File:ActivationFunctions.svg

Summary

Description
English: Figure 5 from Andreas Maier, Christopher Syben. Tobias Lasser. Christian Riess. "A gentle introduction to deep learning in medical image processing". Zeitschrift für Medizinische Physik

Volume 29, Issue 2, May 2019, Pages 86-101.

https://www.sciencedirect.com/science/article/pii/S093938891830120X#fig0010

Please reference this article, if you reuse this figure.

Original Caption: Overview of classical (sign(x), σ(x), and tanh(x)) and modern activation functions, like the Rectified Linear Unit ReLU(x) and the leaky ReLU LReLU(x).
Date
Source https://www.sciencedirect.com/science/article/pii/S093938891830120X#fig0010
Author Andreas Maier
SVG development
InfoField
 
The SVG code is valid.
 
This diagram was created with Inkscape.
Category:Valid SVG created with Inkscape:Diagrams#ActivationFunctions.svg
 
Category:Translation possible - SVGThis diagram uses embedded text that can be easily translated using a text editor.

Licensing

w:en:Creative Commons
attribution
This file is licensed under the Creative Commons Attribution 4.0 International license.
You are free:
  • to share – to copy, distribute and transmit the work
  • to remix – to adapt the work
Under the following conditions:
  • attribution – You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
Category:CC-BY-4.0#ActivationFunctions.svg Category:Artificial neural networks Category:Deep learning Category:Machine learning Category:Pattern recognition
Category:Artificial neural networks Category:CC-BY-4.0 Category:Deep learning Category:Machine learning Category:Pattern recognition Category:Translation possible - SVG Category:Valid SVG created with Inkscape:Diagrams