...

Merge remote-tracking branch 'upstream/master'

1 files changed, 20 insertions, 9 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()