Merge pull request #1243 from tornadomeet/patch-1

fix type of PixelShuffle 

1 files changed, 1 insertions, 1 deletions

diff --git a/doc/simple.md b/doc/simple.md
index 0fa467b..e18e15d 100755
--- a/doc/simple.md
+++ b/doc/simple.md
@@ -1548,7 +1548,7 @@ C = model:forward(A)  -- C will be of size `b x m`
 ## PixelShuffle ##
 ```module = nn.PixelShuffle(r)```
 
-Rearranges elements in a tensor of shape `[C*r, H, W]` to a tensor of shape `[C, H*r, W*r]`. This is useful for implementing efficient sub-pixel convolution with a stride of `1/r` (see [Shi et. al](https://arxiv.org/abs/1609.05158)). Below we show how the `PixelShuffle` module can be used to learn upscaling filters to transform a low-resolution input to a high resolution one, with a 3x upscale factor. This is useful for tasks such as super-resolution, see ["Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network" - Shi et al.](https://arxiv.org/abs/1609.05158) for further details.
+Rearranges elements in a tensor of shape `[C*r*r, H, W]` to a tensor of shape `[C, H*r, W*r]`. This is useful for implementing efficient sub-pixel convolution with a stride of `1/r` (see [Shi et. al](https://arxiv.org/abs/1609.05158)). Below we show how the `PixelShuffle` module can be used to learn upscaling filters to transform a low-resolution input to a high resolution one, with a 3x upscale factor. This is useful for tasks such as super-resolution, see ["Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network" - Shi et al.](https://arxiv.org/abs/1609.05158) for further details.
 
 ```
 upscaleFactor = 3