Rrottmann/anguita: Fast Approximation Of Tanh With Good ... - GitHub
Có thể bạn quan tâm
Skip to content You signed in with another tab or window. Reload to refresh your session. You signed out in another tab or window. Reload to refresh your session. You switched accounts on another tab or window. Reload to refresh your session. Dismiss alert {{ message }} rrottmann / anguita Public
- Notifications You must be signed in to change notification settings
- Fork 1
- Star 1
Fast approximation of tanh with good enough results.
License
MIT license 1 star 1 fork Branches Tags Activity Star Notifications You must be signed in to change notification settings- Code
- Issues 0
- Pull requests 0
- Actions
- Projects 0
- Security
- Insights
Folders and files
Name | Name | Last commit message | Last commit date | |
---|---|---|---|---|
Latest commitHistory3 Commits | ||||
anguita | anguita | |||
.gitignore | .gitignore | |||
LICENSE | LICENSE | |||
README.md | README.md | |||
setup.py | setup.py | |||
View all files |
Repository files navigation
- README
- MIT license
Efficient tanh algorithm using an approximation of the Hyperbolic function:
Speed Improvement of the Back-Propagation on Current Generation Workstations" D. Anguita, G. Parodi and R. Zunino. Proceedings of the World Congress on Neural Networking, 1993.Error of the approximation
The approximation of tanh is not precise, but much faster:
x | math.tanh | anguita.tanh | delta |
---|---|---|---|
-0.9 | -0.716297870199 | -0.689095742562 | -0.0272021276373 |
-0.8 | -0.664036770268 | -0.633359378142 | -0.0306773921261 |
-0.7 | -0.604367777117 | -0.572415613722 | -0.0319521633955 |
-0.6 | -0.537049566998 | -0.506264449302 | -0.0307851176963 |
-0.5 | -0.46211715726 | -0.434905884882 | -0.0272112723783 |
-0.4 | -0.379948962255 | -0.358339920462 | -0.0216090417935 |
-0.3 | -0.291312612452 | -0.276566556042 | -0.0147460564099 |
-0.2 | -0.197375320225 | -0.189585791622 | -0.0077895286032 |
-0.1 | -0.099667994625 | -0.0973976272017 | -0.00227036742325 |
0.1 | 0.099667994625 | 0.0973976272017 | 0.00227036742325 |
0.2 | 0.197375320225 | 0.189585791622 | 0.0077895286032 |
0.3 | 0.291312612452 | 0.276566556042 | 0.0147460564099 |
0.4 | 0.379948962255 | 0.358339920462 | 0.0216090417935 |
0.5 | 0.46211715726 | 0.434905884882 | 0.0272112723783 |
0.6 | 0.537049566998 | 0.506264449302 | 0.0307851176963 |
0.7 | 0.604367777117 | 0.572415613722 | 0.0319521633955 |
0.8 | 0.664036770268 | 0.633359378142 | 0.0306773921261 |
0.9 | 0.716297870199 | 0.689095742562 | 0.0272021276373 |
About
Fast approximation of tanh with good enough results.
Resources
ReadmeLicense
MIT license ActivityStars
1 starWatchers
2 watchingForks
1 fork Report repositoryReleases
No releases publishedPackages 0
No packages publishedLanguages
- Python 100.0%
Từ khóa » Fast Tanh X
-
Rapid Approximation Of $\tanh(x)$ - Mathematics Stack Exchange
-
Fast Hyperbolic Tangent Approximation In Javascript - Stack Overflow
-
Rapid Approximation Of $\tanh(x) - Copy Programming
-
A Fast Approximation Of The Hyperbolic Tangent When Using Posit ...
-
Hyperbolic Tangent, Tanh(x). | Download Scientific Diagram
-
Tanh - Cuemath
-
[PDF] Accurate Hyperbolic Tangent Computation - Utah Math Department
-
Numpy.tanh — NumPy V1.23 Manual
-
Hyperbolic Functions - Wikipedia
-
[PDF] Efficient TanH For Deep Learning - ArXiv
-
Finding The Inverse Of Tanh X - YouTube
-
Given Tanh(x) Find Other Hyperbolic Trig Function Values - YouTube
-
The Stability Of The Shock Profiles Of The Burgers' Equation