use `trainer.lua` in acoustic model examplesbeta-1.1

3 files changed, 160 insertions, 158 deletions

diff --git a/nerv/examples/swb_baseline2.lua b/nerv/examples/swb_baseline2.lua
index 38cfb9a..87b01fa 100644
--- a/nerv/examples/swb_baseline2.lua
+++ b/nerv/examples/swb_baseline2.lua
@@ -1,65 +1,79 @@
 require 'htk_io'
-gconf = {lrate = 0.8, wcost = 1e-6, momentum = 0.9, frm_ext = 5,
-        rearrange = true, -- just to make the context order consistent with old results, deprecated
+gconf = {lrate = 0.8,
+        wcost = 1e-6,
+        momentum = 0.9,
+        frm_ext = 5,
+        rearrange = true, -- just to make the context order consistent with old TNet results, deprecated
         frm_trim = 5, -- trim the first and last 5 frames, TNet just does this, deprecated
+        chunk_size = 1,
         tr_scp = "/speechlab/users/mfy43/swb50/train_bp.scp",
         cv_scp = "/speechlab/users/mfy43/swb50/train_cv.scp",
+        ali = {file = "/speechlab/users/mfy43/swb50/ref.mlf",
+               format = "map",
+               format_arg = "/speechlab/users/mfy43/swb50/dict",
+               dir = "*/",
+               ext = "lab"},
         htk_conf = "/speechlab/users/mfy43/swb50/plp_0_d_a.conf",
         initialized_param = {"/speechlab/users/mfy43/swb50/swb_init.nerv",
                             "/speechlab/users/mfy43/swb50/swb_global_transf.nerv"},
-        chunk_size = 1}
+}
 
