Siamese-LSTM | Siamese LSTM for evaluating semantic similarity | Machine Learning library

by likejazz Python Version: Current License: No License

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kandi X-RAY | Siamese-LSTM Summary

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

Siamese LSTM for evaluating semantic similarity between sentences of the Quora Question Pairs Dataset.

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Siamese-LSTM has a low active ecosystem.

It has 221 star(s) with 63 fork(s). There are 9 watchers for this library.

It had no major release in the last 6 months.

There are 5 open issues and 1 have been closed. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of Siamese-LSTM is current.

Quality

Siamese-LSTM has 0 bugs and 0 code smells.

Security

Siamese-LSTM has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

Siamese-LSTM code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

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

Siamese-LSTM 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.

Siamese-LSTM saves you 83 person hours of effort in developing the same functionality from scratch.

It has 213 lines of code, 8 functions and 4 files.

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

Top functions reviewed by kandi - BETA

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

Create word2vec embeddings
Convert text to a list of words
Extract questions
Splits a dataset into sequences with padding

Get all kandi verified functions for this library.

Siamese-LSTM Key Features

No Key Features are available at this moment for Siamese-LSTM.

Siamese-LSTM Examples and Code Snippets

No Code Snippets are available at this moment for Siamese-LSTM.

Community Discussions

Trending Discussions on Siamese-LSTM

Keras BinaryCrossentropy loss gives NaN for angular distance between two vectors

QUESTION

Keras BinaryCrossentropy loss gives NaN for angular distance between two vectors

Asked 2019-Oct-22 at 14:56

I want to train a siamese-LSTM such that the angular distance of two outputs is 1 (low similarity) if the corresponding label is 0 and 0 (high similarity) if the label is 1.

I took the formular for angular distance from here: https://en.wikipedia.org/wiki/Cosine_similarity

This is my model code:

...

ANSWER

Answered 2019-Oct-22 at 14:56

Binary cross entropy calculates log(output) and log(1-output). This means that your output needs to be strictly greater than 0 and strictly less than 1 as otherwise you will calculate the log of a negative number which results in NaN. (Note: log(0) should give you -inf which is not as bad as NaN, but still not desirable)

Mathematically, your output should be in the correct interval, but due to the inaccuracy of floating point operations, I can very well imagine that this is your problem. However, this is just a guess.

So, try to enforce your output to be greater than 0 and less than 1, e.g. by using clip with a small epsilon:

Source https://stackoverflow.com/questions/58505587

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

Vulnerabilities

No vulnerabilities reported

Install Siamese-LSTM

You can download it from GitHub.
You can use Siamese-LSTM 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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