Percptron-rohandhanraj 0.0.3

Its an Implementation of Percptron

Perceptron

Single Layer Perceptron(one Neuron)
A Perceptron is a simple model of a biological neuron that classifies the label of the input data based on various activation functions:

1. Binary Step Function
2. Signum Function
3. Linear Activation Function
4. Sigmoid / Logistic Activation Function
5. Tanh Function (Hyperbolic Tangent)
6. ReLU Function
7. Exponential Linear Units (ELUs) Function
8. Swish
9. Gaussian Error Linear Unit (GELU)

fn_list = ['step', 'signum', 'linear', 'relu', 'sigmoid', 'tanh', 'elu', 'gelu', 'swish']

How to use this

from Perceptron.perceptron import Perceptron
from Perceptron.utils import prepare_data, save_plot, save_model

# get the data, convert it into a DataFrame and then use below commands
X, y = prepare_data(df)

model = Perceptron(eta = eta, epochs = epochs)
model.fit(X, y, fn, alpha=None) # alpha ranges between 0 to 1 if and only if ELU activation function is applied else alpha value remains None for other activation functions

Total_Error = model.total_loss()

save_model(model, filename = filename)

save_plot(df, plotFilename, model)

Reference

Python Package Publishing Docs

GitHub Actions CICD Docs