Merge branch 'master' of github.com:Determinant/nerv

1 files changed, 240 insertions, 16 deletions

diff --git a/doc/nerv_nn.md b/doc/nerv_nn.md
index 54c7165..c57447d 100644
--- a/doc/nerv_nn.md
+++ b/doc/nerv_nn.md
@@ -6,27 +6,251 @@ Part of the [Nerv](../README.md) toolkit.
 it contains __nerv.LayerRepo__, __nerv.ParamRepo__, and __nerv.DAGLayer__(inherits __nerv.Layer__).
 
 ###Class hierarchy and their members###
-* __nerv.ParamRepo__ Get parameter object by ID.  
-	* `table param_table` Contains the mapping of parameter ID to parameter file(__nerv.ChunkFile__) 
+####nerv.ParamRepo#### 
+Get parameter object by ID.  
+*	`table param_table` Contains the mapping of parameter ID to parameter file(__nerv.ChunkFile__)  
 *  __nerv.LayerRepo__ Get layer object by ID.  
-	* `table layers` Contains the mapping of layer ID to layer object.
+* 	`table layers` Contains the mapping of layer ID to layer object.
 objects.
-* __nerv.DAGLayer__ inherits __nerv.Layer__.  
 
+####__nerv.DAGLayer__####
+Inherits __nerv.Layer__.  
+* 	`layers`: __table__, a mapping from a layer ID to its "ref". A ref is a structure that contains reference to space allocations and other info of the layer.
+* 	`inputs`: __table__, a mapping from the inputs ports of the DAG layer to the input ports of the sublayer, the key is the port number, the value is `{ref, port}`.
+* 	`outputs`:__table__, the counterpart of `inputs`.
+* 	`parsed_conn`: __table__, a list of parsed connections, each entry is of format `{{ref_from, port_from}, {ref_to, port_to}}`.
+* 	`queue`: __table__, a list of "ref"s, the propagation of the DAGLayer will follow this order, and back-propagation will follow a reverse order.
+	
 ##Methods##
+
 ###__nerv.ParamRepo__###
-* __void ParamRepo:\_\_init(table param_files)__  
-`param_files` is a list of file names that stores parameters, the newed __ParamRepo__ will read them from file and store the mapping for future fetching.  
-* __nerv.Param ParamRepo.get_param(ParamRepo self, string pid, table global_conf)__  
-__ParamRepo__ will find the __nerv.ChunkFile__ `pf` that contains parameter of ID `pid` and return `pf:read_chunk(pid, global_conf)`.
+
+####nerv.ParamRepo:\_\_init(param\_files)####
+* 	Parameters:  
+	`param_files`: __table__
+*	Description:  
+	`param_files` is a list of file names that stores parameters, the newed __ParamRepo__ will read them from file and store the mapping for future fetching.  
+    
+####nerv.Param ParamRepo.get_param(ParamRepo self, string pid, table global_conf)####
+*	Returns:  
+	__nerv.Layer__  
+*	Parameters:  
+	`self`: __nerv.ParamRepo__.  
+    `pid`: __string__.  
+    `global_conf`: __table__.  
+*	Description:  
+	__ParamRepo__ will find the __nerv.ChunkFile__ `pf` that contains parameter of ID `pid` and return `pf:read_chunk(pid, global_conf)`.
 
