Skip to main content

TensorFlow Addons.

Project description

TensorFlow Addons is a repository of contributions that conform to well- established API patterns, but implement new functionality not available in core TensorFlow. TensorFlow natively supports a large number of operators, layers, metrics, losses, and optimizers. However, in a fast moving field like ML, there are many interesting new developments that cannot be integrated into core TensorFlow (because their broad applicability is not yet clear, or it is mostly used by a smaller subset of the community).

Project details


Release history Release notifications | RSS feed

Download files

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

Source Distributions

No source distribution files available for this release.See tutorial on generating distribution archives.

Built Distributions

If you're not sure about the file name format, learn more about wheel file names.

tfa_nightly-0.15.0.dev20211110010902-cp39-cp39-win_amd64.whl (756.6 kB view details)

Uploaded CPython 3.9Windows x86-64

tfa_nightly-0.15.0.dev20211110010902-cp39-cp39-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (1.1 MB view details)

Uploaded CPython 3.9manylinux: glibc 2.12+ x86-64

tfa_nightly-0.15.0.dev20211110010902-cp39-cp39-macosx_11_0_arm64.whl (555.4 kB view details)

Uploaded CPython 3.9macOS 11.0+ ARM64

tfa_nightly-0.15.0.dev20211110010902-cp39-cp39-macosx_10_13_x86_64.whl (585.1 kB view details)

Uploaded CPython 3.9macOS 10.13+ x86-64

tfa_nightly-0.15.0.dev20211110010902-cp38-cp38-win_amd64.whl (756.6 kB view details)

Uploaded CPython 3.8Windows x86-64

tfa_nightly-0.15.0.dev20211110010902-cp38-cp38-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (1.1 MB view details)

Uploaded CPython 3.8manylinux: glibc 2.12+ x86-64

tfa_nightly-0.15.0.dev20211110010902-cp38-cp38-macosx_11_0_arm64.whl (555.4 kB view details)

Uploaded CPython 3.8macOS 11.0+ ARM64

tfa_nightly-0.15.0.dev20211110010902-cp38-cp38-macosx_10_13_x86_64.whl (585.1 kB view details)

Uploaded CPython 3.8macOS 10.13+ x86-64

tfa_nightly-0.15.0.dev20211110010902-cp37-cp37m-win_amd64.whl (756.6 kB view details)

Uploaded CPython 3.7mWindows x86-64

tfa_nightly-0.15.0.dev20211110010902-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (1.1 MB view details)

Uploaded CPython 3.7mmanylinux: glibc 2.12+ x86-64

tfa_nightly-0.15.0.dev20211110010902-cp37-cp37m-macosx_10_13_x86_64.whl (585.1 kB view details)

Uploaded CPython 3.7mmacOS 10.13+ x86-64

File details

Details for the file tfa_nightly-0.15.0.dev20211110010902-cp39-cp39-win_amd64.whl.

File metadata

  • Download URL: tfa_nightly-0.15.0.dev20211110010902-cp39-cp39-win_amd64.whl
  • Upload date:
  • Size: 756.6 kB
  • Tags: CPython 3.9, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.5.0 importlib_metadata/4.8.2 pkginfo/1.7.1 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.7.12

File hashes

Hashes for tfa_nightly-0.15.0.dev20211110010902-cp39-cp39-win_amd64.whl
Algorithm Hash digest
SHA256 2ed39b65563567cfc4e299b7e148a6b8f199af0d9d0cc5c51e8923fb8ecf5a6d
MD5 7abea8c4000c94395eb2fc1a806ac272
BLAKE2b-256 f5f52014d5c3f63bb7732f235aba8765ec984275da360cb3e40801a7e89b19dc

See more details on using hashes here.

File details

Details for the file tfa_nightly-0.15.0.dev20211110010902-cp39-cp39-manylinux_2_12_x86_64.manylinux2010_x86_64.whl.

File metadata

File hashes

Hashes for tfa_nightly-0.15.0.dev20211110010902-cp39-cp39-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
Algorithm Hash digest
SHA256 a6d5d2a0a918d7f63e960e992f745f78a939436e0823383234b0091c0b5aea6d
MD5 85cc2de61aa00c3fdcc11c94c2926f68
BLAKE2b-256 dd913a9a95125f43e372c16f7e34ad4d3e4bf744749b36f465fceb2c22810d95

See more details on using hashes here.

File details

Details for the file tfa_nightly-0.15.0.dev20211110010902-cp39-cp39-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for tfa_nightly-0.15.0.dev20211110010902-cp39-cp39-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 dcd6aa710ea256fef8c54390c65e08def37381167bce0d548847cbbf7ccd5daf
MD5 23d30098cede2366cc0394b3ed01064c
BLAKE2b-256 00f51049f3f2d6ae45c7ffe5575a4b8e676f2311412cea54333dd838b70b5a6e

See more details on using hashes here.

File details

Details for the file tfa_nightly-0.15.0.dev20211110010902-cp39-cp39-macosx_10_13_x86_64.whl.

