Ranger-Deep-Learning-Optimizer | synergistic optimizer using RAdam

by lessw2020 Python Version: Current License: Apache-2.0

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

kandi X-RAY | Ranger-Deep-Learning-Optimizer Summary

Ranger-Deep-Learning-Optimizer is a Python library. Ranger-Deep-Learning-Optimizer 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.

Ranger - a synergistic optimizer using RAdam (Rectified Adam), Gradient Centralization and LookAhead in one codebase

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Quality

Security

License

Reuse

Support

Ranger-Deep-Learning-Optimizer has a medium active ecosystem.

It has 1108 star(s) with 173 fork(s). There are 37 watchers for this library.

It had no major release in the last 6 months.

There are 19 open issues and 24 have been closed. On average issues are closed in 47 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of Ranger-Deep-Learning-Optimizer is current.

Quality

Ranger-Deep-Learning-Optimizer has 0 bugs and 0 code smells.

Security

Ranger-Deep-Learning-Optimizer has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

Ranger-Deep-Learning-Optimizer code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

Ranger-Deep-Learning-Optimizer 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

Ranger-Deep-Learning-Optimizer 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.

It has 411 lines of code, 14 functions and 6 files.

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

Top functions reviewed by kandi - BETA

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

Performs a single step
Provide a centralized gradient
Read file contents

Get all kandi verified functions for this library.

Ranger-Deep-Learning-Optimizer Key Features

No Key Features are available at this moment for Ranger-Deep-Learning-Optimizer.

Ranger-Deep-Learning-Optimizer Examples and Code Snippets

SC20 Deep Learning at Scale Tutorial,Performance profiling and optimization

Python

Lines of Code : 23

License : No License

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INFO - Starting Training Loop...
INFO - Epoch: 1, Iteration: 0, Avg img/sec: 110.19908073510402
INFO - Epoch: 1, Iteration: 20, Avg img/sec: 680.8613838734273
INFO - Epoch: 1, Iteration: 40, Avg img/sec: 682.4229819820212
INFO - Epoch: 1, Iteration:

Relative gradient optimization of the Jacobian term in unsupervised deep learning,Training

Python

Lines of Code : 23

License : No License

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$ cd experiments
$ python train.py 

--epochs           number of epochs of training
--batch_size       size of the batches
--lr               adam: learning rate
--log_every        interval between logs
--seed             random generator seed
--dat

deep-learning,Usage

Python

Lines of Code : 18

License : Permissive (MIT)

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# Start a TensorFlow session
sess = tf.Session()

# Initialize an unconfigured autoencoder with specified dimensions, etc.
sda = SDAutoencoder(dims=[784, 256, 64, 32],
                    activations=["sigmoid", "tanh", "sigmoid"],

Community Discussions

No Community Discussions are available at this moment for Ranger-Deep-Learning-Optimizer.Refer to stack overflow page for discussions.

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

Vulnerabilities

No vulnerabilities reported

Install Ranger-Deep-Learning-Optimizer

Clone the repo, cd into it and install it in editable mode (-e option). That way, these is no more need to re-install the package after modification.

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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