From 68849e4aab6747207394a42a5211829b4509464b Mon Sep 17 00:00:00 2001 From: Jonathan L Long Date: Sat, 6 Sep 2014 20:14:28 -0700 Subject: [PATCH] [docs] fixup the MathJax notation in tutorial/layers --- docs/tutorial/layers.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/docs/tutorial/layers.md b/docs/tutorial/layers.md index 42ae598..4f0eae5 100644 --- a/docs/tutorial/layers.md +++ b/docs/tutorial/layers.md @@ -14,11 +14,11 @@ TODO complete list of layers linking to headings * Header: `./include/caffe/vision_layers.hpp` Vision layers usually take *images* as input and produce other *images* as output. -A typical "image" in the real-world may have one color channel ($c = 1$), as in a grayscale image, or three color channels ($c = 3$) as in an RGB (red, green, blue) image. -But in this context, the distinguishing characteristic of an image is its spatial structure: usually an image has some non-trivial height $h > 1$ and width $w > 1$. +A typical "image" in the real-world may have one color channel ($$c = 1$$), as in a grayscale image, or three color channels ($$c = 3$$) as in an RGB (red, green, blue) image. +But in this context, the distinguishing characteristic of an image is its spatial structure: usually an image has some non-trivial height $$h > 1$$ and width $$w > 1$$. This 2D geometry naturally lends itself to certain decisions about how to process the input. In particular, most of the vision layers work by applying a particular operation to some region of the input to produce a corresponding region of the output. -In contrast, other layers (with few exceptions) ignore the spatial structure of the input, effectively treating it as "one big vector" with dimension $$ c h w $$. +In contrast, other layers (with few exceptions) ignore the spatial structure of the input, effectively treating it as "one big vector" with dimension $$chw$$. #### Convolution -- 2.7.4