...

Merge remote-tracking branch 'upstream/master'

2 files changed, 27 insertions, 16 deletions

diff --git a/examples/asr_trainer.lua b/examples/asr_trainer.lua
index b43a547..05d770f 100644
--- a/examples/asr_trainer.lua
+++ b/examples/asr_trainer.lua
@@ -4,7 +4,7 @@ function build_trainer(ifname)
     local layer_repo = make_layer_repo(sublayer_repo, param_repo)
     local crit = get_criterion_layer(sublayer_repo)
     local network = get_network(layer_repo)
-    local iterative_trainer = function (ofname, scp_file, bp)
+    local iterative_trainer = function (prefix, scp_file, bp)
         gconf.randomize = bp
         -- build buffer
         local buffer = make_buffer(make_reader(scp_file, layer_repo))
@@ -18,7 +18,7 @@ function build_trainer(ifname)
                 print_stat(crit)
                 gconf.cnt = 0
             end
-            if gconf.cnt == 100 then break end
+--            if gconf.cnt == 100 then break end
 
             input = {data.main_scp, data.phone_state}
             output = {}
@@ -33,9 +33,12 @@ function build_trainer(ifname)
             collectgarbage("collect")
         end
         print_stat(crit)
-        if bp then
+        nerv.CuMatrix.print_profile()
+        if (not bp) and prefix ~= nil then
             nerv.info("writing back...")
-            cf = nerv.ChunkFile(ofname, "w")
+            local fname = string.format("%s_cv%.3f.nerv",
+                            prefix, get_accuracy(crit))
+            cf = nerv.ChunkFile(fname, "w")
             for i, p in ipairs(network:get_params()) do
                 cf:write_chunk(p)
             end
@@ -52,7 +55,7 @@ halving_factor = 0.6
 end_halving_inc = 0.1
 min_iter = 1
 max_iter = 20
-min_halving = 6
+min_halving = 5
 gconf.batch_size = 256
 gconf.buffer_size = 81920
 
@@ -64,10 +67,18 @@ local do_halving = false
 
 nerv.info("initial cross validation: %.3f", accu_best)
 for i = 1, max_iter do
-    nerv.info("iteration %d with lrate = %.6f", i, gconf.lrate)
-    local accu_tr = trainer(pf0 .. "_iter" .. i .. ".nerv", gconf.tr_scp, true)
+    nerv.info("[NN] begin iteration %d with lrate = %.6f", i, gconf.lrate)
+    local accu_tr = trainer(nil, gconf.tr_scp, true)
     nerv.info("[TR] training set %d: %.3f", i, accu_tr)
-    local accu_new = trainer(nil, gconf.cv_scp, false)
+    local accu_new = trainer(
+                        string.format("%s_%s_iter_%d_lr%f_tr%.3f",
+                            string.gsub(
+                                (string.gsub(pf0, "(.*/)(.*)", "%2")),
+                                "(.*)%..*", "%1"),
+                            os.date("%Y%m%d%H%M%S"),
+                            i, gconf.lrate,
+                            accu_tr),
+                        gconf.cv_scp, false)
     nerv.info("[CV] cross validation %d: %.3f", i, accu_new)
     -- TODO: revert the weights
     local accu_diff = accu_new - accu_best
@@ -83,5 +94,5 @@ for i = 1, max_iter do
     if accu_new > accu_best then
         accu_best = accu_new
     end
+--    nerv.Matrix.print_profile()
 end
-nerv.Matrix.print_profile()
diff --git a/examples/swb_baseline.lua b/examples/swb_baseline.lua
index f536777..28cc6d5 100644
--- a/examples/swb_baseline.lua
+++ b/examples/swb_baseline.lua
@@ -6,8 +6,8 @@ gconf = {lrate = 0.8, wcost = 1e-6, momentum = 0.9,
         tr_scp = "/slfs1/users/mfy43/swb_ivec/train_bp.scp",
         cv_scp = "/slfs1/users/mfy43/swb_ivec/train_cv.scp",
         htk_conf = "/slfs1/users/mfy43/swb_ivec/plp_0_d_a.conf",
-        global_transf = "global_transf.nerv",
-        initialized_param = "converted.nerv",
+        global_transf = "/slfs1/users/mfy43/swb_global_transf.nerv",
+        initialized_param = "/slfs1/users/mfy43/swb_init.nerv",
         debug = false}
 
 function make_param_repo(param_file)
@@ -154,10 +154,10 @@ end
 
 function print_stat(crit)
     nerv.info("*** training stat begin ***")
-    nerv.utils.printf("cross entropy:\t%.8f\n", crit.total_ce)
-    nerv.utils.printf("correct:\t%d\n", crit.total_correct)
-    nerv.utils.printf("frames:\t%d\n", crit.total_frames)
-    nerv.utils.printf("err/frm:\t%.8f\n", crit.total_ce / crit.total_frames)
-    nerv.utils.printf("accuracy:\t%.3f%%\n", get_accuracy(crit))
+    nerv.utils.printf("cross entropy:\t\t%.8f\n", crit.total_ce)
+    nerv.utils.printf("correct:\t\t%d\n", crit.total_correct)
+    nerv.utils.printf("frames:\t\t\t%d\n", crit.total_frames)
+    nerv.utils.printf("err/frm:\t\t%.8f\n", crit.total_ce / crit.total_frames)
+    nerv.utils.printf("accuracy:\t\t%.3f%%\n", get_accuracy(crit))
     nerv.info("*** training stat end ***")
 end