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

by LantaoYu Python Version: Current License: No License

X-Ray Key Features Code Snippets(3)Community Discussions(1)Vulnerabilities Install Support

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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Quality

Security

License

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Support

SeqGAN has a medium active ecosystem.

It has 2037 star(s) with 719 fork(s). There are 74 watchers for this library.

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.

Quality

SeqGAN has 0 bugs and 0 code smells.

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.

License

SeqGAN 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

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

Python

Lines of Code : 23

License : Strong Copyleft (GPL-3.0)

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>>> ./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 Notebook

Lines of Code : 10

License : No License

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# 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

Python

Lines of Code : 3

License : No License

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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

How to share variables of RNN on Tensorflow

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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