VDCNN | tensorflow implementation of Very Deep Convolutional | Machine Learning library

by WenchenLi Python Version: Current License: No License

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

kandi X-RAY | VDCNN Summary

VDCNN is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Tensorflow, Keras applications. VDCNN has no bugs, it has no vulnerabilities, it has build file available and it has low support. You can download it from GitHub.

tensorflow implementation of Very Deep Convolutional Networks for Text Classification.

Support

Quality

Security

License

Reuse

Support

VDCNN has a low active ecosystem.

It has 5 star(s) with 1 fork(s). There are 1 watchers for this library.

It had no major release in the last 6 months.

There are 2 open issues and 0 have been closed. On average issues are closed in 1027 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of VDCNN is current.

Quality

VDCNN has no bugs reported.

Security

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

License

VDCNN 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

VDCNN 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 are not available. Examples and code snippets are available.

Top functions reviewed by kandi - BETA

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

Builds the model
Batch normalization
Get a global variable
Convolution layer
2nd convolution layer
Unit convolution block
Max pooling op

Get all kandi verified functions for this library.

VDCNN Key Features

No Key Features are available at this moment for VDCNN.

VDCNN Examples and Code Snippets

No Code Snippets are available at this moment for VDCNN.

Community Discussions

Trending Discussions on VDCNN

Keras (Tensorflow) - name array_ops not defined

QUESTION

Keras (Tensorflow) - name array_ops not defined

Asked 2018-Feb-22 at 13:40

I'm having an issue with Keras/Tensorflow deserializing a model. Basically this is an implementation of a convolutional neural network on text, which requires a dimension to be added at an early stage. The error message is this:

File "/usr/lib/python3.6/site-packages/tensorflow/python/keras/_impl/keras/backend.py", line 2231, in expand_dims NameError: name 'array_ops' is not defined

The code causing this error message:

...

ANSWER

Answered 2018-Feb-22 at 13:40

The issue was this: https://github.com/keras-team/keras/issues/8123#issuecomment-354857044

On top of that, it required reinstalling everything on all machines and using keras directly instead of tf.keras to get the proper error message, apparently because of how Keras uses object serialization and the way Python "tracebacks" work.

Source https://stackoverflow.com/questions/48869563

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

Vulnerabilities

No vulnerabilities reported

Install VDCNN

You can download it from GitHub.
You can use VDCNN 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: