make network configuration example file clearer

8 files changed, 129 insertions, 43 deletions

diff --git a/embedding_example/main.c b/embedding_example/main.c
index b3c9bf2..8856d58 100644
--- a/embedding_example/main.c
+++ b/embedding_example/main.c
@@ -22,7 +22,7 @@ void setup_nerv() {
     luaL_openlibs(L);
     luaL_loadfile(L, "setup_nerv.lua");
     /* network configuration */
-    lua_pushstring(L, "swb_baseline_decode.lua");
+    lua_pushstring(L, "../nerv/examples/swb_baseline.lua");
     if (lua_pcall(L, 1, LUA_MULTRET, 0))
     {
         printf("%s\n", luaL_checkstring(L, 1));
diff --git a/embedding_example/setup_nerv.lua b/embedding_example/setup_nerv.lua
index 49a5dd6..5ade950 100644
--- a/embedding_example/setup_nerv.lua
+++ b/embedding_example/setup_nerv.lua
@@ -4,9 +4,8 @@ local arg = {...}
 dofile(arg[1])
 local param_repo = nerv.ParamRepo()
 param_repo:import(gconf.initialized_param, nil, gconf)
-local sublayer_repo = make_sublayer_repo(param_repo)
-local layer_repo = make_layer_repo(sublayer_repo, param_repo)
-local network = get_network(layer_repo)
+local layer_repo = make_layer_repo(param_repo)
+local network = get_decode_network(layer_repo)
 local global_transf = get_global_transf(layer_repo)
 local batch_size = 1
 network:init(batch_size)
diff --git a/nerv/doc/gh-pages b/nerv/doc/gh-pages
new file mode 160000
+Subproject 195d95bb663258e3b1c3962f946db9c374018a8
diff --git a/nerv/examples/asr_trainer.lua b/nerv/examples/asr_trainer.lua
index 8dfb2ac..dcadfa3 100644
--- a/nerv/examples/asr_trainer.lua
+++ b/nerv/examples/asr_trainer.lua
@@ -1,8 +1,7 @@
 function build_trainer(ifname)
     local param_repo = nerv.ParamRepo()
     param_repo:import(ifname, nil, gconf)
-    local sublayer_repo = make_sublayer_repo(param_repo)
-    local layer_repo = make_layer_repo(sublayer_repo, param_repo)
+    local layer_repo = make_layer_repo(param_repo)
     local network = get_network(layer_repo)
     local input_order = get_input_order()
     local iterative_trainer = function (prefix, scp_file, bp)
@@ -18,7 +17,7 @@ function build_trainer(ifname)
             -- prine stat periodically
             gconf.cnt = gconf.cnt + 1
             if gconf.cnt == 1000 then
-                print_stat(sublayer_repo)
+                print_stat(layer_repo)
                 nerv.CuMatrix.print_profile()
                 nerv.CuMatrix.clear_profile()
                 gconf.cnt = 0
@@ -42,16 +41,16 @@ function build_trainer(ifname)
             -- collect garbage in-time to save GPU memory
             collectgarbage("collect")
         end
-        print_stat(sublayer_repo)
+        print_stat(layer_repo)
         nerv.CuMatrix.print_profile()
         nerv.CuMatrix.clear_profile()
         if (not bp) and prefix ~= nil then
             nerv.info("writing back...")
             local fname = string.format("%s_cv%.3f.nerv",
-                            prefix, get_accuracy(sublayer_repo))
+                            prefix, get_accuracy(layer_repo))
             network:get_params():export(fname, nil)
         end
-        return get_accuracy(sublayer_repo)
+        return get_accuracy(layer_repo)
     end
     return iterative_trainer
 end
diff --git a/nerv/examples/swb_baseline.lua b/nerv/examples/swb_baseline.lua
index 7783f2a..0e9f897 100644
--- a/nerv/examples/swb_baseline.lua
+++ b/nerv/examples/swb_baseline.lua
@@ -10,8 +10,8 @@ gconf = {lrate = 0.8, wcost = 1e-6, momentum = 0.9,
                 "/slfs1/users/mfy43/swb_global_transf.nerv"},
         debug = false}
 
-function make_sublayer_repo(param_repo)
-    return nerv.LayerRepo(
+function make_layer_repo(param_repo)
+    local layer_repo = nerv.LayerRepo(
     {
         -- global transf
         ["nerv.BiasLayer"] =
@@ -54,21 +54,23 @@ function make_sublayer_repo(param_repo)
             sigmoid5 = {{}, {dim_in = {2048}, dim_out = {2048}}},
             sigmoid6 = {{}, {dim_in = {2048}, dim_out = {2048}}}
         },
-        ["nerv.SoftmaxCELayer"] =
+        ["nerv.SoftmaxCELayer"] = -- softmax + ce criterion layer for finetune output
         {
             ce_crit = {{}, {dim_in = {3001, 1}, dim_out = {1}, compressed = true}}
+        },
+        ["nerv.SoftmaxLayer"] = -- softmax for decode output
+        {
+            softmax = {{}, {dim_in = {3001}, dim_out = {3001}}}
         }
     }, param_repo, gconf)
-end
 
-function make_layer_repo(sublayer_repo, param_repo)
-    return nerv.LayerRepo(
+    layer_repo:add_layers(
     {
         ["nerv.DAGLayer"] =
         {
             global_transf = {{}, {
                 dim_in = {429}, dim_out = {429},
-                sub_layers = sublayer_repo,
+                sub_layers = layer_repo,
                 connections = {
                     ["<input>[1]"] = "blayer1[1]",
                     ["blayer1[1]"] = "wlayer1[1]",
@@ -78,8 +80,8 @@ function make_layer_repo(sublayer_repo, param_repo)
                 }
             }},
             main = {{}, {
-                dim_in = {429, 1}, dim_out = {1},
-                sub_layers = sublayer_repo,
+                dim_in = {429}, dim_out = {3001},
+                sub_layers = layer_repo,
                 connections = {
                     ["<input>[1]"] = "affine0[1]",
                     ["affine0[1]"] = "sigmoid0[1]",
@@ -96,17 +98,51 @@ function make_layer_repo(sublayer_repo, param_repo)
                     ["sigmoid5[1]"] = "affine6[1]",
                     ["affine6[1]"] = "sigmoid6[1]",
                     ["sigmoid6[1]"] = "affine7[1]",
-                    ["affine7[1]"] = "ce_crit[1]",
+                    ["affine7[1]"] = "<output>[1]"
+                }
+            }}
+        }
+    }, param_repo, gconf)
+
+    layer_repo:add_layers(
+    {
+        ["nerv.DAGLayer"] =
+        {
+            ce_output = {{}, {
+                dim_in = {429, 1}, dim_out = {1},
+                sub_layers = layer_repo,
+                connections = {
+                    ["<input>[1]"] = "main[1]",
+                    ["main[1]"] = "ce_crit[1]",
                     ["<input>[2]"] = "ce_crit[2]",
                     ["ce_crit[1]"] = "<output>[1]"
                 }
+            }},
+            softmax_output = {{}, {
+                dim_in = {429}, dim_out = {3001},
+                sub_layers = layer_repo,
+                connections = {
+                    ["<input>[1]"] = "main[1]",
+                    ["main[1]"] = "softmax[1]",
+                    ["softmax[1]"] = "<output>[1]"
+                }
             }}
         }
     }, param_repo, gconf)
+
+    return layer_repo
 end
 
 function get_network(layer_repo)
-    return layer_repo:get_layer("main")
+    return layer_repo:get_layer("ce_output")
+end
+
+function get_decode_network(layer_repo)
+    return layer_repo:get_layer("softmax_output")
+end
+
+function get_global_transf(layer_repo)
+    return layer_repo:get_layer("global_transf")
 end
 
 function make_readers(scp_file, layer_repo)
@@ -145,18 +181,18 @@ function get_input_order()
     return {"main_scp", "phone_state"}
 end
 
-function get_accuracy(sublayer_repo)
-    local ce_crit = sublayer_repo:get_layer("ce_crit")
+function get_accuracy(layer_repo)
+    local ce_crit = layer_repo:get_layer("ce_crit")
     return ce_crit.total_correct / ce_crit.total_frames * 100
 end
 
