Relu

Description

The Relu class implements the Rectified Linear Unit activation function, a common function in neural networks and data processing that outputs the input directly if it is positive and outputs zero otherwise. This function is especially useful for introducing non-linearity while maintaining positive gradients.

Equation:

\[ f(x) = \max(0, x) \]

Parameters: Relu 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 Relu

# Generate example data with negative and positive values
data = np.linspace(-3, 3, 100)
relu_data = Relu()(data)

fig = go.Figure()
fig.add_trace(go.Scatter(y=data, mode='lines', name='Original Data'))
fig.add_trace(go.Scatter(y=relu_data, mode='lines', name='ReLU Output', line=dict(color='green')))

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

fig.show()