-function make_layer_repo(param_repo)
+local input_size = 429
+local output_size = 3001
+local hidden_size = 2048
+local trainer = nerv.Trainer
+
+function trainer:make_layer_repo(param_repo)
     local layer_repo = nerv.LayerRepo(
     {
         -- global transf
         ["nerv.BiasLayer"] =
         {
-            blayer1 = {dim_in = {429}, dim_out = {429}, params = {bias = "bias0"}},
-            blayer2 = {dim_in = {429}, dim_out = {429}, params = {bias = "bias1"}}
+            blayer1 = {dim_in = {input_size}, dim_out = {input_size}, params = {bias = "bias0"}},
+            blayer2 = {dim_in = {input_size}, dim_out = {input_size}, params = {bias = "bias1"}}
         },
         ["nerv.WindowLayer"] =
         {
-            wlayer1 = {dim_in = {429}, dim_out = {429}, params = {window = "window0"}},
-            wlayer2 = {dim_in = {429}, dim_out = {429}, params = {window = "window1"}}
+            wlayer1 = {dim_in = {input_size}, dim_out = {input_size}, params = {window = "window0"}},
+            wlayer2 = {dim_in = {input_size}, dim_out = {input_size}, params = {window = "window1"}}
         },
         -- biased linearity
         ["nerv.AffineLayer"] =
         {
-            affine0 = {dim_in = {429}, dim_out = {2048},
+            affine0 = {dim_in = {input_size}, dim_out = {hidden_size},
                         params = {ltp = "affine0_ltp", bp = "affine0_bp"}},
-            affine1 = {dim_in = {2048}, dim_out = {2048},
+            affine1 = {dim_in = {hidden_size}, dim_out = {hidden_size},
                         params = {ltp = "affine1_ltp", bp = "affine1_bp"}},
-            affine2 = {dim_in = {2048}, dim_out = {2048},
+            affine2 = {dim_in = {hidden_size}, dim_out = {hidden_size},
                         params = {ltp = "affine2_ltp", bp = "affine2_bp"}},
-            affine3 = {dim_in = {2048}, dim_out = {2048},
+            affine3 = {dim_in = {hidden_size}, dim_out = {hidden_size},
                         params = {ltp = "affine3_ltp", bp = "affine3_bp"}},
-            affine4 = {dim_in = {2048}, dim_out = {2048},
+            affine4 = {dim_in = {hidden_size}, dim_out = {hidden_size},
                         params = {ltp = "affine4_ltp", bp = "affine4_bp"}},
-            affine5 = {dim_in = {2048}, dim_out = {2048},
+            affine5 = {dim_in = {hidden_size}, dim_out = {hidden_size},
                         params = {ltp = "affine5_ltp", bp = "affine5_bp"}},
-            affine6 = {dim_in = {2048}, dim_out = {2048},
+            affine6 = {dim_in = {hidden_size}, dim_out = {hidden_size},
                         params = {ltp = "affine6_ltp", bp = "affine6_bp"}},
-            affine7 = {dim_in = {2048}, dim_out = {3001},
+            affine7 = {dim_in = {hidden_size}, dim_out = {output_size},
                         params = {ltp = "affine7_ltp", bp = "affine7_bp"}}
         },
         ["nerv.SigmoidLayer"] =
         {
-            sigmoid0 = {dim_in = {2048}, dim_out = {2048}},
-            sigmoid1 = {dim_in = {2048}, dim_out = {2048}},
-            sigmoid2 = {dim_in = {2048}, dim_out = {2048}},
-            sigmoid3 = {dim_in = {2048}, dim_out = {2048}},
-            sigmoid4 = {dim_in = {2048}, dim_out = {2048}},
-            sigmoid5 = {dim_in = {2048}, dim_out = {2048}},
-            sigmoid6 = {dim_in = {2048}, dim_out = {2048}}
+            sigmoid0 = {dim_in = {hidden_size}, dim_out = {hidden_size}},
+            sigmoid1 = {dim_in = {hidden_size}, dim_out = {hidden_size}},
+            sigmoid2 = {dim_in = {hidden_size}, dim_out = {hidden_size}},
+            sigmoid3 = {dim_in = {hidden_size}, dim_out = {hidden_size}},
+            sigmoid4 = {dim_in = {hidden_size}, dim_out = {hidden_size}},
+            sigmoid5 = {dim_in = {hidden_size}, dim_out = {hidden_size}},
+            sigmoid6 = {dim_in = {hidden_size}, dim_out = {hidden_size}}
         },
         ["nerv.SoftmaxCELayer"] = -- softmax + ce criterion layer for finetune output
         {
-            ce_crit = {dim_in = {3001, 1}, dim_out = {1}, compressed = true}
+            ce_crit = {dim_in = {output_size, 1}, dim_out = {1}, compressed = true}
         },
         ["nerv.SoftmaxLayer"] = -- softmax for decode output
         {
-            softmax = {dim_in = {3001}, dim_out = {3001}}
+            softmax = {dim_in = {output_size}, dim_out = {output_size}}
         }
     }, param_repo, gconf)
 
@@ -68,7 +82,7 @@ function make_layer_repo(param_repo)
         ["nerv.GraphLayer"] =
         {
             global_transf = {
-                dim_in = {429}, dim_out = {429},
+                dim_in = {input_size}, dim_out = {input_size},
                 layer_repo = layer_repo,
                 connections = {
                     {"<input>[1]", "blayer1[1]", 0},
@@ -79,7 +93,7 @@ function make_layer_repo(param_repo)
                 }
             },
             main = {
-                dim_in = {429}, dim_out = {3001},
+                dim_in = {input_size}, dim_out = {output_size},
                 layer_repo = layer_repo,
                 connections = {
                     {"<input>[1]", "affine0[1]", 0},
@@ -108,20 +122,22 @@ function make_layer_repo(param_repo)
         ["nerv.GraphLayer"] =
         {
             ce_output = {
-                dim_in = {429, 1}, dim_out = {1},
+                dim_in = {input_size, 1}, dim_out = {1},
                 layer_repo = layer_repo,
                 connections = {
-                    {"<input>[1]", "main[1]", 0},
+                    {"<input>[1]", "global_transf[1]", 0},
+                    {"global_transf[1]", "main[1]", 0},
                     {"main[1]", "ce_crit[1]", 0},
                     {"<input>[2]", "ce_crit[2]", 0},
                     {"ce_crit[1]", "<output>[1]", 0}
                 }
             },
             softmax_output = {
-                dim_in = {429}, dim_out = {3001},
+                dim_in = {input_size}, dim_out = {output_size},
                 layer_repo = layer_repo,
                 connections = {
-                    {"<input>[1]", "main[1]", 0},
+                    {"<input>[1]", "global_transf[1]", 0},
+                    {"global_transf[1]", "main[1]", 0},
                     {"main[1]", "softmax[1]", 0},
                     {"softmax[1]", "<output>[1]", 0}
                 }
@@ -132,73 +148,59 @@ function make_layer_repo(param_repo)
     return layer_repo
 end
 
