Perceptron Training

This code implements a simple perceptron for binary classification. The perceptron is trained using a gradient descent approach to minimize the loss function.

Requirements

Ensure you have Python installed on your system. The script uses the math library for mathematical operations.

Usage

1. How to Run:

   • Clone the repository to your local machine:

     git clone https://github.com/toita86/ML-attempts.git
     cd ML-attempts

   • Run the script:

     python3 Dataset.py

   • Modify the x_train and x_expected arrays in the code to represent your dataset and corresponding labels.

   • Adjust hyperparameters such as lam (learning rate) and epochs in the training function as needed.

2. Description of Functions:

   • stable_sigmoid(x): Implements a stable version of the sigmoid activation function.
   • ReLU(x): Implements the Rectified Linear Unit (ReLU) activation function (comment the function you want to use).
   • weights_init(x_train, random_init=True): Initializes the weights for the perceptron.
   • perceptron(x_inputs): Computes the output of the perceptron given input features.
   • training(x_train, x_expected, epochs, early_stop_flag=True): Trains the perceptron using gradient descent.

3. Important Notes:

   • Ensure that the input features (x_train) and expected outputs (x_expected) are correctly defined for your specific problem.

Info

• License:

  • This code is provided under the GPL-3.0 license.

• Acknowledgments:

  • This code is a simple implementation for educational purposes. It may need modification for more complex scenarios or improved performance.