Mish Calculator
Compute the Mish activation function output, a smooth self-regularized non-monotonic function.
About Mish Calculator
Mish is a smooth, self-gated activation function similar in spirit to Swish: Mish(x) = x·tanh(softplus(x)) = x·tanh(ln(1+e^x)). Like Swish, it is smooth everywhere, non-monotonic (dipping slightly below zero for small negative inputs), and unbounded above for positive inputs, but its softplus-then-tanh construction gives it a slightly different curvature. Mish was introduced as an activation for computer vision architectures (notably used in YOLOv4) and has empirically shown modest accuracy improvements over both ReLU and Swish on several benchmark tasks, though at a higher computational cost due to the combination of exponential, logarithm, and tanh operations.
Formula
Mish(x) = x × tanh(ln(1 + e^x))
- x
- Input value
- tanh
- Hyperbolic tangent function
- ln
- Natural logarithm
Worked example
With the default values below, Mish Calculator returns Mish(x) of 1.943959. Change any input above and every figure here is recalculated in your browser.
Inputs used
- Input (x)accepts -100–100
- 2
Results
- Mish(x)
- 1.943959
- softplus(x)
- 2.126928
- tanh(softplus(x))
- 0.971979
Step by step
- 1softplus(x) = ln(1 + e^x)= 2.126928
- 2tanh(softplus(x))= 0.971979
- 3Mish(x) = x × tanh(softplus(x))= 1.943959
Assumptions and limits
- The result is derived only from the single input listed above. Anything not among them is not part of the calculation.
- In particular, this calculator does not take inflation, tax, fees or charges or compounding frequency into account.
- Figures are rounded for display to 6 decimal places, so totals built from several rounded parts can differ slightly from the unrounded arithmetic.
How these calculators are built and tested is described on the Methodology page. If a figure looks wrong, please report it.
Frequently Asked Questions
How is Mish different from Swish?
Both are smooth, self-gated activations of the form x × (gating function), but Swish gates with a sigmoid (x × σ(βx)) while Mish gates with tanh(softplus(x)) — the softplus-tanh combination gives Mish a slightly smoother, more gradual transition near zero and marginally different curvature for negative inputs.
Is Mish more expensive to compute than ReLU or Swish?
Yes — computing Mish requires an exponential, a logarithm (for softplus), and a tanh, making it noticeably more expensive per-element than ReLU's single comparison or even Swish's single sigmoid, which is a practical tradeoff against its reported accuracy benefits.
Where has Mish been used successfully?
Mish gained popularity in computer vision, most notably as the activation function used in YOLOv4's backbone network, where it contributed to measurable accuracy improvements over the ReLU-family activations used in earlier YOLO versions.