SeqGAN | Sequence Generative Adversarial Nets with Policy Gradient | Machine Learning library

 by   LantaoYu Python Version: Current License: No License

kandi X-RAY | SeqGAN Summary

kandi X-RAY | SeqGAN Summary

SeqGAN is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Pytorch, Tensorflow, Generative adversarial networks applications. SeqGAN has no bugs, it has no vulnerabilities and it has medium support. However SeqGAN build file is not available. You can download it from GitHub.

Apply Generative Adversarial Nets to generating sequences of discrete tokens. The illustration of SeqGAN. Left: D is trained over the real data and the generated data by G. Right: G is trained by policy gradient where the final reward signal is provided by D and is passed back to the intermediate action value via Monte Carlo search. The research paper SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient has been accepted at the Thirty-First AAAI Conference on Artificial Intelligence (AAAI-17).
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              SeqGAN has a medium active ecosystem.
              It has 2037 star(s) with 719 fork(s). There are 74 watchers for this library.
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              It had no major release in the last 6 months.
              There are 33 open issues and 32 have been closed. On average issues are closed in 71 days. There are 1 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of SeqGAN is current.

            kandi-Quality Quality

              SeqGAN has 0 bugs and 0 code smells.

            kandi-Security Security

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

            kandi-License License

              SeqGAN does not have a standard license declared.
              Check the repository for any license declaration and review the terms closely.
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              Without a license, all rights are reserved, and you cannot use the library in your applications.

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              SeqGAN releases are not available. You will need to build from source code and install.
              SeqGAN 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.
              SeqGAN saves you 297 person hours of effort in developing the same functionality from scratch.
              It has 717 lines of code, 49 functions and 6 files.
              It has medium code complexity. Code complexity directly impacts maintainability of the code.

            Top functions reviewed by kandi - BETA

            kandi has reviewed SeqGAN and discovered the below as its top functions. This is intended to give you an instant insight into SeqGAN implemented functionality, and help decide if they suit your requirements.
            • Update embeddings
            • Update recurrent unit
            • Calculate the output unit
            • A highway layer
            • Compute the linear layer
            Get all kandi verified functions for this library.

            SeqGAN Key Features

            No Key Features are available at this moment for SeqGAN.

            SeqGAN Examples and Code Snippets

            seqgan-text-tensorflow,Usage
            Pythondot img1Lines of Code : 23dot img1License : Strong Copyleft (GPL-3.0)
            copy iconCopy
            >>> ./train
            
            >>> ./sample 
            
            >>> ./train -t /path/to/your/file.txt
            
            >>> ./train --help
            
            usage: train.py [-h] [-t TEXT] [-l SEQ_LEN] [-b BATCH_SIZE] [-n NUM_STEPS]
                            [-e NUM_EPOCHS] [-c] [-p LEARN_PHASE  
            chainer-SeqGAN,requirements
            Jupyter Notebookdot img2Lines of Code : 10dot img2no licencesLicense : No License
            copy iconCopy
            # Ubuntu/Linux 64-bit Python 3.4
            export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.11.0-cp34-cp34m-linux_x86_64.whl
            
            # Ubuntu/Linux 64-bit Python 3.5
            export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/l  
            Training LSTM
            Pythondot img3Lines of Code : 3dot img3no licencesLicense : No License
            copy iconCopy
            python2 main.py --pretrain_g_epochs 2000 --total_epochs 0 --log_dir logs/train/pure_pretrain --eval_log_dir logs/eval/pure_pretrain
            
            python2 main.py --pretrain_g_epochs 1000 --total_epochs 1000 --log_dir logs/train/pretrain_n_seqgan  --eval_log_dir l  

            Community Discussions

            Trending Discussions on SeqGAN

            QUESTION

            How to share variables of RNN on Tensorflow
            Asked 2017-Aug-24 at 17:54

            I just make seqGAN on Tensorflow.

            But I cannot share variables.

            I wrote code aimed Discriminator as following...

            ...

            ANSWER

            Answered 2017-Aug-24 at 17:54

            variable_scope doesn't interact directly with name_scope. variable_scope is used to determine whether to create new variables or lookup new variables. You should use variable_scope with get_variable to accomplish this.

            Here are some examples:

            Source https://stackoverflow.com/questions/45798088

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

            Vulnerabilities

            No vulnerabilities reported

            Install SeqGAN

            You can download it from GitHub.
            You can use SeqGAN 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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