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Swish (SiLU) Activation Function

Plot x times sigmoid(beta x) and its derivative, and change beta.

swish(x)=x σ(βx)\mathrm{swish}(x) = x\,\sigma(\beta x)swish(x)=xσ(βx)swish′(x)=σ(βx)+βx σ(βx) (1−σ(βx))\mathrm{swish}'(x) = \sigma(\beta x) + \beta x\,\sigma(\beta x)\,(1-\sigma(\beta x))swish′(x)=σ(βx)+βxσ(βx)(1−σ(βx))

Swish (SiLU) at x = 1

0.731059

Derivative at this point: 0.927671. Range of the function: [about -0.28, infinity).

Swish (SiLU) and its derivative

  • Swish (SiLU)
  • Swish (SiLU) derivative
Table of values
Plot of Swish (SiLU) and its derivative: sampled values across the visible range
xSwish (SiLU)Swish (SiLU) derivative
-5-0.033464-0.026547
-4-0.071945-0.052665
-3-0.14228-0.088104
-2-0.23841-0.090784
-1-0.268940.072329
000.5
10.731060.92767
21.76161.0908
32.85771.0881
43.92811.0527
54.96651.0265

The derivative is what backpropagation multiplies by. Where it is near zero, learning signals fade (the vanishing gradient problem).

Values

xf(x)f'(x)
-3-0.142278-0.088104
-2-0.238406-0.090784
-1-0.2689410.072329
-0.5-0.188770.260039
000.5
0.50.311230.739961
10.7310590.927671
21.7615941.090784
32.8577221.088104

About Swish (SiLU)

The input times its own sigmoid. With beta = 1 it is called SiLU.

  • Strengths: Smooth and self-gated; often matches or beats ReLU in deeper models.
  • Weaknesses: Non-monotonic and costs a sigmoid per unit.
  1. Tools
  2. Machine Learning
  3. Activation Function Plotter
  4. Swish (SiLU) Activation Function

About Swish (SiLU) Activation Function

This swish calculator plots x times sigmoid(beta x) and its derivative for any beta. With beta equal to 1 the function is called SiLU. It is free, needs no sign-up and runs in your browser, so nothing you enter is uploaded.

Swish multiplies the input by a sigmoid gate that depends on the input itself. For large positive x the gate is close to 1 and swish is close to x; for large negative x it is close to 0. A small dip below zero in between makes it non-monotonic. It was found by an automated search over activation functions and often matches or beats ReLU in deep models.

Beta controls the sharpness of the gate. At beta = 0 swish is x / 2, a straight line; as beta grows it approaches ReLU. Try beta = 5 to see the curve tighten toward ReLU.

How to use Swish (SiLU) Activation Function

  1. 1Choose a function from the menu, or "Compare all" to overlay the curves.
  2. 2For leaky ReLU, ELU and swish, set the extra parameter (alpha or beta) if you want something other than the default.
  3. 3Type an x to see f(x) and f'(x), and read the table of values.
  4. 4Zoom, pan and trace the plot to read the curve. Double-tap resets the view.
  5. 5For softmax, enter your scores separated by commas, set the temperature and read the probabilities and the steps.
  6. 6Read the strengths and weaknesses under the plot to compare the functions.

Features

  • Nine activation functions with formulas and derivatives rendered in LaTeX.
  • Interactive plot of the function and its derivative, with zoom, pan, trace and PNG download.
  • A compare view of eight functions on one plot.
  • Parameters for leaky ReLU (alpha), ELU (alpha) and swish (beta).
  • GELU computed exactly with the error function, not the tanh approximation.
  • Softmax with temperature and a worked, numerically stable calculation.
  • A value table, range, and plain-language strengths and weaknesses for each function.

Worked examples

  • beta = 1 at x = -2, -1, 0, 1, 2 → gives -2: -0.2384, -1: -0.2689, 0: 0, 1: 0.7311, 2: 1.7616

    With beta = 5, swish(-1) is -0.0067, close to ReLU's 0.

Privacy: how it works

  • All calculations and plots run in your browser tab. Nothing is sent to Ulkaa or anyone else.

Tips

  • Compare the derivative curves: where one is flat near zero, learning is slow in that region of input.
  • Try a leaky ReLU with a large alpha such as 0.3 to see how the negative side tilts, and a swish beta of 5 to see it approach ReLU.
  • For softmax, add 100 to every score: the probabilities do not change, because only the differences matter.
  • Scores with one very large value give a probability close to 1 for that class, whatever the others are.

Frequently asked questions

Is it really free?

Yes. There is no price, no trial and no credit card. Ulkaa Tool does the work with your own device, so there is no server bill to pass on to you.

Do I need an account?

No. There is no sign-up and no login. Open the page and use it. An Ulkaa account is not needed for anything on this tool.

Does it run in my browser?

Yes. Your inputs are processed by code running in your browser tab, on your own device.

Is my data uploaded?

No. Your inputs are never sent to Ulkaa or to anyone else. You can check this yourself by watching the Network tab in your browser developer tools while you use the tool.

What is the difference between swish and SiLU?

SiLU is swish with beta fixed at 1. Swish keeps beta as a parameter.

Why does swish dip below zero?

For small negative x the product of a negative number and a gate that is still positive is a small negative value, before the gate closes and the output returns to zero.

More Activation Function Plotter pages

  • Activation Function PlotterPlot any activation function and its derivative, evaluate it at a point, or turn scores into softmax probabilities.
  • Sigmoid Function CalculatorEvaluate the logistic sigmoid and its derivative at any x, and plot both.
  • Tanh Function CalculatorEvaluate tanh and its derivative at any x, and plot both.
  • ReLU Activation FunctionPlot max(0, x) and its derivative and evaluate them at any input.
  • Leaky ReLU CalculatorSet the negative slope alpha and see how leaky ReLU differs from ReLU.
  • ELU Activation FunctionPlot the exponential linear unit and its derivative for any alpha.
  • GELU Activation FunctionPlot the Gaussian error linear unit and its derivative, and evaluate it at any x.
  • Softplus Function CalculatorPlot softplus and its derivative, which is exactly the sigmoid.
  • Softmax CalculatorTurn a list of scores into probabilities, with the working shown.

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