ReGAN: Sequence GAN using RE[INFORCE|LAX|BAR] based PG estimators

Related tags

Deep Learning REGAN
Overview

Sequence Generation with GANs trained by Gradient Estimation

Requirements:

  • PyTorch v0.3
  • Python 3.6
  • CUDA 9.1 (For GPU)

Origin

The idea is from paper SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient. We build on SeqGAN and compare three gradient estimators for sequence generation with GANs: REINFORCE (as in SeqGAN), and state-of-the-art REBAR (https://arxiv.org/pdf/1703.07370.pdf) and RELAX (https://arxiv.org/pdf/1711.00123.pdf).

The code is rewrited in PyTorch with the structure derived from (https://github.com/LantaoYu/SeqGAN)

Running

$ python main.py

After runing this file, the results will be printed on terminal. You can change the parameters in the main.py.

Using CUDA

Pass in the gpu device number for e.g. 0

$ python main.py --cude {GPU_DEVICE_NUMBER}

Enable Visualization

Start the server (probably in a screen or tmux):

python -m visdom.server -port 8097

Run with --visualize parameter

$ python main.py --cude {GPU_DEVICE_NUMBER} --visualize

Visdom Server Page Vanishing Gradients - With Pretraining Console log - With Pretraining

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Comments
  • separate reinforce from relax

    separate reinforce from relax

    2 main changes here: _Create the option to choose the Gradient Estimator between "REINFORCE" and "RELAX" _When it is RELAX, get rid of the MC sampling and rollout + rewards phase, for more efficiency

    opened by Ravoxsg 1
  • Visualizer enabler code.

    Visualizer enabler code.

    @Aparna-B @satyakrishnagorti @Ravoxsg

    Please review and possibly merge. We will need the visualizer for writing report. This PR should have no impact on the way you run a.t.m. Incorporates Visdom (FAIR's standard visualization tool and TorchNet - an abstraction lib for PyTorch).

    Lastly, presuming you are all using Python > 3.6, thus used my fav `f strings'

    opened by raeidsaqur 0
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