dee
deepautoencoder
pypi i deepautoencoder
dee

deepautoencoder

A simple Tensorflow based library for deep and/or denoising AutoEncoder.

by Rajarshee Mitra

1.1 (see all)
pypi i deepautoencoder
Readme

libsdae - deep-Autoencoder & denoising autoencoder

A simple Tensorflow based library for Deep autoencoder and denoising AE. Library follows sklearn style.

Prerequisities & Support

  • Tensorflow 1.0 is needed.
  • Supports both Python 2.7 and 3.4+ . Inform if it doesn't.

Installing

pip install git+https://github.com/rajarsheem/libsdae.git

Usage and small doc

test.ipynb has small example where both a tiny and a large dataset is used.

from deepautoencoder import StackedAutoEncoder
model = StackedAutoEncoder(dims=[5,6], activations=['relu', 'relu'], noise='gaussian', epoch=[10000,500],
                            loss='rmse', lr=0.007, batch_size=50, print_step=2000)
# usage 1 - encoding same data                           
result = model.fit_transform(x)
# usage 2 - fitting on one dataset and transforming (encoding) on another data
model.fit(x)
result = model.transform(np.random.rand(5, x.shape[1]))

Alt text

Important points:

  • If noise is not given, it becomes an autoencoder instead of denoising autoencoder.
  • dims refers to the dimenstions of hidden layers. (3 layers in this case)
  • noise = (optional)['gaussian', 'mask-0.4']. mask-0.4 means 40% of bits will be masked for each example.
  • x_ is the encoded feature representation of x.
  • loss = (optional) reconstruction error. rmse or softmax with cross entropy are allowed. default is rmse.
  • print_step is the no. of steps to skip between two loss prints.
  • activations can be 'sigmoid', 'softmax', 'tanh' and 'relu'.
  • batch_size is the size of batch in every epoch
  • Note that while running, global loss means the loss on the total dataset and not on a specific batch.
  • epoch is a list denoting the no. of iterations for each layer.

Citing

  • Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion by P. Vincent, H. Larochelle, I. Lajoie, Y. Bengio and P. Manzagol (Journal of Machine Learning Research 11 (2010) 3371-3408)

Contributing

You are free to contribute by starting a pull request. Some suggestions are:

  • Variational Autoencoders
  • Recurrent Autoencoders.

GitHub Stars

147

LAST COMMIT

4yrs ago

MAINTAINERS

1

CONTRIBUTORS

3

OPEN ISSUES

1

OPEN PRs

0
VersionTagPublished
1.1
6yrs ago
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