

3. clone the repository and install manually:

   git clone https://github.com/Ruhaan838/CGrad
   

   python setup.py build_ext --inplace
   pip install .
   

🚀 Getting Started

Here’s a simple guide to get you started with CGrad:

📥 Importing the module

import cgrad

📦 Creating Tensors

You can create a tensor from a Python list or NumPy array:

# Creating a tensor from a list
tensor = cgrad.Tensor([1.0, 2.0, 3.0])

# Creating a tensor with a specified shape
tensor = cgrad.Tensor([[1.0, 2.0], [3.0, 4.0]])

🔄 Basic Tensor Operations

CGrad supports basic operations like addition, multiplication, etc.:

# Tensor addition 
a = cgrad.Tensor([1.0, 2.0, 3.0])
b = cgrad.Tensor([4.0, 5.0, 6.0])
result = a + b  # Element-wise addition

# Tensor multiplication 
c = cgrad.Tensor([[1.0, 2.0], [3.0, 4.0]])
d = cgrad.Tensor([[5.0, 6.0], [7.0, 8.0]])
result = c * d  # Element-wise multiplication

📐 Advance Tensor Operations

CGrad supports advanced operations like matrix multiplication etc.:

a = cgrad.randn((1,2,3))
b = cgrad.randn((5,3,2))
result = a @ b

Note: cgrad.matmul is still underdevelopment.

🔥 Gradient Computation

CGrad automatically tracks operations and computes gradients for backpropagation:

# Defining tensors with gradient tracking 
x = cgrad.Tensor([2.0, 3.0], requires_grad=True)
y = cgrad.Tensor([1.0, 4.0], requires_grad=True)

# Performing operations 
z = x * y

# Backpropagation to compute gradients 
z.backward()

# Accessing gradients 
print(x.grad)  # Gradients of x
print(y.grad)  # Gradients of y

📖 Documentation

For more detailed information, please visit our documentation website.

🤝 Contributing

I ❤️ contributions! If you’d like to contribute to CGrad, please:

You can contribute to code improvement and documentation editing.

If any issue is found, report it on the GitHub issue

1. 🍴 Clone the repository.
2. 🌱 Create a new branch for your feature or bugfix.
3. ✉️ Submit a pull request.

📖 Reading

• Blog about how tensors work at the computer level. [link]
• Cython Documentation. [link]

📝 License

📜 See LICENSE for more details.