Keras RAdam
Unofficial implementation of RAdam in Keras and TensorFlow.
Install
pip install keras-rectified-adam
External Link
Usage
import keras
import numpy as np
from keras_radam import RAdam
# Build toy model with RAdam optimizer
model = keras.models.Sequential()
model.add(keras.layers.Dense(input_shape=(17,), units=3))
model.compile(RAdam(), loss='mse')
# Generate toy data
x = np.random.standard_normal((4096 * 30, 17))
w = np.random.standard_normal((17, 3))
y = np.dot(x, w)
# Fit
model.fit(x, y, epochs=5)
TensorFlow without Keras
from keras_radam.training import RAdamOptimizer
RAdamOptimizer(learning_rate=1e-3)
Use Warmup
from keras_radam import RAdam
RAdam(total_steps=10000, warmup_proportion=0.1, min_lr=1e-5)
Q & A
About Correctness
The optimizer produces similar losses and weights to the official optimizer after 500 steps.
Use tf.keras or tf-2.0
Add TF_KERAS=1 to environment variables to use tensorflow.python.keras.
Use theano Backend
Add KERAS_BACKEND=theano to environment variables to enable theano backend.
1 maintainer
Metadata
1 maintainer
Release files for keras-radam 0.15.0
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 | |
|---|---|---|---|---|
| keras-radam-0.15.0.tar.gz | 11.7 kB | Without provenance | Details |
Release files / keras-radam-0.15.0.tar.gz
| Download URL | keras-radam-0.15.0.tar.gz |
|---|---|
| Size | 11.7 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
4a0bbcaa3bcd20e0a7a05192a15fcf490fe26f4cfc677b65a056155b866d58e1
|
|
BLAKE2b-256 checksum How to use checksums |
468db83ccaa94253fbc920b21981f038393041d92236bb541751b98a66a2ac1d
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/1.13.0 pkginfo/1.5.0.1 requests/2.22.0 setuptools/41.0.1 requests-toolbelt/0.9.1 tqdm/4.32.1 CPython/3.7.3
|
Security for keras-radam 0.15.0
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.
