tensorflow-fcn | An Implementation of Fully Convolutional Networks | Machine Learning library

by MarvinTeichmann Python Version: Current License: MIT

X-Ray Key Features Code Snippets(2)Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | tensorflow-fcn Summary

tensorflow-fcn is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Tensorflow applications. tensorflow-fcn 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.

An Implementation of Fully Convolutional Networks in Tensorflow.

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tensorflow-fcn has a medium active ecosystem.

It has 1112 star(s) with 437 fork(s). There are 55 watchers for this library.

It had no major release in the last 6 months.

There are 22 open issues and 30 have been closed. On average issues are closed in 22 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of tensorflow-fcn is current.

Quality

tensorflow-fcn has 0 bugs and 0 code smells.

Security

tensorflow-fcn has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

tensorflow-fcn code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

tensorflow-fcn 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

tensorflow-fcn 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.

tensorflow-fcn saves you 421 person hours of effort in developing the same functionality from scratch.

It has 997 lines of code, 55 functions and 9 files.

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

Top functions reviewed by kandi - BETA

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

Build the convolution layer
Upscore layer
Create a bias variable
Adds a weight to a variable
Compute max pool
Get a bias tensor
Get layer weight reshaping
Convolution layer
Reshape the bias matrix
Create summaries for a variable
Get a convolution filter
Summarize activations
Get the decay filter
Score layer
Create a variable with weight decay
Layer layer
Reshape the average fweight
Color an image

Get all kandi verified functions for this library.

tensorflow-fcn Key Features

No Key Features are available at this moment for tensorflow-fcn.

tensorflow-fcn Examples and Code Snippets

tensorflow-fcn,Requirements,Tensorflow 1.0rc

Python

Lines of Code : 2

License : Permissive (MIT)

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export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.0.0rc0-cp27-none-linux_x86_64.whl
pip install --upgrade $TF_BINARY_URL

tensorflow-fcn,Usage

Python

Lines of Code : 2

License : Permissive (MIT)

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vgg = vgg16.Vgg16()
vgg.build(images, train=True, num_classes=num_classes, random_init_fc8=True)

Community Discussions

Trending Discussions on tensorflow-fcn

Tensorflow - Avoid Tensor Size Limit

QUESTION

Tensorflow - Avoid Tensor Size Limit

Asked 2018-Apr-12 at 14:57

I'm working on an implementation of the FCN-32 net described in the Long, Shelhamer paper, but have run into a roadblock when upsampling. In order to upsample to original size, other implementations use a conv2d_transpose layer with a bilinear filter w/kernel size 64x64. This works fine until you start using lots of classes.

For any number of classes > ~375, the filters variable in the transpose layer is > 2 gb ( 64 x 64 x (>375) x (>375) ) so Tensorflow complains and dies, saying

ValueError: Cannot create a tensor proto whose content is larger than 2GB.

Is there any way to avoid this size limit? My first thought would be generative tensor, but I can't find any documentation on how to create if such a construct exists or is possible.

...

ANSWER

Answered 2018-Apr-11 at 21:11

You can split the output classes into multiple operations and concatenate them at the end.

Backprop will work just fine through the concat operation. It should be as trivial as creating two conv2d_transpose operations, each with half the classes and concat the results appropriately and continue to the loss function from there.

Creating more than 2 conv2d_transpose operations as necessary will work just as well.

After thinking about this I'm confident it will work. If there's an issue let me know and I'll update the answer.

Source https://stackoverflow.com/questions/49782521

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

Vulnerabilities

No vulnerabilities reported

Install tensorflow-fcn

Installing matplotlib from pip requires the following packages to be installed libpng-dev, libjpeg8-dev, libfreetype6-dev and pkg-config. On Debian, Linux Mint and Ubuntu Systems type:.

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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