mtsa | Experimental Codes for Multi-mask Tensorized Self | Machine Learning library

by taoshen58 Python Version: Current License: No License

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

kandi X-RAY | mtsa Summary

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

Experimental Codes for Multi-mask Tensorized Self-Attention (MTSA)

Support

Quality

Security

License

Reuse

Support

mtsa has a low active ecosystem.

It has 6 star(s) with 1 fork(s). There are 1 watchers for this library.

It had no major release in the last 6 months.

mtsa has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of mtsa is current.

Quality

mtsa has no bugs reported.

Security

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

License

mtsa 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

mtsa releases are not available. You will need to build from source code and install.

mtsa 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.

Top functions reviewed by kandi - BETA

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

Calculate multihead attention
Reshape x
Reshapes the last two dimensions
Combine two tensors
Deepattention layer
Layer layer
Transformer encoder
Compute memory - self - attention
Scaled dot product attention
Train the model
Analyse sst
Self - attention layer
Shift the subtraction of the subtree
Load the training data
Update tensorflow
Analyse sst file
Combine features
Initialize the training
Compute inference time
Constructs a stacking MTSA
Creates a contextual bi - neural network
Convolution layer
Build the network
Self - attention
Reduce a multihead attention layer
Sense dot product attention
Compute dot product attention

Get all kandi verified functions for this library.

mtsa Key Features

No Key Features are available at this moment for mtsa.

mtsa Examples and Code Snippets

No Code Snippets are available at this moment for mtsa.

Community Discussions

Trending Discussions on mtsa

How to override OnItemSelected method in Fragment

QUESTION

How to override OnItemSelected method in Fragment

Asked 2018-Oct-10 at 03:45

I'm trying to override the OnItemSelected method of a Spinner in my Fragment, the same way I did with OnClick on Button, but it's not responding anymore

Inside the OnCreate, the commented block works perfectly, but I want to clean it because I have more than one spinner in this layout and this way it is getting very polluted.

Another question: If possible , how this OnNothingSelected method works and how to use it more effectively?

In the expression "if (itemSelected! =" ... ")" is because the first element of my spinner is a string with "....", so as not to have any item selected at the beginning, I'm using this wonderfull technique... How could I improve this?

My code is as follows:

Vetarano2.kt

...

ANSWER

Answered 2018-Oct-10 at 03:45

As the doc says, if you have multiple spinners, you should judge the id of parent like below, sorry,I just know java.

Source https://stackoverflow.com/questions/52726264

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

Vulnerabilities

No vulnerabilities reported

Install mtsa

You can download it from GitHub.
You can use mtsa 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.