Metadata-Version: 1.1
Name: pytorch-mighty
Version: 0.2.1
Summary: The Mighty Monitor Trainer for your pytorch models.
Home-page: https://github.com/dizcza/pytorch-mighty
Author: Danylo Ulianych
Author-email: d.ulianych@gmail.com
License: BSD-3
Description: # pytorch-mighty
        
        The Mighty Monitor Trainer for your pytorch models. Powered by [Visdom](https://github.com/facebookresearch/visdom).
        
        ![](images/training-progress.png)
        
        
        
        ### Quick start
        
        Requires Python 3.6+
        
        1. Install [pytorch](https://pytorch.org/)
        2. `$ pip install pytorch-mighty`
        3. `$ python -m visdom.server -port 8097` - start visdom server on port 8097
        4. In a separate terminal, run `python examples.py`
        5. Navigate to http://localhost:8097 to see the training progress.
        6. Check-out more examples on [http://85.217.171.57:8097](http://85.217.171.57:8097/). Give your browser a few minutes to parse the json data.
        
        
        ### Articles, implemented in the package
        
        1. Fong, R. C., & Vedaldi, A. (2017). Interpretable explanations of black boxes by meaningful perturbation.
            * Paper: https://arxiv.org/abs/1704.03296
            * Used in: [`trainer/mask.py`](mighty/trainer/mask.py)
        
        2. Belghazi, M. I., Baratin, A., Rajeswar, S., Ozair, S., Bengio, Y., Courville, A., & Hjelm, R. D. (2018). Mine: mutual information neural estimation.
            * Paper: https://arxiv.org/abs/1801.04062
            * Used in: [`monitor/mutual_info/neural_estimation.py`](mighty/monitor/mutual_info/neural_estimation.py)
        
        3. Kraskov, A., Stögbauer, H., & Grassberger, P. (2004). Estimating mutual information.
            * Paper: https://arxiv.org/abs/1208.4475
            * Used in: [`monitor/mutual_info/npeet.py`](mighty/monitor/mutual_info/npeet.py)
            * Original source code: https://github.com/gregversteeg/NPEET
        
        4. Ince, R. A., Giordano, B. L., Kayser, C., Rousselet, G. A., Gross, J., & Schyns, P. G. (2017). A statistical framework for neuroimaging data analysis based on mutual information estimated via a gaussian copula. Human brain mapping, 38(3), 1541-1573.
            * Paper: http://dx.doi.org/10.1002/hbm.23471
            * Used in [`monitor/mutual_info/gcmi.py`](mighty/monitor/mutual_info/gcmi.py)
            * Original source code: https://github.com/robince/gcmi
        
        
        ### Projects that use pytorch-mighty
        
        * [MCMC_BinaryNet](https://github.com/dizcza/MCMC_BinaryNet) - Markov Chain Monte Carlo binary networks optimization.
        * [EmbedderSDR](https://github.com/dizcza/EmbedderSDR) - encode images into binary Sparse Distributed Representation ([SDR](https://discourse.numenta.org/t/sparse-distributed-representations/2150)).
        * [sparse-representation](https://github.com/dizcza/sparse-representation) - Basis Pursuit solvers for the P0- and P1-problems, which encode the data into sparse vectors of high dimensionality.
        
        
Platform: UNKNOWN
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: BSD License
Classifier: Natural Language :: English
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Software Development
