caffe-dice-loss-layer | caffe dice loss python/c layer | Machine Learning library

by yihui-he Python Version: Current License: No License

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kandi X-RAY | caffe-dice-loss-layer Summary

caffe-dice-loss-layer is a Python library typically used in Artificial Intelligence, Machine Learning applications. caffe-dice-loss-layer has no bugs, it has no vulnerabilities and it has low support. However caffe-dice-loss-layer build file is not available. You can download it from GitHub.

caffe dice loss python/c++ layer

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caffe-dice-loss-layer has a low active ecosystem.

It has 12 star(s) with 1 fork(s). There are 2 watchers for this library.

It had no major release in the last 6 months.

There are 1 open issues and 1 have been closed. On average issues are closed in 1 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of caffe-dice-loss-layer is current.

Quality

caffe-dice-loss-layer has 0 bugs and 0 code smells.

Security

caffe-dice-loss-layer has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

caffe-dice-loss-layer code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

caffe-dice-loss-layer does not have a standard license declared.

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Without a license, all rights are reserved, and you cannot use the library in your applications.

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caffe-dice-loss-layer releases are not available. You will need to build from source code and install.

caffe-dice-loss-layer has no build file. You will be need to create the build yourself to build the component from source.

caffe-dice-loss-layer saves you 8 person hours of effort in developing the same functionality from scratch.

It has 25 lines of code, 4 functions and 1 files.

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

Top functions reviewed by kandi - BETA

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

Forward computation .
Reshape the difference between two arrays .
r Compute the difference between two vertices .
Sets up the model .

Get all kandi verified functions for this library.

caffe-dice-loss-layer Key Features

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caffe-dice-loss-layer Examples and Code Snippets

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Community Discussions

Trending Discussions on caffe-dice-loss-layer

What is the meaning of 'self.diff' in 'forward' of a custom python loss layer for Caffe training?

QUESTION

What is the meaning of 'self.diff' in 'forward' of a custom python loss layer for Caffe training?

Asked 2017-Mar-29 at 18:07

I try to use a custom python loss layer. When I checked several examples online, such as:

Euclidean loss layer, Dice loss layer,

I notice a variable 'self.diff' is always assigned in 'forward'. Especially for the Dice loss layer,

self.diff[...] = bottom[1].data

I wonder if there is any reason that this variable has to be introduced in forward or I can just use bottom[1].data to access ground truth label?

In addition, what is the point of top[0].reshape(1) in reshape, since by definition in forward, the loss output is a scalar itself.

...

ANSWER

Answered 2017-Mar-29 at 18:07

You need to set the diff attribute of the layer for overall consistency and data communication protocol; it's available other places in the class, and anywhere the loss layer object appears. bottom is a local parameter, and is not available elsewhere in the same form.

In general, the code is expandable for a variety of applications and more complex computations; the reshaping is part of this, ensuring that the returned value is scalar, even if someone expands the inputs to work with vectors or matrices.

Source https://stackoverflow.com/questions/43100664

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

Vulnerabilities

No vulnerabilities reported

Install caffe-dice-loss-layer

You can download it from GitHub.
You can use caffe-dice-loss-layer 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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