 ###__nerv.LayerRepo__###
-* __void LayerRepo:\_\_init(table layer_spec, ParamRepo param_repo, table global_conf)__  
-__LayerRepo__ will construct the layers specified in `layer_spec`. Every entry in the `layer_spec` table should follow the format below:  
-```
-layer_spec : {[layer_type1] = llist1, [layer_type2] = llist2, ...}
-llist : {layer1, layer2, ...}
-layer : layerid = {param_config, layer_config}
-param_config : {param1 = paramID1, param2 = paramID2}
+####nerv.LayerRepo:\_\_init(layer\_spec, param\_repo, global\_conf)####
+* 	Returns:  
+  	__nerv.LayerRepo__.  
+* 	Parameters:  
+  	`self`: __nerv.ParamRepo__.  
+  	`layer_spec`: __table__.  
+  	`param_repo`: __nerv.ParamRepo__.  
+  	`global_conf`: __table__.  
+* 	Description:  
+  	__LayerRepo__ will construct the layers specified in `layer_spec`. Every entry in the `layer_spec` table should follow the format below:  
+	
+    > layer_spec : {[layer_type1] = llist1, [layer_type2] = llist2, ...}   
+  	> 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)`.    
+
+####nerv.LayerRepo.get\_layer(self, lid)####
+* 	Returns:  
+	__nerv.LayerRepo__, the layer with ID `lid`.
+* 	Parameters:  
+	`self`:__nerv.LayerRepo__.  
+	`lid`:__string__.  
+*	Description:   
+	Returns the layer with ID `lid`.
+    
+###nerv.DAGLayer###
+####nerv.DAGLayer:\_\_init(id, global\_conf, layer\_conf)####
+*	Returns:  
+	__nerv.DAGLayer__  
+*	Parameters:  
+	`id`: __string__  
+    `global_conf`: __table__  
+    `layer_conf`: __table__  
+*	Description:  
+	The `layer_conf` should contain `layer_conf.sub_layers` which is a __nerv.LayerRepo__ storing the sub layers of the DAGLayer. It should also contain `layer_conf.connections`, which is a string-to-string mapping table describing the DAG connections. See an example below:
+    
+    ```
+    dagL = nerv.DAGLayer("DAGL", global_conf, {["dim_in"] = {input_dim, 2}, ["dim_out"] = {}, ["sub_layers"] = layerRepo,
+    	["connections"] = {
+    	["<input>[1]"] = "AffineL[1]",
+    	["AffineL[1]"] = "SoftmaxL[1]",
+    	["<input>[2]"] = "SoftmaxL[2]",
+  	}})
+    ```
+    
+####nerv.DAGLayer.init(self, batch\_size)####
+*	Parameters:  
+	`self`: __nerv.DAGLayer__  
+    `batch_size`: __int__
+* 	Description:  
+	This initialization method will allocate space for output and input matrice, and will call `init()` for each of its sub layers.
+    
+
+####nerv.DAGLayer.propagate(self, input, output)####
+*	Parameters:  
+	`self`: __nerv.DAGLayer__  
+    `input`: __table__  
+    `output`: __table__  
+*	Description:  
+	The same function as __nerv.Layer.propagate__, do propagation for each layer in the order of `self.queue`.
+
+####nerv.DAGLayer.back\_propagate(self, next\_bp\_err, bp\_err, input, output)####
+*	Parameters:  
+	`self`: __nerv.DAGLayer__  
+    `next_bp_err`: __table__  
+    `bp_err`: __table__  
+    `input`: __table__  
+    `output`: __table__  
+*	Description:  
+	The same function as __nerv.Layer.back_propagate__, do back-propagation for each layer in the reverse order of `self.queue`.
+
+####nerv.DAGLayer.update(self, bp\_err, input, output)####
+*	Parameters:  
+	`self`: __nerv.DAGLayer__  
+    `bp_err`: __table__  
+    `input`: __table__  
+    `output`: __table__  
+*	Description:  
+	The same function as __nerv.Layer.update__, do update for each layer in the order of `self.queue`.
+    
+##Examples##
+*	aaa
+	
 ```
-__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
+require 'math'
+
+require 'layer.affine'
+require 'layer.softmax_ce'
+
+--[[Example using DAGLayer, a simple two-classification problem]]--
+
+--[[begin global setting and data generation]]--
