Skip to main content

Differentiable Fuzzy Logic operators for

Project description

torch-norms

t-norms in PyTorch

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

torchnorms-1.0.2.tar.gz (5.3 kB view details)

Uploaded Source

Built Distribution

torchnorms-1.0.2-py3-none-any.whl (13.5 kB view details)

Uploaded Python 3

File details

Details for the file torchnorms-1.0.2.tar.gz.

File metadata

  • Download URL: torchnorms-1.0.2.tar.gz
  • Upload date:
  • Size: 5.3 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.7.1 importlib_metadata/4.8.3 pkginfo/1.8.2 requests/2.13.0 requests-toolbelt/0.9.1 tqdm/4.36.1 CPython/3.6.9

File hashes

Hashes for torchnorms-1.0.2.tar.gz
Algorithm Hash digest
SHA256 1611fcb32bce050a3f0a5161904304d1345a040c27c0c9668074721fd17525fd
MD5 5b9819273ce5300f0e7c50fd00d196dc
BLAKE2b-256 959d49bac38c482904fd3eefddaff0bbfe0124b5b407a6759b215458a25929a5

See more details on using hashes here.

File details

Details for the file torchnorms-1.0.2-py3-none-any.whl.

File metadata

  • Download URL: torchnorms-1.0.2-py3-none-any.whl
  • Upload date:
  • Size: 13.5 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.7.1 importlib_metadata/4.8.3 pkginfo/1.8.2 requests/2.13.0 requests-toolbelt/0.9.1 tqdm/4.36.1 CPython/3.6.9

File hashes

Hashes for torchnorms-1.0.2-py3-none-any.whl
Algorithm Hash digest
SHA256 191e9f09b5b44bc2ef41220bcbe740148f23accef767b2cb1a36fa221c19d923
MD5 02d7a17c12f6e3261ec0c1c470c28aee
BLAKE2b-256 bf59811481df505ef88965789c5dbbe5933823f0b9752adf6d2a206759f90fba

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page