tensorflow-vgg16 | conversation of caffe vgg16 model to tensorflow | Machine Learning library

by ry Python Version: Current License: No License

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kandi X-RAY | tensorflow-vgg16 Summary

tensorflow-vgg16 is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Tensorflow applications. tensorflow-vgg16 has no bugs, it has no vulnerabilities and it has low support. However tensorflow-vgg16 build file is not available. You can download it from GitHub.

conversation of caffe vgg16 model to tensorflow

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tensorflow-vgg16 has a low active ecosystem.

It has 654 star(s) with 289 fork(s). There are 25 watchers for this library.

It had no major release in the last 6 months.

There are 12 open issues and 4 have been closed. On average issues are closed in 12 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of tensorflow-vgg16 is current.

Quality

tensorflow-vgg16 has 0 bugs and 14 code smells.

Security

tensorflow-vgg16 has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

tensorflow-vgg16 code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

tensorflow-vgg16 does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

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

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tensorflow-vgg16 releases are not available. You will need to build from source code and install.

tensorflow-vgg16 has no build file. You will be need to create the build yourself to build the component from source.

tensorflow-vgg16 saves you 98 person hours of effort in developing the same functionality from scratch.

It has 249 lines of code, 23 functions and 4 files.

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

Top functions reviewed by kandi - BETA

kandi has reviewed tensorflow-vgg16 and discovered the below as its top functions. This is intended to give you an instant insight into tensorflow-vgg16 implemented functionality, and help decide if they suit your requirements.

Build the convolution layer
Returns a fully connected layer
Convolution layer
Max pool

Get all kandi verified functions for this library.

tensorflow-vgg16 Key Features

No Key Features are available at this moment for tensorflow-vgg16.

tensorflow-vgg16 Examples and Code Snippets

No Code Snippets are available at this moment for tensorflow-vgg16.

Community Discussions

Trending Discussions on tensorflow-vgg16

Tensorflow model works in Python but not in C++

QUESTION

Tensorflow model works in Python but not in C++

Asked 2017-Feb-08 at 19:56

For a little background, my main goal is to use Tensorflow's C++ API to classify an image and time it on different systems.

I have used Ry's model converter to convert his Caffe model to Tensorflow, and it produces the vgg16.tfmodel file, which appears to be a .pb file, once you open it up.

Using Ry's tf_forward.py to run this resulting file seems to work perfectly, classifying cats, dogs, etc. However, when I modify the label_image example (tensorflow/examples/label_image/) to use my new vgg16.pb file, something appears to go wrong.

Here's the output of classifying the picture of the cat from the tensorflow-vgg16 example:

...

ANSWER

Answered 2017-Feb-08 at 19:56

For anyone looking at this in the future, this problem was caused by using the wrong input layer name.

Source https://stackoverflow.com/questions/41497982

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

Vulnerabilities

No vulnerabilities reported

Install tensorflow-vgg16

You can download it from GitHub.
You can use tensorflow-vgg16 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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