...

1 files changed, 8 insertions, 6 deletions

diff --git a/examples/asr_trainer.lua b/examples/asr_trainer.lua
index b43a547..d72b763 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 +33,10 @@ function build_trainer(ifname)
             collectgarbage("collect")
         end
         print_stat(crit)
-        if bp then
+        if (not bp) and prefix ~= nil then
             nerv.info("writing back...")
-            cf = nerv.ChunkFile(ofname, "w")
+            local accu_cv = get_accuracy(crit)
+            cf = nerv.ChunkFile(prefix .. "_cv" .. accu_cv .. ".nerv", "w")
             for i, p in ipairs(network:get_params()) do
                 cf:write_chunk(p)
             end
@@ -65,9 +66,10 @@ 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)
+    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(pf0 .. "_iter" .. i .. "_tr" .. 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