Sigmoid

Description

The Sigmoid class transforms input values into the range (0) to (1), making it a staple activation function in neural networks, especially for binary classification problems.

Equation:

\[ f(x) = \frac{1}{1 + e^{-x}} \]

Parameters: Sigmoid takes no parameters.

NaN handling: NaN values are not modified by this function.

Usage Example and Plot

import numpy as np
import plotly.graph_objects as go
from screamer import Sigmoid

# Generate example data
data = np.linspace(-6, 6, 100)
sigmoid_data = Sigmoid()(data)

fig = go.Figure()
fig.add_trace(go.Scatter(y=data, mode='lines', name='Original Data'))
fig.add_trace(go.Scatter(y=sigmoid_data, mode='lines', name='Sigmoid Output', line=dict(color='red')))

fig.update_layout(
    title="Sigmoid Transformation",
    yaxis_title="Output",
    xaxis_title="Input",
    margin=dict(l=20, r=20, t=40, b=20)
)

fig.show()