gpkg.mnist
CNN and multinomial logistic regression classifiers for MNIST digits (Guild AI)
Models
cnn
CNN classifier for MNIST
Operations
evaluate
Evaluate a trained CNN
train
Train the CNN
Flags
- batch-size
Number of images to include in a training batch (default is 100)
- epochs
Number of epochs to train (default is 5)
- learning-rate
Learning rate (default is 0.0001)
logreg
Multinomial logistic regression classifier for MNIST
Operations
evaluate
Evaluate a trained logistic regression
train
Train the logistic regression
Flags
- batch-size
Number of images to include in a training batch (default is 100)
- epochs
Number of epochs to train (default is 5)
- learning-rate
Learning rate (default is 0.5)
samples
Sample MNIST images
Operations
prepare
Generate a set of sample MNIST images
Flags
- count
Number of images to generate (default is 100)
_check
Operations
all
cnn
logreg
Metadata
Release files for gpkg.mnist 0.6.1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Actions Reset |
|---|---|---|---|---|
| gpkg.mnist-0.6.1-py2.py3-none-any.whl | Python 2, Python 3 | none | any | Details for gpkg.mnist-0.6.1-py2.py3-none-any.whl |
Release files / gpkg.mnist-0.6.1-py2.py3-none-any.whl
| Download URL | gpkg.mnist-0.6.1-py2.py3-none-any.whl |
|---|---|
| Size | 10.1 kB |
| Tags | Python 2 Python 3 |
|
SHA-256 checksum How to use checksums |
9ceb9220e71e5331fd7912a2977a67bdcb973c95f470552e6f386dce047bdaf6
|
|
BLAKE2b-256 checksum How to use checksums |
b969afb02d146fb4802b63e96150874efdb3e48da50686b9437befaa1f6765c2
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/1.15.0 pkginfo/1.7.0 requests/2.25.1 setuptools/44.1.1 requests-toolbelt/0.9.1 tqdm/4.59.0 CPython/2.7.18
|
Security for gpkg.mnist 0.6.1
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.