xlearn | Deep learning toolbox for x-ray imaging | Machine Learning library

by tomography Python Version: Current License: Non-SPDX

X-Ray Key Features Code Snippets Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | xlearn Summary

xlearn is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Pytorch applications. xlearn has no bugs, it has no vulnerabilities, it has build file available and it has low support. However xlearn has a Non-SPDX License. You can download it from GitHub.

Deep learning toolbox for x-ray imaging

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Quality

Security

License

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Support

xlearn has a low active ecosystem.

It has 34 star(s) with 34 fork(s). There are 17 watchers for this library.

It had no major release in the last 6 months.

There are 3 open issues and 6 have been closed. On average issues are closed in 2 days. There are 1 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of xlearn is current.

Quality

xlearn has 0 bugs and 0 code smells.

Security

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

xlearn code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

xlearn has a Non-SPDX License.

Non-SPDX licenses can be open source with a non SPDX compliant license, or non open source licenses, and you need to review them closely before use.

Reuse

xlearn 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.

xlearn saves you 586 person hours of effort in developing the same functionality from scratch.

It has 1368 lines of code, 64 functions and 18 files.

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

Top functions reviewed by kandi - BETA

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

Wrapper for segment prediction
Returns a transformer for the given tensor
Get parameters for segmentation
Extract patches from an image
Predict a single image
2D transformer
Perform DCAN
A discriminator layer
Return dictionary of kwargs
Convert totomo
Reconstruct reconstruction
Create the keras model
Create the discriminator layer
Performs a rec_dcans on the given image
Crops an image
Segmentation
Extracts patches from an image
Computes the cost function for a given model
Reconfigure the model
Generate a random image
Train the model
Perform a recon step filter
Performs a recon step filter
Runs the prediction on the given image
Extract patches of an image
Normalize an image
Logarithm of an array
Expand an image

Get all kandi verified functions for this library.

xlearn Key Features

No Key Features are available at this moment for xlearn.

xlearn Examples and Code Snippets

No Code Snippets are available at this moment for xlearn.

Community Discussions

Trending Discussions on xlearn

xlearn predictions error give a different mse than output by the function

QUESTION

xlearn predictions error give a different mse than output by the function

Asked 2021-Feb-10 at 18:00

the xlearn predict function gives a different mse than what you get by looking at the predictions and calculating it yourself. Here is code to do this; you can run it by cloning the xlearn repository and copying the below code in demo/regression/house_price in the repository

...

ANSWER

Answered 2021-Feb-09 at 23:58

A lot of people use 1/2 MSE for the loss because it makes the derivative "easier". Given that they use the word "loss" rather than "MSE" or something like that, I'd bet this is what's going on.

For clarity, if your loss is

1/2n * [(y_1 - p_1)^2 + ... + (y_n - p_n)^2]

then the derivative (wrt p) would be

-1/n * [(y_1 - p_1) + ... + (y_n - p_n)]

The 2 goes away because you end up multiplying by 2 for the power rule.

pardon the formatting... I don't know how to do math stuff here.

Source https://stackoverflow.com/questions/66026339

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

Vulnerabilities

No vulnerabilities reported

Install xlearn

You can download it from GitHub.
You can use xlearn 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.

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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