3 files changed, 42 insertions, 7 deletions

diff --git a/doc/nerv.md b/doc/nerv.md
index 22b4072..28411f5 100644
--- a/doc/nerv.md
+++ b/doc/nerv.md
@@ -13,3 +13,5 @@ A registered function, the original function is `luaT_lua_getmetatable`. `tname`
 A registered function, the original function is `luaT_newmetatable`, it returns the metatable of the created class by the name `tname`.
 * __string = nerv.setmetatable(table self, string tname)__  
 A registered function, the original function is `luaT_lua_setmetatable`. It assigns the metatable registered in __luaT__ by the name *tname* to the table *self*. And return *tname* to user.
+* __table = nerv.get_type(string typename)__  
+Returns the type(`loadstring("return " .. typename)`).
\ No newline at end of file
diff --git a/doc/nerv_layer.md b/doc/nerv_layer.md
index 0425d5f..ac6480c 100644
--- a/doc/nerv_layer.md
+++ b/doc/nerv_layer.md
@@ -141,7 +141,8 @@ print('network input&output&error space allocation...')
 affineI = {dataM} --input to the network is data
 affineO = {nerv.CuMatrixFloat(data_num, 2)}
 softmaxI = {affineO[1], labelM}
-softmaxO = {nerv.CuMatrixFloat(data_num, 2)} 
+softmaxO = {}
+output = nerv.CuMatrixFloat(data_num, 2)
 
 affineE = {nerv.CuMatrixFloat(data_num, 2)}
 --[[space allocation end]]--
@@ -152,9 +153,9 @@ ce_last = 0
 for l = 0, 10, 1 do
     affineL:propagate(affineI, affineO)
     softmaxL:propagate(softmaxI, softmaxO)
-    softmaxO[1]:softmax(softmaxI[1])
+    output:softmax(softmaxI[1])
 
-    softmaxL:back_propagate(affineE, nil, softmaxI, softmaxO)
+    softmaxL:back_propagate(affineE, {}, softmaxI, softmaxO)
     
     affineL:update(affineE, affineI, affineO) 
 
@@ -162,10 +163,9 @@ for l = 0, 10, 1 do
         nerv.utils.printf("training iteration %d finished\n", l)
         nerv.utils.printf("cross entropy: %.8f\n", softmaxL.total_ce - ce_last)
         ce_last = softmaxL.total_ce 
-        nerv.utils.printf("accurate labels: %d\n", calculate_accurate(softmaxO[1], labelM))
+        nerv.utils.printf("accurate labels: %d\n", calculate_accurate(output, labelM))
         nerv.utils.printf("total frames processed: %.8f\n", softmaxL.total_frames)
     end
 end
 --[[end training]]--
-
 ```
\ No newline at end of file
diff --git a/doc/nerv_nn.md b/doc/nerv_nn.md
index 54c7165..0f274c4 100644
--- a/doc/nerv_nn.md
+++ b/doc/nerv_nn.md
@@ -12,7 +12,24 @@ it contains __nerv.LayerRepo__, __nerv.ParamRepo__, and __nerv.DAGLayer__(inheri
 	* `table layers` Contains the mapping of layer ID to layer object.
 objects.
 * __nerv.DAGLayer__ inherits __nerv.Layer__.  
-
+	* `table layers` Mapping from a layer ID to its "ref". A ref is of the structure below:
+	 ```
+     nerv.Layer layer --its layer
+     nerv.Matrix inputs	
+     nerv.Matrix outputs 
+     nerv.Matrix err_inputs
+     nerv.Matrix err_outputs
+     table next_layers
+     int input_len -- #dim_in
+     int output_len -- #dim_out
+     int in_deg 
+     bool visited -- used in topology sort
+     ```
+	* `inputs`
+	* `outputs`
+	* `parsed_conn`
+	* `queue`
+	
 ##Methods##
 ###__nerv.ParamRepo__###
 * __void ParamRepo:\_\_init(table param_files)__  
@@ -29,4 +46,20 @@ llist : {layer1, layer2, ...}
 layer : layerid = {param_config, layer_config}
 param_config : {param1 = paramID1, param2 = paramID2}
 ```
-__LayerRepo__ will merge `param_config` into `layer_config` and construct a layer by calling `layer_type(layerid, global_conf, layer_config)`.
\ No newline at end of file
+__LayerRepo__ will merge `param_config` into `layer_config` and construct a layer by calling `layer_type(layerid, global_conf, layer_config)`.
+
+* __[nerv.Layer] LayerRepo.get_layer([LayerRepo] self, [string] lid)__  
+`self`, __nerv.LayerRepo__, ...
+Returns the layer with ID `lid`.
+
+###__nerv.DAGLayer__###
+* __DAGLayer:\_\_init(id, global_conf, layer_conf, [a, b, ...])__  
+	Returns: 
+	__string__, dfdfdfddf
+    __asasa__, asasasasa
+    Parameters:
+	`id`: __string__, the ID of the layer.  
+    `global_conf`:__table__,the global config.  
+	
+    sasasa
+     
\ No newline at end of file