-function print_stat(sublayer_repo)
-    local ce_crit = sublayer_repo:get_layer("ce_crit")
+function print_stat(layer_repo)
+    local ce_crit = layer_repo:get_layer("ce_crit")
     nerv.info("*** training stat begin ***")
     nerv.printf("cross entropy:\t\t%.8f\n", ce_crit.total_ce)
     nerv.printf("correct:\t\t%d\n", ce_crit.total_correct)
     nerv.printf("frames:\t\t\t%d\n", ce_crit.total_frames)
     nerv.printf("err/frm:\t\t%.8f\n", ce_crit.total_ce / ce_crit.total_frames)
-    nerv.printf("accuracy:\t\t%.3f%%\n", get_accuracy(sublayer_repo))
+    nerv.printf("accuracy:\t\t%.3f%%\n", get_accuracy(layer_repo))
     nerv.info("*** training stat end ***")
 end
diff --git a/embedding_example/swb_baseline_decode.lua b/nerv/examples/swb_baseline_basic.lua
index 8cdb320..c47ec3e 100644
--- a/embedding_example/swb_baseline_decode.lua
+++ b/nerv/examples/swb_baseline_basic.lua
@@ -10,8 +10,8 @@ gconf = {lrate = 0.8, wcost = 1e-6, momentum = 0.9,
                 "/slfs1/users/mfy43/swb_global_transf.nerv"},
         debug = false}
 
-function make_sublayer_repo(param_repo)
-    return nerv.LayerRepo(
+function make_layer_repo(param_repo)
+    local layer_repo = nerv.LayerRepo(
     {
         -- global transf
         ["nerv.BiasLayer"] =
@@ -54,21 +54,19 @@ function make_sublayer_repo(param_repo)
             sigmoid5 = {{}, {dim_in = {2048}, dim_out = {2048}}},
             sigmoid6 = {{}, {dim_in = {2048}, dim_out = {2048}}}
         },
-        ["nerv.SoftmaxLayer"] =
+        ["nerv.SoftmaxCELayer"] =
         {
-            soutput = {{}, {dim_in = {3001}, dim_out = {3001}}}
+            ce_crit = {{}, {dim_in = {3001, 1}, dim_out = {1}, compressed = true}}
         }
     }, param_repo, gconf)
-end
 
-function make_layer_repo(sublayer_repo, param_repo)
-    return nerv.LayerRepo(
+    layer_repo:add_layers(
     {
         ["nerv.DAGLayer"] =
         {
             global_transf = {{}, {
                 dim_in = {429}, dim_out = {429},
-                sub_layers = sublayer_repo,
+                sub_layers = layer_repo,
                 connections = {
                     ["<input>[1]"] = "blayer1[1]",
                     ["blayer1[1]"] = "wlayer1[1]",
@@ -78,8 +76,8 @@ function make_layer_repo(sublayer_repo, param_repo)
                 }
             }},
             main = {{}, {
-                dim_in = {429}, dim_out = {3001},
-                sub_layers = sublayer_repo,
+                dim_in = {429, 1}, dim_out = {1},
+                sub_layers = layer_repo,
                 connections = {
                     ["<input>[1]"] = "affine0[1]",
                     ["affine0[1]"] = "sigmoid0[1]",
@@ -96,19 +94,68 @@ function make_layer_repo(sublayer_repo, param_repo)
                     ["sigmoid5[1]"] = "affine6[1]",
                     ["affine6[1]"] = "sigmoid6[1]",
                     ["sigmoid6[1]"] = "affine7[1]",
-                    ["affine7[1]"] = "soutput[1]",
-                    ["soutput[1]"] = "<output>[1]"
+                    ["affine7[1]"] = "ce_crit[1]",
+                    ["<input>[2]"] = "ce_crit[2]",
+                    ["ce_crit[1]"] = "<output>[1]"
                 }
             }}
         }
     }, param_repo, gconf)
+    return layer_repo
 end
 
