TensorFlow Constrained Optimization (TFCO) is a library for optimizing inequality-constrained problems in TensorFlow.
In the most general case, both the objective function and the constraints are
represented as Tensors, giving users the maximum amount of flexibility in
specifying their optimization problems. Constructing these Tensors can be
cumbersome, so we also provide helper functions to make it easy to construct
constrained optimization problems based on rates, i.e. proportions of the
training data on which some event occurs (e.g. the error rate, true positive
rate, recall, etc).
1 maintainer
Metadata
1 maintainer
Release files for tensorflow-constrained-optimization 0.2
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | Provenance | Actions |
|---|---|---|---|---|
| tensorflow_constrained_optimization-0.2.tar.gz | 115.3 kB | Without provenance | Details for tensorflow_constrained_optimization-0.2.tar.gz |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Actions Reset |
|---|---|---|---|---|
| tensorflow_constrained_optimization-0.2-py2.py3-none-any.whl | Python 2, Python 3 | none | any | Details for tensorflow_constrained_optimization-0.2-py2.py3-none-any.whl |
Total release size: 258.4 kB
Release files / tensorflow_constrained_optimization-0.2.tar.gz
| Download URL | tensorflow_constrained_optimization-0.2.tar.gz |
|---|---|
| Size | 115.3 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
cb432338c36936f141e604e463b29761f2832fe3a0670551b12f18d892939735
|
|
BLAKE2b-256 checksum How to use checksums |
acfe58aa2f613ea2887dbe85dcb38597fbe12020bb09ca4ac95f1326e3041d9d
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/2.0.0 pkginfo/1.4.2 requests/2.21.0 setuptools/41.2.0 requests-toolbelt/0.8.0 tqdm/4.30.0 CPython/3.7.5rc1
|
Release files / tensorflow_constrained_optimization-0.2-py2.py3-none-any.whl
| Download URL | tensorflow_constrained_optimization-0.2-py2.py3-none-any.whl |
|---|---|
| Size | 143.1 kB |
| Tags | Python 2 Python 3 |
|
SHA-256 checksum How to use checksums |
1c9429e40ac6c6104bc0ae1478fc576423294cdd6bdd735065248c7e4e6fc522
|
|
BLAKE2b-256 checksum How to use checksums |
43476d03502850ecd02218a9998250ad9310fbebc7b5353cb2dba54f06180a52
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/2.0.0 pkginfo/1.4.2 requests/2.21.0 setuptools/41.2.0 requests-toolbelt/0.8.0 tqdm/4.30.0 CPython/3.7.5rc1
|
Security for tensorflow-constrained-optimization 0.2
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
No provenance found
None of the files in this release were uploaded with provenance metadata.
A note on package security
PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.
To learn more about protecting yourself from malicious actors, visit our package security documentation.
