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The Mighty Monitor Trainer for your pytorch models.

Project description


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The Mighty Monitor Trainer for your pytorch models. Powered by Visdom.



Requires Python 3.6+

  1. Install PyTorch:
    • CPU backend: conda install pytorch torchvision cpuonly -c pytorch
    • GPU backend: conda install pytorch torchvision cudatoolkit=10.2 -c pytorch
  2. $ pip install pytorch-mighty

Quick start

Before running any script, start Visdom server:

$ python -m visdom.server -port 8097

Then run python or use the code below:

import torch
import torch.nn as nn
from torchvision import transforms
from torchvision.datasets import MNIST

from mighty.models import MLP
from mighty.monitor.monitor import MonitorLevel
from mighty.trainer import TrainerGrad
from import DataLoader

model = MLP(784, 128, 10)

optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)
scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer)

data_loader = DataLoader(MNIST, transform=transforms.ToTensor())

trainer = TrainerGrad(model,
# trainer.restore()  # uncomment to restore the saved state
trainer.train(n_epochs=10, mutual_info_layers=0)

Finally, navigate to http://localhost:8097 to see the training progress.

Articles, implemented or reused in the package

  1. Fong, R. C., & Vedaldi, A. (2017). Interpretable explanations of black boxes by meaningful perturbation.

  2. Belghazi, M. I., Baratin, A., Rajeswar, S., Ozair, S., Bengio, Y., Courville, A., & Hjelm, R. D. (2018). Mine: mutual information neural estimation.

  3. Kraskov, A., Stögbauer, H., & Grassberger, P. (2004). Estimating mutual information.

  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.

  5. IDTxl package to estimate mutual information.

Projects that use pytorch-mighty

  • MCMC_BinaryNet - Markov Chain Monte Carlo binary networks optimization.
  • EmbedderSDR - encode images into binary Sparse Distributed Representation (SDR).
  • sparse-representation - Basis Pursuit solvers for the P0- and P1-problems, which encode the data into sparse vectors of high dimensionality.
  • entropy-estimators - estimate Entropy and Mutual Information between multivariate random variables.

Check-out more examples on Give your browser a few minutes to parse the json data.

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