add sdae

Author: eelxpeng

README.md

@@ -2,6 +2,17 @@
 
 This repository tries to provide unsupervised deep learning models with Pytorch for convenient use.
 
+## Denoising Autoencoder
+1-layer autoencoder. Corrupt the input (masking), then reconstruct the original input.
+
+## Stacked Denoising Autoencoder
+Layerwise pretraining by denoising autoenocder. Then stack all layers and finetune further.
+
+## VAE
+The famous Variational Autoencoder from the paper
+
+`Kingma, Diederik P., and Max Welling. "Auto-encoding variational bayes." ICLR (2014).`
+
 ## Convolutional VAE
 VAE using convolutional and deconvolutional networks is demonstrated with SVHN dataset. Expample code is `test_convvae.py`. The reconstruction and samples generated is show as follows:
 
@@ -11,7 +22,7 @@ VAE using convolutional and deconvolutional networks is demonstrated with SVHN d
 ## Variational Deep Embedding
 Implementation of Variational Deep Embedding from the IJCAI2017 paper:
 
-Jiang, Zhuxi, et al. "Variational deep embedding: An unsupervised and generative approach to clustering." International Joint Conference on Artificial Intelligence. 2017.
+`Jiang, Zhuxi, et al. "Variational deep embedding: An unsupervised and generative approach to clustering." International Joint Conference on Artificial Intelligence. 2017.`
 
 The original code is written in [Keras](https://github.com/slim1017/VaDE). However, the original code is incorrect when computing the loss function. And I have corrected the loss function part with my code. The example usage can be found in `test/test_vade-3layer.py`, and it uses the pretrained weights from autoencoder in `test/model/pretrained_vade-3layer.pt`. 
 

udlp/autoencoder/denoisingAutoencoder.py

@@ -51,6 +51,7 @@ def encode(self, x, train=True):
         return self.dropout(self.enc_act_func(F.linear(x, self.weight, self.bias)))
 
     def encodeBatch(self, dataloader):
+        use_cuda = torch.cuda.is_available()
         encoded = []
         for batch_idx, (inputs, _) in enumerate(dataloader):
             inputs = inputs.view(inputs.size(0), -1).float()