Skip to main content

ModelManager

Wrapper for keras models to ensure logging and reproducibility

import keras
from keras.models import Sequential

from ModelManager import ModelManager

# Create some simple model
my_model = Sequential()
my_model.add(Dense(2, input_dim=1, activation='relu'))
my_model.add(Dense(1, activation='sigmoid'))
my_model.compile(optimizer=keras.optimizers.Adam(learning_rate=0.01), loss='categorical_crossentropy')

# Some data
x = [1, 2, 3, 4, 5]
y = [1, 2, 3, 4, 5]

mm = ModelManager("some/path", model=my_model, save_history=True, save_weights=True, save_model=True)

mm.fit(x=x, y=y, batch_size=1, epochs=3)

Metadata

Release files for KerasModelManager 0.0.10

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for KerasModelManager 0.0.10
File Size Uploaded Provenance Actions
KerasModelManager-0.0.10.tar.gz 4.9 kB Without provenance Details for KerasModelManager-0.0.10.tar.gz

Built distribution (wheel)

Table of built distributions (wheels) for KerasModelManager 0.0.10
File Interpreter ABI Platform Actions
KerasModelManager-0.0.10-py3-none-any.whl Python 3 none any Details for KerasModelManager-0.0.10-py3-none-any.whl

Total release size: 10.5 kB

Release files / KerasModelManager-0.0.10.tar.gz

Download URL KerasModelManager-0.0.10.tar.gz
Size 4.9 kB
Tags Source
SHA-256 checksum
How to use checksums
03fc607713150e76fb784a58fffc1ceb3dd59df2e90a6222c5cb97ea70616d0b
BLAKE2b-256 checksum
How to use checksums
07b9c030b2830aba8603e1571ff0043ecf1ba2901624baf132f6acab609683ec
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/3.7.1 importlib_metadata/4.8.2 pkginfo/1.8.2 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.9.9

Release files / KerasModelManager-0.0.10-py3-none-any.whl

Download URL KerasModelManager-0.0.10-py3-none-any.whl
Size 5.7 kB
Tags Python 3
SHA-256 checksum
How to use checksums
6a6bb7c2a5f194bccdcfdf0ec92bd2fae24216335a852c7732778af3d1afd3a2
BLAKE2b-256 checksum
How to use checksums
afb207e1f757a1bb18cedb242cfc42f83da6de2e389a1580153fcda13836c5e2
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/3.7.1 importlib_metadata/4.8.2 pkginfo/1.8.2 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.9.9

Security for KerasModelManager 0.0.10

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.

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page