tensorflow-vgg | VGG19 and VGG16 on Tensorflow | Machine Learning library

by machrisaa Python Version: Current License: No License

X-Ray Key Features Code Snippets(3)Community Discussions(2)Vulnerabilities Install Support

kandi X-RAY | tensorflow-vgg Summary

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

This is a Tensorflow implemention of VGG 16 and VGG 19 based on tensorflow-vgg16 and Caffe to Tensorflow. Original Caffe implementation can be found in here and here. We have modified the implementation of tensorflow-vgg16 to use numpy loading instead of default tensorflow model loading in order to speed up the initialisation and reduce the overall memory usage. This implementation enable further modify the network, e.g. remove the FC layers, or increase the batch size. To use the VGG networks, the npy files for VGG16 NPY or VGG19 NPY has to be downloaded.

Support

Quality

Security

License

Reuse

Support

tensorflow-vgg has a medium active ecosystem.

It has 2149 star(s) with 1094 fork(s). There are 68 watchers for this library.

It had no major release in the last 6 months.

There are 18 open issues and 50 have been closed. On average issues are closed in 63 days. There are 3 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of tensorflow-vgg is current.

Quality

tensorflow-vgg has 0 bugs and 0 code smells.

Security

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

tensorflow-vgg code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

tensorflow-vgg 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.

Reuse

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

tensorflow-vgg 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.

tensorflow-vgg saves you 162 person hours of effort in developing the same functionality from scratch.

It has 402 lines of code, 33 functions and 8 files.

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

Top functions reviewed by kandi - BETA

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

Build the convolution layer
Creates a fully connected layer
Convolution layer
Get the convolutional convolution layer
Get weights and biases
Get bias tensor
Get convolution filter
Gets weight for a given variable
A max pool
Get a tf Variable
Build the model
Gets weight
Max pooling
Get a convolution filter
A fully connected layer
Load image
Saves data to a npy file
Print the top1 label of a file
Creates a test image
Get the number of variables

Get all kandi verified functions for this library.

tensorflow-vgg Key Features

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

tensorflow-vgg Examples and Code Snippets

Tensorflow-VGG-face,Result

Python

Lines of Code : 9

License : No License

Copy

Classification Result:
        Category Name: Aamir_Khan
        Propbability: 51.60%
        
        Category Name: Adam_Driver
        Propbability: 6.78%
        
        Category Name: Manish_Dayal
        Propbability: 1.95%

Real-Time-Style-Transfer

Python

Lines of Code : 5

License : No License

Copy

-Python 3.5
-Tensorflow 0.12
-Numpy
-PILLOW
-Scipy

Train your own network on COCO

Python

Lines of Code : 3

License : No License

Copy

[{ "file_path": "path/img.jpg", "captions": ["a caption", "a second caption of i"tgit ...] }, ...]

$ python prepro.py --input_json coco/coco_raw.json --num_val 5000 --num_test 5000 --images_root coco/images --word_count_threshold 5 --output_json coc

Community Discussions

Trending Discussions on tensorflow-vgg

Tensor' object has no attribute 'is_initialized' in keras vgg16 when using it in tensorflow 1.14 with mixed precision training

Tensorflow model works in Python but not in C++

QUESTION

Tensor' object has no attribute 'is_initialized' in keras vgg16 when using it in tensorflow 1.14 with mixed precision training

Asked 2020-Feb-04 at 20:59

Let me start from the beggining. I'm implementing in tensorflow 1.14 a partial convolution layer for image inpainting based on the not official Keras implementation (I already test it and it works on my dataset).

This architecture uses a pretrained (imagenet) VGG16 to compute some loss terms. Sadly, a VGG implemented in tensorflow didn't worked (I've tried with this one), as the one in keras application. Therefore, I used this class to incorporate the keras application VGG16 into my tensorflow 1.14 code.

Everything was working fine but then I incorporate Mixed Precision Training (documentation) into my code and the VGG16 part gave the following error:

...

ANSWER

Answered 2020-Feb-04 at 20:59

I've try many ways and my final thought is that pre trained keras models are not compatible. I changed it to a tensorflow VGG16 model and it works slower but at least it works.

Source https://stackoverflow.com/questions/58697001

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-vgg

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

Find more information at: