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@@ -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
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