DGCNN | Dilation Gate CNN For Machine Reading Comprehension | Natural Language Processing library
kandi X-RAY | DGCNN Summary
kandi X-RAY | DGCNN Summary
DGCNN is a Python library typically used in Artificial Intelligence, Natural Language Processing, Tensorflow applications. DGCNN has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can download it from GitHub.
Dilation Gate CNN For Machine Reading Comprehension
Dilation Gate CNN For Machine Reading Comprehension
Support
Quality
Security
License
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Support
DGCNN has a low active ecosystem.
It has 16 star(s) with 9 fork(s). There are no watchers for this library.
It had no major release in the last 6 months.
There are 0 open issues and 1 have been closed. On average issues are closed in 51 days. There are no pull requests.
It has a neutral sentiment in the developer community.
The latest version of DGCNN is current.
Quality
DGCNN has 0 bugs and 0 code smells.
Security
DGCNN has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
DGCNN code analysis shows 0 unresolved vulnerabilities.
There are 0 security hotspots that need review.
License
DGCNN is licensed under the MIT License. This license is Permissive.
Permissive licenses have the least restrictions, and you can use them in most projects.
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DGCNN 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.
DGCNN saves you 528 person hours of effort in developing the same functionality from scratch.
It has 1238 lines of code, 88 functions and 12 files.
It has high code complexity. Code complexity directly impacts maintainability of the code.
Top functions reviewed by kandi - BETA
kandi has reviewed DGCNN and discovered the below as its top functions. This is intended to give you an instant insight into DGCNN implemented functionality, and help decide if they suit your requirements.
- Transformer model for Transformer
- Attention layer
- Get the shape of a tensor
- Apply dropout
- Embedding postprocessor
- Apply layer norm and dropout
- Create an optimizer
- Apply gradients
- Return True if weight decay rate decay is used
- Extract the variable name from a variable name
- Tokenize text
- Convert to unicode
- R Split text into tokens
- Embed word embedding
- Trains a single tower
- Average gradients
- Calculate the loss
- Creates attention_mask_from_tensor
- Save variable specs info
- Train a single model
- Evaluate the prediction
- Get a batch from input file
- Concatenate inputs
- Load a vocabulary from a file
- Imports a tensorflow module
- Reads from a json file
Get all kandi verified functions for this library.
DGCNN Key Features
No Key Features are available at this moment for DGCNN.
DGCNN Examples and Code Snippets
No Code Snippets are available at this moment for DGCNN.
Community Discussions
Trending Discussions on DGCNN
QUESTION
Stellargraph failing to work with data shuffle
Asked 2021-Jun-08 at 16:23
when I ran the StellarGraph's demo on graph classification using DGCNNs, I got the same result as in the demo.
However, when I tested what happens when I first shuffle the data using the following code:
...ANSWER
Answered 2021-Jun-08 at 16:23I found the problem. It wasn't anything to do with the shuffling algorithm, nor with StellarGraph's implementation. The problem was in the demo, at the following lines:
QUESTION
tf.gather with indices of higher dimention than input data?
Asked 2018-Dec-02 at 21:09
Reading Dynamic Graph CNN for Learning on Point Clouds code, I came across this snippet:
...ANSWER
Answered 2018-Dec-02 at 21:09An equivalent function in numpy is np.take, a simple example:
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install DGCNN
You can download it from GitHub.
You can use DGCNN 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.
You can use DGCNN 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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