RAdam | On the Variance of the Adaptive Learning Rate and Beyond | Machine Learning library

by LiyuanLucasLiu Python Version: Current License: Apache-2.0

X-Ray Key Features Code Snippets(3)Community Discussions(2)Vulnerabilities Install Support

kandi X-RAY | RAdam Summary

RAdam is a Python library typically used in Institutions, Learning, Education, Artificial Intelligence, Machine Learning, Deep Learning, Pytorch, Tensorflow applications. RAdam has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has medium support. You can download it from GitHub.

The learning rate warmup for Adam is a must-have trick for stable training in certain situations (or eps tuning). But the underlying mechanism is largely unknown. In our study, we suggest one fundamental cause is the large variance of the adaptive learning rates, and provide both theoretical and empirical support evidence. In addition to explaining why we should use warmup, we also propose RAdam, a theoretically sound variant of Adam.

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RAdam has a medium active ecosystem.

It has 2491 star(s) with 340 fork(s). There are 57 watchers for this library.

It had no major release in the last 6 months.

There are 11 open issues and 38 have been closed. On average issues are closed in 30 days. There are 1 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of RAdam is current.

Quality

RAdam has 0 bugs and 0 code smells.

Security

RAdam has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

RAdam code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

RAdam is licensed under the Apache-2.0 License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

RAdam releases are not available. You will need to build from source code and install.

Build file is available. You can build the component from source.

Installation instructions, examples and code snippets are available.

RAdam saves you 1534 person hours of effort in developing the same functionality from scratch.

It has 3417 lines of code, 227 functions and 44 files.

It has medium code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed RAdam and discovered the below as its top functions. This is intended to give you an instant insight into RAdam implemented functionality, and help decide if they suit your requirements.

Train model
Compute accuracy
Append a list of numbers to the file
Updates the sum
Show the masks
Make an image
Calculate the batch norm
Check that the input tensor is correct
Forward computation
Forward RNN
Average checkpointpoints
Compute the log probability of a word embedding
Creates a directory
Initialize random infom
Colorize x
Construct a dense block
Construct the index
Sets the names
Create a layer of a block layer
Perform a forward pass through the layer
Generate a dataset
Create a layer
Encodes a dataset
Visualize a single image
Evaluate the model
Find the last n checkpoint files

Get all kandi verified functions for this library.

RAdam Key Features

No Key Features are available at this moment for RAdam.

RAdam Examples and Code Snippets

Optimization,Motivation for Nadam and Radam,Incorporating Nesterov Momentum into Adam

Python

Lines of Code : 26

License : No License

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g_t = grads(loss, x_tm1 - mu*m_tm1) ###
m_t = mu*m_tm1 + lr*g_t
x_t = x_tm1 - m_t

x_hat_t = x_tm1 - mu*m_tm1 ###
g_t = grads(loss, x_hat_t) ###
m_t = mu*m_tm1 + lr*g_t
x_t = x_tm1 - m_t

x_hat_t = x_tm1 - mu*m_tm1
g_t = grads(loss, x_hat_t)
m_t = mu

Optimization,Motivation for Nadam and Radam,Why Adam is the coolest first order algorithm there is

Python

Lines of Code : 14

License : No License

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m_t = mu*m_tm1 + lr_t*g_t
x_t = x_tm1 - m_t  

m_t = mu*m_tm1 + (1-mu)*g_t
m_hat_t = m_t / (1-mu**t)
x_t = x_tm1 = lr_t * m_hat_t

v_t = v_tm1 + g_t**2
x_t = x_tm1 - g_t / sqrt(v_t + eps)

v_t = ups*v_tm1 + (1-ups)*g_t**2
x_t = x_tm1 - g_t / sqrt(v_t

DemonRangerOptimizer,Recover RAdam

Python

Lines of Code : 14

License : No License

Copy

optimizer = DemonRanger(params=model.parameters(),
                        lr=config.lr,
                        betas=(0.9,0.999,0.999), # restore default AdamW betas
                        nus=(1.0,1.0), # disables QHMomentum

Community Discussions

Trending Discussions on RAdam

How to solve a type error while using RAdam optimizer?

How to import tensorflow in google colab

QUESTION

How to solve a type error while using RAdam optimizer?

Asked 2021-Apr-28 at 19:08

I am building a neural network using keras and tensorflow and I get a error at this place

...

ANSWER

Answered 2021-Apr-28 at 19:08

For others who may be looking for another solution.

RAdam is not in tensorflow.keras.optimizers and neither in keras by default, but in tensorflow-addons package, which is a better alternative (IMHO) than the external keras_radam library, considerably less prone to errors.

What you are looking for is here: https://www.tensorflow.org/addons/api_docs/python/tfa/optimizers/RectifiedAdam

Source https://stackoverflow.com/questions/67231257

QUESTION

How to import tensorflow in google colab

Asked 2020-Aug-02 at 08:57

Google Colab seems throwing the below error while trying to import Tensorflow, while it was working okey couple of weeks ago

...

ANSWER

Answered 2020-Jul-26 at 14:26

This should suffice i feel %tensorflow_version 2.x import tensorflow as tf

This has always worked for me in Google Colab. I think the issue is that you are giving %tensorflow_version as 1.x please try changing that to 2.x

Source https://stackoverflow.com/questions/63100247

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install RAdam

Note that in our paper, our major contribution is to identify why we need the warmup for Adam. Although some researchers successfully improve their model performance (user comments), considering the difficulty of training NNs, directly plugging in RAdam may not result in an immediate performance boost. Based on our experience, replacing the vanilla Adam with RAdam usually results in a better performance; however, if warmup has already been employed and tuned in the baseline method, it is necessary to also tune hyper-parameters for RAdam.
Directly replace the vanilla Adam with RAdam without changing any settings.
Further tune hyper-parameters (including the learning rate) for a better performance.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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