transferlearning | Transfer learning / domain adaptation | Machine Learning library

by jindongwang Python Version: Current License: MIT

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

kandi X-RAY | transferlearning Summary

transferlearning is a Python library typically used in Institutions, Learning, Education, Artificial Intelligence, Machine Learning, Deep Learning applications. transferlearning has no bugs, it has no vulnerabilities, it has a Permissive License and it has medium support. However transferlearning build file is not available. You can download it from GitHub.

Transfer learning / domain adaptation / domain generalization / multi-task learning etc. Papers, codes, datasets, applications, tutorials.-迁移学习

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transferlearning has a medium active ecosystem.

It has 11556 star(s) with 3647 fork(s). There are 335 watchers for this library.

It had no major release in the last 6 months.

There are 4 open issues and 300 have been closed. On average issues are closed in 7 days. There are 2 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of transferlearning is current.

Quality

transferlearning has 0 bugs and 0 code smells.

Security

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

transferlearning code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

transferlearning is licensed under the MIT License. This license is Permissive.

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

Reuse

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

transferlearning has no build file. You will be need to create the build yourself to build the component from source.

Installation instructions are not available. Examples and code snippets are available.

transferlearning saves you 2378 person hours of effort in developing the same functionality from scratch.

It has 15709 lines of code, 1030 functions and 169 files.

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

Top functions reviewed by kandi - BETA

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

Train a model
Calculates the L2 gradient of a model
Evaluate the model
Computes the L2SP loss
Forward computation
Compute the cuda loss function
Compute the adversarial loss
Implementation of imagenet validation
Returns a list of the names of the model models
Wrapper for get_visual
Calculate the danno methods for a given domain
Compute the predictive prediction for the given data
Train a model for one epoch
Returns a function that computes the loss functions for a given domain
Adds weights to the network
Update intermediate layer
Get argument parser
Train an AdaRNN model
Get prior parameters
Estimate the prediction for the given matrix
Load multilingual data
Load data into tensors
Calculates weight ratio score
Train the model
Adds weights to the model
Recognize and evaluate a trained model
Train a dataset

Get all kandi verified functions for this library.

transferlearning Key Features

No Key Features are available at this moment for transferlearning.

transferlearning Examples and Code Snippets

TransferLearning-Tasks,Citation

Python

Lines of Code : 6

License : No License

Copy

@article{tan2020relative,
  title={RelativeNAS: Relative Neural Architecture Search via Slow-Fast Learning},
  author={Tan, Hao and Cheng, Ran and Huang, Shihua and He, Cheng and Qiu, Changxiao and Yang, Fan and Luo, Ping},
  journal={arXiv preprint

Relative Neural Architecture Search via Slow-Fast Learning,Architecture evaluation (using full-sized models)

Python

Lines of Code : 2

License : No License

Copy

python train.py --auxiliary --cutout --set cifar10

python train_imagenet.py --data_path 'The path of ImageNet lmdb data' --init_channels 46 --layers 14 --arch RelativeNAS --gpus 0,1,2,3

Community Discussions

Trending Discussions on transferlearning

Pytorch features and classes from .npy files

DL4J-Image become too bright

QUESTION

Pytorch features and classes from .npy files

Asked 2022-Jan-31 at 16:06

I am very rookie in moving from TensorFlow to Pytorch. In tensorflow, I can simply load features and labels from separate .npy files and train a CNN using them. It is simple as below:

...

ANSWER

Answered 2022-Jan-31 at 15:42

It seems like you need to create a custom Dataset.

Source https://stackoverflow.com/questions/70925539

QUESTION

DL4J-Image become too bright

Asked 2021-Oct-14 at 08:21

Currently, I've been asked to write CNN code using DL4J using YOLOv2 architecture. But the problem is after the model has complete, I do a simple GUI for validation testing then the image shown is too bright and sometimes the image can be displayed. Im not sure where does this problem comes from whether at earliest stage of training or else. Here, I attach the code that I have for now. For Iterator:

...

ANSWER

Answered 2021-Oct-14 at 08:01

CanvasFrame tries to do gamma correction by default because it's typically needed by cameras used for CV, but cheap webcams usually output gamma corrected images, so make sure to let CanvasFrame know about it this way:

Source https://stackoverflow.com/questions/69566911

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

Vulnerabilities

No vulnerabilities reported

Install transferlearning

You can download it from GitHub.
You can use transferlearning like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.