 function get_network(layer_repo)
     return layer_repo:get_layer("main")
 end
 
+function make_readers(scp_file, layer_repo)
+    return {
+                {reader = nerv.TNetReader(gconf,
+                    {
+                        id = "main_scp",
+                        scp_file = scp_file,
+                        conf_file = gconf.htk_conf,
+                        frm_ext = gconf.frm_ext,
+                        mlfs = {
+                            phone_state = {
+                                file = "/slfs1/users/mfy43/swb_ivec/ref.mlf",
+                                format = "map",
+                                format_arg = "/slfs1/users/mfy43/swb_ivec/dict",
+                                dir = "*/",
+                                ext = "lab"
+                            }
+                        },
+                        global_transf = layer_repo:get_layer("global_transf")
+                    }),
+                data = {main_scp = 429, phone_state = 1}}
+            }
+end
+
+function make_buffer(readers)
+    return nerv.SGDBuffer(gconf,
+        {
+            buffer_size = gconf.buffer_size,
+            randomize = gconf.randomize,
+            readers = readers
+        })
+end
+
+function get_input_order()
+    return {"main_scp", "phone_state"}
+end
+
+function get_accuracy(layer_repo)
+    local ce_crit = layer_repo:get_layer("ce_crit")
+    return ce_crit.total_correct / ce_crit.total_frames * 100
+end
 
-function get_global_transf(layer_repo)
-    return layer_repo:get_layer("global_transf")
+function print_stat(layer_repo)
+    local ce_crit = layer_repo:get_layer("ce_crit")
+    nerv.info("*** training stat begin ***")
+    nerv.printf("cross entropy:\t\t%.8f\n", ce_crit.total_ce)
+    nerv.printf("correct:\t\t%d\n", ce_crit.total_correct)
+    nerv.printf("frames:\t\t\t%d\n", ce_crit.total_frames)
+    nerv.printf("err/frm:\t\t%.8f\n", ce_crit.total_ce / ce_crit.total_frames)
+    nerv.printf("accuracy:\t\t%.3f%%\n", get_accuracy(layer_repo))
+    nerv.info("*** training stat end ***")
 end
diff --git a/nerv/nn/layer_dag.lua b/nerv/nn/layer_dag.lua
index e5c1ac7..e9d4d86 100644
--- a/nerv/nn/layer_dag.lua
+++ b/nerv/nn/layer_dag.lua
@@ -112,7 +112,7 @@ function DAGLayer:__init(id, global_conf, layer_conf)
         end
     end
     for i = 1, #queue do
-        nerv.info("enqueued layer: %s", queue[i].layer.id)
+        nerv.info("enqueued layer: %s %s", queue[i].layer, queue[i].layer.id)
     end
 
     for id, ref in pairs(layers) do
@@ -125,6 +125,7 @@ function DAGLayer:__init(id, global_conf, layer_conf)
     self.layers = layers
     self.inputs = inputs
     self.outputs = outputs
+    self.id = id
     self.dim_in = dim_in
     self.dim_out = dim_out
     self.parsed_conn = parsed_conn
diff --git a/nerv/nn/layer_repo.lua b/nerv/nn/layer_repo.lua
index 602c37c..ef333a7 100644
--- a/nerv/nn/layer_repo.lua
+++ b/nerv/nn/layer_repo.lua
@@ -1,7 +1,12 @@
 local LayerRepo = nerv.class("nerv.LayerRepo")
 
 function LayerRepo:__init(layer_spec, param_repo, global_conf)
-    local layers = {}
+    self.layers = {}
+    self:add_layers(layer_spec, param_repo, global_conf)
+end
+
+function LayerRepo:add_layers(layer_spec, param_repo, global_conf)
+    local layers = self.layers
     for ltype, llist in pairs(layer_spec) do
         local layer_type = nerv.get_type(ltype)
         for id, spec in pairs(llist) do
@@ -22,7 +27,6 @@ function LayerRepo:__init(layer_spec, param_repo, global_conf)
             layers[id] = layer_type(id, global_conf, layer_config)
         end
     end
-    self.layers = layers
 end
 
 function LayerRepo:get_layer(lid)