textgenrnn | Easily train your own text-generating neural network | Machine Learning library

 by   minimaxir Python Version: 2.0.0 License: Non-SPDX

kandi X-RAY | textgenrnn Summary

kandi X-RAY | textgenrnn Summary

textgenrnn is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Pytorch, Tensorflow, Keras, Neural Network applications. textgenrnn has no bugs, it has no vulnerabilities, it has build file available and it has medium support. However textgenrnn has a Non-SPDX License. You can install using 'pip install textgenrnn' or download it from GitHub, PyPI.

Easily train your own text-generating neural network of any size and complexity on any text dataset with a few lines of code, or quickly train on a text using a pretrained model.

            kandi-support Support

              textgenrnn has a medium active ecosystem.
              It has 4902 star(s) with 756 fork(s). There are 138 watchers for this library.
              It had no major release in the last 12 months.
              There are 130 open issues and 98 have been closed. On average issues are closed in 76 days. There are 13 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of textgenrnn is 2.0.0

            kandi-Quality Quality

              textgenrnn has 0 bugs and 0 code smells.

            kandi-Security Security

              textgenrnn has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              textgenrnn code analysis shows 0 unresolved vulnerabilities.
              There are 0 security hotspots that need review.

            kandi-License License

              textgenrnn has a Non-SPDX License.
              Non-SPDX licenses can be open source with a non SPDX compliant license, or non open source licenses, and you need to review them closely before use.

            kandi-Reuse Reuse

              textgenrnn releases are available to install and integrate.
              Deployable package is available in PyPI.
              Build file is available. You can build the component from source.
              Installation instructions are not available. Examples and code snippets are available.
              textgenrnn saves you 336 person hours of effort in developing the same functionality from scratch.
              It has 806 lines of code, 36 functions and 8 files.
              It has high code complexity. Code complexity directly impacts maintainability of the code.

            Top functions reviewed by kandi - BETA

            kandi has reviewed textgenrnn and discovered the below as its top functions. This is intended to give you an instant insight into textgenrnn implemented functionality, and help decide if they suit your requirements.
            • Train a neural network
            • Train a new model
            • Generate an RNN model
            • Create a new RNN layer
            • Synthesize textgens to a file
            • Sample text generation from preds
            • Synthesize textgen
            • Generate text generator
            • Computes the similarity between two texts
            • Encodes a list of texts
            • Encodes a sequence of text
            • Generate a text file
            • Generate text
            • Generate text samples
            • Generate samples
            • Train a model from a LTI file
            • Load the model
            Get all kandi verified functions for this library.

            textgenrnn Key Features

            No Key Features are available at this moment for textgenrnn.

            textgenrnn Examples and Code Snippets

            Pythondot img1Lines of Code : 7dot img1License : Non-SPDX (NOASSERTION)
            copy iconCopy
            * * * incoming message * * *
            The Marathon is equipped with three artificial intelligence units. Tycho handles the science and engineering network, Durandal controls the marathon autonomous functions such as doors, air from kitchens, and I don't reme  
            RapLyrics-Back,Getting started,Train the model
            Pythondot img2Lines of Code : 2dot img2License : Permissive (MIT)
            copy iconCopy
            $ rm -r textgenrnn  
            $ pip install textgenrnn    
            Tensorflow error: AttributeError: module 'tensorflow' has no attribute 'placeholder'
            Pythondot img3Lines of Code : 6dot img3License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            textgen = textgenrnn(name=marselo)
            textgen = textgenrnn(name='marselo')
            textgen = textgenrnn(name=model_name)
            copy iconCopy
            pip3 instal tensorflow-gpu==1.14
            How to convert the output of a script
            Pythondot img5Lines of Code : 4dot img5License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            output = t.generate(1, temperature=.5, return_as_list=True)
            for string in output:

            Community Discussions


            ( AttributeError: module 'tensorflow_core._api.v2.config' has no attribute 'get_visible_devices' ) occurs in Heroku
            Asked 2020-Jun-09 at 10:37

            I was running a Heroku app that gets YouTube video titles and outputs it to a dataset file, it then generates a batch of titles from the dataset. I am using tensorflow 2.0.0.

            When I deploy the app and watch the logs (Heroku CLI) I get the following error:



            Answered 2020-Jun-09 at 09:59

            I use tensorflow==2.0.0.

            AttributeError: module 'tensorflow_core._api.v2.config' has no attribute 'get_visible_devices'

            tensorflow==2.0.0 does not have have get_visible_devices
            tensorflow==2.2.0 has get_visible_devices



            Source https://stackoverflow.com/questions/62279473


            Django app with tensorflow running very slowly
            Asked 2020-May-11 at 09:14

            I have a web app created with django that is using tensorflow to generate compliments and text them to people who sign up for the service. Every time a user submits a number, I'm using subprocess.call() to run a separate script which creates my model and writes the AI-generated compliment to a text file, then the SMS gateway (twilio) uses this .txt file for the body of the message.

            However, after submitting a valid number when it is running the model the app is loading for a very long time, like sometimes it takes 30 seconds or longer before the page stops loading and this is not ideal.

            Why is this taking so long? Is it because creating the model in tensorflow is just a timely process? And can I do something to speed this up, should I just have it run the code to create the model earlier in the program, or should I just generate a whole bunch of compliments ahead of time and have the SMS gateway draw from that instead?

            This is the script which creates the model:



            Answered 2020-May-11 at 09:14

            It looks like your Tensorflow model is just that slow on this machine. You tested it without Django and it also needs 30 seconds, so we can skip the Django part here.

            The best option to make it faster is to use tensorflow-gpu and a server with a Nvidia graphics card (e.g. AWS GPU instances). This usually reduces the needed time by a factor of at least 10, so you will get a waiting time that is probably acceptable for your application.

            In addition you should try to decouple Django and the Tensorflow-Script to avoid loading and initializing the model for each request. Create some kind of queue system. Then your Django app write the input value into the queue and waits for the result and your Tensorflow program runs in the background and reads the values from the queue, evaluates the model and sends the result back to Django.

            Source https://stackoverflow.com/questions/61718079


            Tensorflow error: AttributeError: module 'tensorflow' has no attribute 'placeholder'
            Asked 2020-Apr-09 at 03:25

            I'm not very experienced using Tensorflow, and I started using a Google Colab created by Max Woolf (https://minimaxir.com/2018/05/text-neural-networks/)

            I've edited this post before as I had another simple error I corrected, but then now a bunch of new problems arose with that correction. I'm not experienced as I said, and there's a ton of errors I cannot understand.

            I'm trying to train my machine to produce text from a training data file called "marselo", and I suddenly get this error:



            Answered 2020-Apr-08 at 12:44
            textgen = textgenrnn(name=marselo)

            Source https://stackoverflow.com/questions/61100907

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


            No vulnerabilities reported

            Install textgenrnn

            You can install using 'pip install textgenrnn' or download it from GitHub, PyPI.
            You can use textgenrnn 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.


            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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