word-rnn-tensorflow | layer Recurrent Neural Networks | Machine Learning library

by hunkim Python Version: Current License: MIT

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

kandi X-RAY | word-rnn-tensorflow Summary

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

Multi-layer Recurrent Neural Networks (LSTM, RNN) for word-level language models in Python using TensorFlow.

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Support

word-rnn-tensorflow has a medium active ecosystem.

It has 1310 star(s) with 505 fork(s). There are 84 watchers for this library.

It had no major release in the last 6 months.

There are 31 open issues and 27 have been closed. On average issues are closed in 33 days. There are 2 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of word-rnn-tensorflow is current.

Quality

word-rnn-tensorflow has 0 bugs and 0 code smells.

Security

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

word-rnn-tensorflow code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

word-rnn-tensorflow is licensed under the MIT License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

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

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

Sample a model
Sample from the RNN
Search for a given OOV
Predict probabilities for the given samples
Train the model
Returns the next batch
Reset the batch pointer
Prepare the vocab
Builds a vocabulary from a list of sentences

Get all kandi verified functions for this library.

word-rnn-tensorflow Key Features

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

word-rnn-tensorflow Examples and Code Snippets

No Code Snippets are available at this moment for word-rnn-tensorflow.

Community Discussions

Trending Discussions on word-rnn-tensorflow

How to fix the error 'UnicodeDecodeError: 'charmap' codec can't decode byte 0x9d in position 36188: character maps to '

QUESTION

How to fix the error 'UnicodeDecodeError: 'charmap' codec can't decode byte 0x9d in position 36188: character maps to '

Asked 2020-Aug-20 at 15:54

I am training an AI to write a book using TensorFlow 1.14 and python 2.6.7. Whenever I run my training python code, I get the error message UnicodeDecodeError: 'charmap' codec can't decode byte 0x9d in position 36188: character maps to I have reinstalled TensorFlow and python as well as searched the forums to try and find an answer. The traceback leads me to a file called cp1252.py in the encodings folder

The code I'm running is

...

ANSWER

Answered 2020-Aug-20 at 15:54

So it turns out there was a weird character in the text file. All I had to do was replace all the weird symbols with the correct symbol. Thanks for all he help!

Source https://stackoverflow.com/questions/63433111

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

Vulnerabilities

No vulnerabilities reported

Install word-rnn-tensorflow

You can download it from GitHub.
You can use word-rnn-tensorflow 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.