-function get_network(layer_repo)
+function trainer:get_network(layer_repo)
     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)
-    return {
-                {reader = nerv.HTKReader(gconf,
+function trainer:get_readers(dataset)
+    local function reader_gen(scp, ali)
+        return {{reader = nerv.HTKReader(gconf,
                     {
                         id = "main_scp",
-                        scp_file = scp_file,
+                        scp_file = scp,
                         conf_file = gconf.htk_conf,
                         frm_ext = gconf.frm_ext,
                         mlfs = {
-                            phone_state = {
-                                file = "/speechlab/users/mfy43/swb50/ref.mlf",
-                                format = "map",
-                                format_arg = "/speechlab/users/mfy43/swb50/dict",
-                                dir = "*/",
-                                ext = "lab"
-                            }
+                            phone_state = ali
                         }
                     }),
-                data = {main_scp = 429, phone_state = 1}}
-            }
+                data = {main_scp = input_size, phone_state = 1}}}
+    end
+    if dataset == 'train' then
+        return reader_gen(gconf.tr_scp, gconf.tr_ali or gconf.ali)
+    elseif dataset == 'validate' then
+        return reader_gen(gconf.cv_scp, gconf.cv_ali or gconf.ali)
+    else
+        nerv.error('no such dataset')
+    end
 end
 
-function make_buffer(readers)
-    return nerv.FrmBuffer(gconf,
-        {
-            buffer_size = gconf.buffer_size,
-            batch_size = gconf.batch_size,
-            chunk_size = gconf.chunk_size,
-            randomize = gconf.randomize,
-            readers = readers,
-            use_gpu = true
-        })
+function trainer:get_input_order()
+    return {"main_scp", "phone_state"}
 end
 
-function get_input_order()
-    return {{id = "main_scp", global_transf = true},
-            {id = "phone_state"}}
+function trainer:get_decode_input_order()
+    return {"main_scp"}
 end
 
-function get_decode_input_order()
-    return {{id = "main_scp", global_transf = true}}
+function trainer:get_error()
+    local ce_crit = self.layer_repo:get_layer("ce_crit")
+    return ce_crit.total_ce / ce_crit.total_frames
 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
+function trainer:mini_batch_afterprocess(cnt, info)
+    if cnt % 1000 == 0 then
+        self:epoch_afterprocess()
+    end
 end
 
