# pt-sdae

**Repository Path**: juice1/pt-sdae

## Basic Information

- **Project Name**: pt-sdae
- **Description**: PyTorch implementation of SDAE (Stacked Denoising AutoEncoder)
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 1
- **Forks**: 0
- **Created**: 2020-11-16
- **Last Updated**: 2022-04-01

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# pt-sdae
[![Build Status](https://travis-ci.org/vlukiyanov/pt-sdae.svg?branch=master)](https://travis-ci.org/vlukiyanov/pt-sdae) [![codecov](https://codecov.io/gh/vlukiyanov/pt-sdae/branch/master/graph/badge.svg)](https://codecov.io/gh/vlukiyanov/pt-sdae)
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PyTorch implementation of a version of the Stacked Denoising AutoEncoder (*note this implementation is unofficial*). Compatible with PyTorch 1.0.0 and Python 3.6 or 3.7 with or without CUDA.

## Examples

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](http://scikit-learn.org/stable/modules/generated/sklearn.metrics.confusion_matrix.html), true labels on y-axis and predicted labels on the x-axis.
![Alt text](confusion.png)

## Usage

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>.