Gorch

Gorch is a lightweight neural network library for educational purposes. It provides basic functionalities for building and training neural networks using automatic differentiation.

Installation

To install Gorch, clone the repository using the command below:

git clone https://github.com/Armangb1/pygorch.git

To install the required dependencies, use the following command:

pip install numpy

Usage

After cloning the repository, create a Jupyter notebook and import Gorch as shown below:

import gorch

Creating Tensors

To use backward differentiation, create a Tensor object as follows:

t1 = Tensor(data, required_grad=True)

Perform any operations on t1. At the end, use the backward method on the resulting tensor to compute the gradients:

result.backward()

Example

Here is an example of how to use Gorch in a simple neural network:

from pygorch import gorch
import numpy as np

# Generate random input and output data
input = np.random.randn(1, 5)
output = np.random.randn()

# Create input and output tensor
x = gorch.Tensor(input)
y = gorch.Tensor(output)
# Initialize weights and biases
W1_d = np.random.randn(5, 1)
b1_d = np.random.randn(1, 1)
W1 = gorch.Tensor(W1_d, required_grad=True)
b1 = gorch.Tensor(b1_d, required_grad=True)

# Forward pass
net1 = x @ W1 + b1
O1 = net1.tanh()

# Compute error
e = output - O1

# Compute cost
cost = e.transpose()@e

# Backward pass
cost.backward()

# Get gradients
dcost_dW1 = W1.grad 
dcost_db1 = b1.grad

License

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

Contributing

Contributions are welcome! Please open an issue or submit a pull request for any improvements or bug fixes.

Contact

For any questions or inquiries, please contact [arman.ghbn@gmail.com].