Heaviside Step Activation Function

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A Heaviside Step Activation Function is a Neuron Activation Function based on the Linear Threshold Function.



References

2017

  • (Mate Labs, 2017) ⇒ Mate Labs Aug 23, 2017. Secret Sauce behind the beauty of Deep Learning: Beginners guide to Activation Functions
    • QUOTE: Heaviside (Binary step, 0 or 1, high or low) step function is typically only useful within single-layer perceptrons, an early type of neural networks that can be used for classification in cases where the input data is linearly separable. These functions are useful for binary classification tasks. The output is a certain value, A1, if the input sum is above a certain threshold and A0 if the input sum is below a certain threshold. The values used by the Perceptron were A1 = 1 and A0 = 0

      [math]\displaystyle{ f(x) = \begin{cases} 0, & \mbox{for } x \lt 0 \\ 1, & \mbox{for } x \geq 0 \end{cases} }[/math]

      Range: [math]\displaystyle{ \{0 \text{ or } 1\} }[/math]

      Examples: [math]\displaystyle{ f(2) = 1,\; f(-4) = 0,\; f(0) = 0,\; f(1) = 1 }[/math]

2005

1986