-function print_stat(layer_repo)
-    local ce_crit = layer_repo:get_layer("ce_crit")
+function trainer:epoch_afterprocess()
+    local ce_crit = self.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.printf("accuracy:\t\t%.3f%%\n", ce_crit.total_correct / ce_crit.total_frames * 100)
     nerv.info("*** training stat end ***")
 end
diff --git a/nerv/examples/timit_baseline2.lua b/nerv/examples/timit_baseline2.lua
index 658aa2e..313156f 100644
--- a/nerv/examples/timit_baseline2.lua
+++ b/nerv/examples/timit_baseline2.lua
@@ -1,62 +1,77 @@
 require 'kaldi_io'
-gconf = {lrate = 0.8, wcost = 1e-6, momentum = 0.9, frm_ext = 5,
+gconf = {lrate = 0.8,
+        wcost = 1e-6,
+        momentum = 0.9,
+        frm_ext = 5,
+        chunk_size = 1,
         tr_scp = "ark:/speechlab/tools/KALDI/kaldi-master/src/featbin/copy-feats " ..
                     "scp:/speechlab/users/mfy43/timit/s5/exp/dnn4_nerv_dnn/train.scp ark:- |",
         cv_scp = "ark:/speechlab/tools/KALDI/kaldi-master/src/featbin/copy-feats " ..
                     "scp:/speechlab/users/mfy43/timit/s5/exp/dnn4_nerv_dnn/cv.scp ark:- |",
+        ali = {targets_rspecifier = "ark:/speechlab/tools/KALDI/kaldi-master/src/bin/ali-to-pdf " ..
+                                                        "/speechlab/users/mfy43/timit/s5/exp/tri3_ali/final.mdl " ..
+                                                        "\"ark:gunzip -c /speechlab/users/mfy43/timit/s5/exp/tri3_ali/ali.*.gz |\" " ..
+                                                        "ark:- | " ..
+                                                    "/speechlab/tools/KALDI/kaldi-master/src/bin/ali-to-post " ..
+                                                        "ark:- ark:- |"},
         initialized_param = {"/speechlab/users/mfy43/timit/s5/exp/dnn4_nerv_dnn/nnet_init.nerv",
                             "/speechlab/users/mfy43/timit/s5/exp/dnn4_nerv_dnn/nnet_output.nerv",
                             "/speechlab/users/mfy43/timit/s5/exp/dnn4_nerv_dnn/nnet_trans.nerv"},
          -- params in nnet_trans.nerv are included in the trained model
-        decode_param = {"/speechlab/users/mfy43/timit/s5/nerv_20160311205342/nnet_init_20160311211609_iter_13_lr0.013437_tr72.572_cv58.709.nerv"},
-        chunk_size = 1}
+        decode_param = {"/speechlab/users/mfy43/timit/s5/nerv_2016-05-06_17:40:54/2016-05-06_19:44:43_iter_20_lr0.012500_tr0.867_cv1.464.nerv"}
+}
 
-function make_layer_repo(param_repo)
+local input_size = 440
+local output_size = 1959
+local hidden_size = 1024
+local trainer = nerv.Trainer
+
+function trainer:make_layer_repo(param_repo)
     local layer_repo = nerv.LayerRepo(
     {
         -- global transf
         ["nerv.BiasLayer"] =
         {
-            blayer1 = {dim_in = {440}, dim_out = {440}, params = {bias = "bias0"}}
+            blayer1 = {dim_in = {input_size}, dim_out = {input_size}, params = {bias = "bias0"}, no_update_all = true}
         },
         ["nerv.WindowLayer"] =
         {
-            wlayer1 = {dim_in = {440}, dim_out = {440}, params = {window = "window0"}}
+            wlayer1 = {dim_in = {input_size}, dim_out = {input_size}, params = {window = "window0"}, no_update_all = true}
         },
         -- biased linearity
         ["nerv.AffineLayer"] =
         {
-            affine0 = {dim_in = {440}, dim_out = {1024},
+            affine0 = {dim_in = {input_size}, dim_out = {hidden_size},
                         params = {ltp = "affine0_ltp", bp = "affine0_bp"}},
-            affine1 = {dim_in = {1024}, dim_out = {1024},
+            affine1 = {dim_in = {hidden_size}, dim_out = {hidden_size},
                         params = {ltp = "affine1_ltp", bp = "affine1_bp"}},
-            affine2 = {dim_in = {1024}, dim_out = {1024},
+            affine2 = {dim_in = {hidden_size}, dim_out = {hidden_size},
                         params = {ltp = "affine2_ltp", bp = "affine2_bp"}},
-            affine3 = {dim_in = {1024}, dim_out = {1024},
+            affine3 = {dim_in = {hidden_size}, dim_out = {hidden_size},
                         params = {ltp = "affine3_ltp", bp = "affine3_bp"}},
-            affine4 = {dim_in = {1024}, dim_out = {1024},
+            affine4 = {dim_in = {hidden_size}, dim_out = {hidden_size},
                         params = {ltp = "affine4_ltp", bp = "affine4_bp"}},
-            affine5 = {dim_in = {1024}, dim_out = {1024},
+            affine5 = {dim_in = {hidden_size}, dim_out = {hidden_size},
                         params = {ltp = "affine5_ltp", bp = "affine5_bp"}},
-            affine6 = {dim_in = {1024}, dim_out = {1959},
+            affine6 = {dim_in = {hidden_size}, dim_out = {output_size},
                         params = {ltp = "affine6_ltp", bp = "affine6_bp"}}
         },
         ["nerv.SigmoidLayer"] =
         {
-            sigmoid0 = {dim_in = {1024}, dim_out = {1024}},
-            sigmoid1 = {dim_in = {1024}, dim_out = {1024}},
-            sigmoid2 = {dim_in = {1024}, dim_out = {1024}},
-            sigmoid3 = {dim_in = {1024}, dim_out = {1024}},
-            sigmoid4 = {dim_in = {1024}, dim_out = {1024}},
-            sigmoid5 = {dim_in = {1024}, dim_out = {1024}}
+            sigmoid0 = {dim_in = {hidden_size}, dim_out = {hidden_size}},
+            sigmoid1 = {dim_in = {hidden_size}, dim_out = {hidden_size}},
+            sigmoid2 = {dim_in = {hidden_size}, dim_out = {hidden_size}},
+            sigmoid3 = {dim_in = {hidden_size}, dim_out = {hidden_size}},
+            sigmoid4 = {dim_in = {hidden_size}, dim_out = {hidden_size}},
+            sigmoid5 = {dim_in = {hidden_size}, dim_out = {hidden_size}}
         },
         ["nerv.SoftmaxCELayer"] = -- softmax + ce criterion layer for finetune output
         {
-            ce_crit = {dim_in = {1959, 1}, dim_out = {1}, compressed = true}
+            ce_crit = {dim_in = {output_size, 1}, dim_out = {1}, compressed = true}
         },
         ["nerv.SoftmaxLayer"] = -- softmax for decode output
         {
-            softmax = {dim_in = {1959}, dim_out = {1959}}
+            softmax = {dim_in = {output_size}, dim_out = {output_size}}
         }
     }, param_repo, gconf)
 