+global_conf =  {lrate = 10, 
+                wcost = 1e-6,
+                momentum = 0.9,
+                cumat_type = nerv.CuMatrixFloat,
+               }
+
+input_dim = 5
+data_num = 100
+param_fn = "../tmp"
+ansV = nerv.CuMatrixFloat(input_dim, 1)
+for i = 0, input_dim - 1, 1 do
+    ansV[i][0] = math.random() - 0.5
+end
+ansB = math.random() - 0.5
+print('displaying ansV')
+print(ansV)
+print('displaying ansB(bias)')
+print(ansB)
+
+dataM = nerv.CuMatrixFloat(data_num, input_dim)
+for i = 0, data_num - 1, 1 do
+    for j = 0, input_dim - 1, 1 do
+        dataM[i][j] = math.random() * 2 - 1
+    end
+end
+refM = nerv.CuMatrixFloat(data_num, 1)
+refM:fill(ansB)
+refM:mul(dataM, ansV, 1, 1) --refM = dataM * ansV + ansB
+
+labelM = nerv.CuMatrixFloat(data_num, 2)
+for i = 0, data_num - 1, 1 do
+    if (refM[i][0] > 0) then
+        labelM[i][0] = 1 
+        labelM[i][1] = 0
+    else
+        labelM[i][0] = 0
+        labelM[i][1] = 1
+    end
+end
+--[[global setting and data generation end]]--
+
+
+--[[begin network building]]--
+--parameters
+do
+    local affineL_ltp = nerv.LinearTransParam('AffineL_ltp', global_conf)
+    affineL_ltp.trans = nerv.CuMatrixFloat(input_dim, 2)
+    for i = 0, input_dim - 1, 1 do
+        for j = 0, 1, 1 do
+            affineL_ltp.trans[i][j] = math.random() - 0.5 
+        end
+    end
+    local affineL_bp = nerv.BiasParam('AffineL_bp', global_conf)
+    affineL_bp.trans = nerv.CuMatrixFloat(1, 2)
+    for j = 0, 1, 1 do
+        affineL_bp.trans[j] = math.random() - 0.5
+    end
+
+    local chunk = nerv.ChunkFile(param_fn, 'w')
+    chunk:write_chunk(affineL_ltp)
+    chunk:write_chunk(affineL_bp)
+    chunk:close()
+
+    paramRepo = nerv.ParamRepo({param_fn})
+end
+
+--layers
+layerRepo = nerv.LayerRepo({
+        ["nerv.AffineLayer"] = 
+        {
+            ["AffineL"] = {{["ltp"] = "AffineL_ltp", ["bp"] = "AffineL_bp"}, {["dim_in"] = {input_dim}, ["dim_out"] = {2}}},
+        },
+        ["nerv.SoftmaxCELayer"] = 
+        {
+            ["SoftmaxL"] = {{}, {["dim_in"] = {2, 2}, ["dim_out"] = {}}}
+        },
+        }, paramRepo, global_conf)
+affineL = layerRepo:get_layer("AffineL")
+softmaxL = layerRepo:get_layer("SoftmaxL")
+print('layers initializing...')
+dagL = nerv.DAGLayer("DAGL", global_conf, {["dim_in"] = {input_dim, 2}, ["dim_out"] = {}, ["sub_layers"] = layerRepo,
+        ["connections"] = {
+           ["<input>[1]"] = "AffineL[1]",
+           ["AffineL[1]"] = "SoftmaxL[1]",
+           ["<input>[2]"] = "SoftmaxL[2]",
+        }})
+dagL:init(data_num)
+--affineL:init()
+--softmaxL:init()
+--[[network building end]]--
+
+
+--[[begin space allocation]]--
+print('network input&output&error space allocation...')
+dagL_input = {dataM, labelM}
+dagL_output = {}
+dagL_err = {}
+dagL_ierr = {nerv.CuMatrixFloat(data_num, input_dim), nerv.CuMatrixFloat(data_num, 2)}
+--[[space allocation end]]--
+
+
+--[[begin training]]--
+ce_last = 0
+for l = 0, 10, 1 do
+    dagL:propagate(dagL_input, dagL_output)
+    dagL:back_propagate(dagL_ierr, dagL_err, dagL_input, dagL_output)
+    dagL:update(dagL_err, dagL_input, dagL_output)
+    
+    if (l % 2 == 0) then
+        nerv.utils.printf("training iteration %d finished\n", l)
+        nerv.utils.printf("cross entropy: %.8f\n", softmaxL.total_ce - ce_last)
+        --nerv.utils.printf("accurate labels: %d\n", calculate_accurate(output, labelM))
+        nerv.utils.printf("total frames processed: %.8f\n", softmaxL.total_frames)
+    end
+    ce_last = softmaxL.total_ce 
+end
+--[[end training]]--
+```
\ No newline at end of file