update multi label doc

doc/README.md

@@ -21,3 +21,5 @@ In general, cxxnet configuration file contains four kinds of configurations in a
   - This page includes all the four tasks you could try by cxxnet.
 * [Other Setting](other.md)
   - Set other parameters for neural network, related to device selection, running control.
+* [Advanced Usages](advanced.md)
+  - Some advanced usages of cxxnet can he found here.

doc/advanced.md

@@ -0,0 +1,28 @@
+#### Introduction
+This page will introduce some advanced usages in cxxnet, including:
+* [Multi-label Training](#multi-label-training)
+
+#### Multi-label Training
+* To use multi-label training, you need the following three steps in additional to the case of single label training:
+	- First, you need to specify the number of labels in the network by setting ```label_width``` variable in global settings. The following setting denotes that we have 5 labels in the network.
+	```bash
+	label_width = 5
+	```
+	- In the [image list file](io.md#image-list-file), you need to provide ```label_width``` labels instead of one label. Namely, each line is in the format:
+	```
+	image_index \t label_1 \t label_2 ... \t label_n \t file_name
+	```
+	- In global setting, you need to specify how each field of the labels form a label vector. For example, we are interested in a localization task. In the task, we first need to output the label for one image, and next predict its position denoted by a bounding box. The configuration can be written as:
+	```
+	label_vec[0, 1) = class
+	label_vec[1, 5) = bounding_box
+	```
+	- At last, in each loss layer, you need to specify the target of the loss:
+	```
+	layer[19->21] = softmax
+		target = class
+	layer[20->22] = l2_loss
+		target = bounding_box
+	```
+	This means for the first field of the labels, we treat it as a class label, and apply standard softmax loss function on it. For the other four labels, we treat them as the coordinates of the bounding box, and train them using Euclidean loss.
+

doc/io.md

@@ -131,7 +131,7 @@ Deterministic transformations are usually used in test to generate diverse predi
 
 =
 ### CSV Iterator
-This iterator can be used to read data files that stores in a raw CSV file. The CSV file should have the following data structure ```label(s) | other_columns```. The number of label columns can be controlled via ```label_width``` parameter, by default it is set to 1, i.e. first column of CSV file is treated as labels. Example:
+This iterator can be used to read data files that stores in a raw CSV file. The CSV file should have the following data structure ```label(s) , other_columns```. The number of label columns can be controlled via ```label_width``` parameter, by default it is set to 1, i.e. first column of CSV file is treated as labels. Example:
 ```bash
 iter = csv
 filename = "train.csv"