@@ -65,7 +80,7 @@ function make_layer_repo(param_repo)
         ["nerv.GraphLayer"] =
         {
             global_transf = {
-                dim_in = {440}, dim_out = {440},
+                dim_in = {input_size}, dim_out = {input_size},
                 layer_repo = layer_repo,
                 connections = {
                     {"<input>[1]", "blayer1[1]", 0},
@@ -74,7 +89,7 @@ function make_layer_repo(param_repo)
                 }
             },
             main = {
-                dim_in = {440}, dim_out = {1959},
+                dim_in = {input_size}, dim_out = {output_size},
                 layer_repo = layer_repo,
                 connections = {
                     {"<input>[1]", "affine0[1]", 0},
@@ -101,20 +116,22 @@ function make_layer_repo(param_repo)
         ["nerv.GraphLayer"] =
         {
             ce_output = {
-                dim_in = {440, 1}, dim_out = {1},
+                dim_in = {input_size, 1}, dim_out = {1},
                 layer_repo = layer_repo,
                 connections = {
-                    {"<input>[1]", "main[1]", 0},
+                    {"<input>[1]", "global_transf[1]", 0},
+                    {"global_transf[1]", "main[1]", 0},
                     {"main[1]", "ce_crit[1]", 0},
                     {"<input>[2]", "ce_crit[2]", 0},
                     {"ce_crit[1]", "<output>[1]", 0}
                 }
             },
             softmax_output = {
-                dim_in = {440}, dim_out = {1959},
+                dim_in = {input_size}, dim_out = {output_size},
                 layer_repo = layer_repo,
                 connections = {
-                    {"<input>[1]", "main[1]", 0},
+                    {"<input>[1]", "global_transf[1]", 0},
+                    {"global_transf[1]", "main[1]", 0},
                     {"main[1]", "softmax[1]", 0},
                     {"softmax[1]", "<output>[1]", 0}
                 }
@@ -125,90 +142,73 @@ function make_layer_repo(param_repo)
     return layer_repo
 end
 
