PyTorch implementation of a version of the Stacked Denoising AutoEncoder. Compatible with PyTorch 1.0.0 and Python 3.6 or 3.7 with or without CUDA.
An example using MNIST data can be found in the examples/mnist/mnist.py
which achieves around 80% accuracy using
k-Means on the encoded values.
Here is an example confusion matrix, true labels on y-axis and predicted labels on the x-axis.
This is distributed as a Python package ptsdae
and can be installed with python setup.py install
. The PyTorch nn.Module
class representing the SDAE is StackedDenoisingAutoEncoder
in ptsdae.sdae
, while the pretrain
and train
functions from ptsdae.model
are used to train the autoencoder.
Currently this code is used in a PyTorch implementation of DEC, see https://github.com/vlukiyanov/pt-dec.