File metadata

File hashes

Hashes for tfa_nightly-0.15.0.dev20211110010902-cp39-cp39-macosx_10_13_x86_64.whl
Algorithm Hash digest
SHA256 54003288e9b54f3c4ad84d761aeb02dcf4f8ceee9a3839f576ac8ce91716a929
MD5 7ecd352ad65eece23fc5f286324c17c7
BLAKE2b-256 5a3aaa78446bf9f394fe9b0ad40947fd3b1daecb3d00810d8281319598adbdf5

See more details on using hashes here.

File details

Details for the file tfa_nightly-0.15.0.dev20211110010902-cp38-cp38-win_amd64.whl.

File metadata

  • Download URL: tfa_nightly-0.15.0.dev20211110010902-cp38-cp38-win_amd64.whl
  • Upload date:
  • Size: 756.6 kB
  • Tags: CPython 3.8, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.5.0 importlib_metadata/4.8.2 pkginfo/1.7.1 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.7.12

File hashes

Hashes for tfa_nightly-0.15.0.dev20211110010902-cp38-cp38-win_amd64.whl
Algorithm Hash digest
SHA256 77229983c2a535a34b7ef776c60980ae569053b437035b5ff20ee695da35b4ad
MD5 44df9368a73cde45f168c1d0659e16d8
BLAKE2b-256 2eb5aefe3c4e2a4e93836721ad5852af3733232bbc59e2aa58dda76ea64dfc1c

See more details on using hashes here.

File details

Details for the file tfa_nightly-0.15.0.dev20211110010902-cp38-cp38-manylinux_2_12_x86_64.manylinux2010_x86_64.whl.

File metadata

File hashes

Hashes for tfa_nightly-0.15.0.dev20211110010902-cp38-cp38-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
Algorithm Hash digest
SHA256 61ff5590838edd8b2107894ba3dee524d563a65409ff180f2ac3ff30f87fab20
MD5 775e02a579f6b80f77f17615488b8641
BLAKE2b-256 0d60635e27346bf48138e8b25809490f16d447e233d3c96f7cbbbb064e3602ae

See more details on using hashes here.

File details

Details for the file tfa_nightly-0.15.0.dev20211110010902-cp38-cp38-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for tfa_nightly-0.15.0.dev20211110010902-cp38-cp38-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 6eca1c8d131c949dd5a027657ee463a8cb15c4264035b676e0d90d9af4f558dd
MD5 92b53908c7c2dc9ed5474c54ae9783e5
BLAKE2b-256 c85182b14dba84dd142db4551c5bb373bf3c3880bbcb69909f9d7c860d81ff5a

See more details on using hashes here.

File details

Details for the file tfa_nightly-0.15.0.dev20211110010902-cp38-cp38-macosx_10_13_x86_64.whl.

File metadata

File hashes

Hashes for tfa_nightly-0.15.0.dev20211110010902-cp38-cp38-macosx_10_13_x86_64.whl
Algorithm Hash digest
SHA256 6f2b5a93c7a91506d09cd822b06bf63b322ca6db433611aeefbb31787288a2f3
MD5 cfaccd99a3b636bcec6bf68f92e193c6
BLAKE2b-256 8c0410ac105d53c5a4ca1f14acc7e506ed8f9df39ab20c93d5a8119cc97e4692

See more details on using hashes here.

File details

Details for the file tfa_nightly-0.15.0.dev20211110010902-cp37-cp37m-win_amd64.whl.

File metadata

  • Download URL: tfa_nightly-0.15.0.dev20211110010902-cp37-cp37m-win_amd64.whl
  • Upload date:
  • Size: 756.6 kB
  • Tags: CPython 3.7m, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.5.0 importlib_metadata/4.8.2 pkginfo/1.7.1 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.7.12

File hashes

Hashes for tfa_nightly-0.15.0.dev20211110010902-cp37-cp37m-win_amd64.whl
Algorithm Hash digest
SHA256 78fd878df71324c288c9615284bac5eb665979a5e7fc4c5ae06f36aec94fd21f
MD5 bd6f4797a07197b752f1d2e57d62d791
BLAKE2b-256 e65ef7bcd7f3833f7b140484fbfb5d38ccce12869c398d880b768c5194e396cb

See more details on using hashes here.

File details

Details for the file tfa_nightly-0.15.0.dev20211110010902-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl.

File metadata

File hashes

Hashes for tfa_nightly-0.15.0.dev20211110010902-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
Algorithm Hash digest
SHA256 4975c89816383e73f00febdf4e809b61ec6f5c22813b916ea7395bafdff7e223
MD5 b1a062aa1f69e999caff4254b4657e3c
BLAKE2b-256 a25c0c8a3d4f8fc57c4b3d7542f60409da9857491d8f695fb2f9b6add0bd143e

See more details on using hashes here.

File details

Details for the file tfa_nightly-0.15.0.dev20211110010902-cp37-cp37m-macosx_10_13_x86_64.whl.

File metadata

File hashes

Hashes for tfa_nightly-0.15.0.dev20211110010902-cp37-cp37m-macosx_10_13_x86_64.whl
Algorithm Hash digest
SHA256 f782d3b2d22c22707ad0be63c8d0d8b8f0f21360faba5d950f313b9d78d6dd0f
MD5 fa64a7daf4e82ecc25f71631261e93ca
BLAKE2b-256 35bb20bf80c8e63cfec478b89e781eb4502afece4a3ff6c03bc9c9e295f76791

See more details on using hashes here.

Supported by

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