-function get_network(layer_repo)
+function trainer:get_network(layer_repo)
     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)
-    return {
-                {reader = nerv.KaldiReader(gconf,
+function trainer:get_readers(dataset)
+    local function reader_gen(scp, ali)
+        return {{reader = nerv.KaldiReader(gconf,
                     {
                         id = "main_scp",
-                        feature_rspecifier = scp_file,
-                        conf_file = gconf.htk_conf,
+                        feature_rspecifier = scp,
                         frm_ext = gconf.frm_ext,
                         mlfs = {
-                            phone_state = {
-                                targets_rspecifier = "ark:/speechlab/tools/KALDI/kaldi-master/src/bin/ali-to-pdf " ..
-                                                        "/speechlab/users/mfy43/timit/s5/exp/tri3_ali/final.mdl " ..
-                                                        "\"ark:gunzip -c /speechlab/users/mfy43/timit/s5/exp/tri3_ali/ali.*.gz |\" " ..
-                                                        "ark:- | " ..
-                                                    "/speechlab/tools/KALDI/kaldi-master/src/bin/ali-to-post " ..
-                                                        "ark:- ark:- |",
-                                format = "map"
-                            }
+                            phone_state = ali
                         }
                     }),
-                data = {main_scp = 440, phone_state = 1}}
-            }
+                data = {main_scp = input_size, phone_state = 1}}}
+    end
+    if dataset == 'train' then
+        return reader_gen(gconf.tr_scp, gconf.tr_ali or gconf.ali)
+    elseif dataset == 'validate' then
+        return reader_gen(gconf.cv_scp, gconf.cv_ali or gconf.ali)
+    else
+        nerv.error('no such dataset')
+    end
+end
+
+function trainer:get_input_order()
+    return {"main_scp", "phone_state"}
+end
+
+function trainer:get_decode_network(layer_repo)
+    return layer_repo:get_layer("softmax_output")
 end
 
-function make_decode_readers(scp_file, layer_repo)
-    return {
-                {reader = nerv.KaldiReader(gconf,
+function trainer:make_decode_readers(scp_file)
+    return {{reader = nerv.KaldiReader(gconf,
                     {
                         id = "main_scp",
                         feature_rspecifier = scp_file,
-                        conf_file = gconf.htk_conf,
                         frm_ext = gconf.frm_ext,
-                        mlfs = {},
-                        need_key = true
+                        mlfs = {}
                     }),
-                data = {main_scp = 440, phone_state = 1}}
-            }
-end
-
-function make_buffer(readers)
-    return nerv.FrmBuffer(gconf,
-        {
-            buffer_size = gconf.buffer_size,
-            batch_size = gconf.batch_size,
-            chunk_size = gconf.chunk_size,
-            randomize = gconf.randomize,
-            readers = readers,
-            use_gpu = true
-        })
+                data = {main_scp = input_size, phone_state = 1}}}
 end
 
-function get_input_order()
-    return {{id = "main_scp", global_transf = true},
-            {id = "phone_state"}}
+function trainer:get_decode_input_order()
+    return {"main_scp"}
 end
 
-function get_decode_input_order()
-    return {{id = "main_scp", global_transf = true}}
+function trainer:get_error()
+    local ce_crit = self.layer_repo:get_layer("ce_crit")
+    return ce_crit.total_ce / ce_crit.total_frames
 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
+function trainer:mini_batch_afterprocess(cnt, info)
+    if cnt % 1000 == 0 then
+        self:epoch_afterprocess()
+    end
 end
 
-function print_stat(layer_repo)
-    local ce_crit = layer_repo:get_layer("ce_crit")
+function trainer:epoch_afterprocess()
+    local ce_crit = self.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.printf("accuracy:\t\t%.3f%%\n", ce_crit.total_correct / ce_crit.total_frames * 100)
     nerv.info("*** training stat end ***")
 end
diff --git a/nerv/examples/trainer.lua b/nerv/examples/trainer.lua
index 8e3efcb..f6c7a5a 100644
--- a/nerv/examples/trainer.lua
+++ b/nerv/examples/trainer.lua
@@ -36,7 +36,7 @@ local function make_options(spec)
 end
 
 local function print_help(options)
-    nerv.printf("Usage: <asr_trainer.lua> [options] network_config.lua\n")
+    nerv.printf("Usage: <trainer.lua> [options] <network_config.lua>\n")
     nerv.print_usage(options)
 end