1 files changed, 21 insertions, 8 deletions
diff --git a/examples/test_nn_lib.lua b/examples/test_nn_lib.lua
index 04fd7d6..6fdbd67 100644
--- a/examples/test_nn_lib.lua
+++ b/examples/test_nn_lib.lua
@@ -117,7 +117,7 @@ tnet_reader = nerv.TNetReader(gconf,
 buffer = nerv.SGDBuffer(gconf,
     {
         buffer_size = 81920,
-        -- randomize = true,
+        randomize = true,
         readers = {
             { reader = tnet_reader,
               data = {main_scp = 429, ref = 1}}
@@ -128,9 +128,12 @@ sm = sublayer_repo:get_layer("softmax_ce0")
 main = layer_repo:get_layer("main")
 main:init(gconf.batch_size)
 gconf.cnt = 0
+-- data = buffer:get_data()
+-- input = {data.main_scp, data.ref}
+-- while true do
 for data in buffer.get_data, buffer do
-    if gconf.cnt == 1000 then break end
-    gconf.cnt = gconf.cnt + 1
+--    if gconf.cnt == 100 then break end
+--    gconf.cnt = gconf.cnt + 1
 
     input = {data.main_scp, data.ref}
     output = {}
@@ -141,11 +144,21 @@ for data in buffer.get_data, buffer do
     main:back_propagate(err_output, err_input, input, output)
     main:update(err_input, input, output)
 
-    nerv.utils.printf("cross entropy: %.8f\n", sm.total_ce)
-    nerv.utils.printf("correct: %d\n", sm.total_correct)
-    nerv.utils.printf("frames: %d\n", sm.total_frames)
-    nerv.utils.printf("err/frm: %.8f\n", sm.total_ce / sm.total_frames)
-    nerv.utils.printf("accuracy: %.8f\n", sm.total_correct / sm.total_frames)
+--    nerv.utils.printf("cross entropy: %.8f\n", sm.total_ce)
+--    nerv.utils.printf("correct: %d\n", sm.total_correct)
+--    nerv.utils.printf("frames: %d\n", sm.total_frames)
+--    nerv.utils.printf("err/frm: %.8f\n", sm.total_ce / sm.total_frames)
+--    nerv.utils.printf("accuracy: %.8f\n", sm.total_correct / sm.total_frames)
     collectgarbage("collect")
 end
+nerv.utils.printf("cross entropy: %.8f\n", sm.total_ce)
+nerv.utils.printf("correct: %d\n", sm.total_correct)
+nerv.utils.printf("accuracy: %.3f%%\n", sm.total_correct / sm.total_frames * 100)
+nerv.utils.printf("writing back...\n")
+cf = nerv.ChunkFile("output.nerv", "w")
+for i, p in ipairs(main:get_params()) do
+    print(p)
+    cf:write_chunk(p)
+end
+cf:close()
 nerv.Matrix.print_profile()