🧮 TensorOpsInPython

A Python repository demonstrating tensor operations implemented from scratch, compared with NumPy's efficient implementations.

📝 Description

TensorOpsInPython provides educational examples of fundamental tensor operations implemented using naive Python loops alongside their NumPy equivalents. This repository serves as a learning resource for understanding the underlying mechanics of tensor operations commonly used in machine learning and deep learning applications.

✨ Features

• Basic Tensor Operations:

  • ReLU activation function
  • Addition, subtraction, and multiplication of tensors
  • Broadcasting (adding a matrix and a vector)

• Advanced Tensor Operations:

  • Vector dot product
  • Matrix-vector dot product (two implementations)
  • Matrix-matrix dot product

• Tensor Transformations:

  • Reshaping tensors
  • Transposing tensors (exchanging rows and columns)

• Comparative Learning:

  • Each operation is implemented both with naive Python loops and using NumPy's optimized functions
  • Output comparison between custom implementations and NumPy equivalents

🔧 Prerequisites

• Python 3.x
• NumPy library

🚀 Usage

Run the main script to see all tensor operations in action:

python main.py

Example Code Snippets

Implementing a Naive ReLU Function

@staticmethoddefnaive_relu(x):
assertlen(x.shape) ==2x=x.copy()
foriinrange(x.shape[0]):
forjinrange(x.shape[1]):
x[i, j] =max(x[i, j], 0)
returnx

Matrix-Vector Dot Product

@staticmethoddefnaive_matrix_vector_dot(x, y):
assertlen(x.shape) ==2# Numpy matrixassertlen(y.shape) ==1# Numpy vectorassertx.shape[1] ==y.shape[0]
z=np.zeros(x.shape[0])
foriinrange(x.shape[0]):
forjinrange(x.shape[1]):
z[i] +=x[i, j] *y[j]
returnz

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.