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authortxh18 <[email protected]>2015-11-15 22:44:02 +0800
committertxh18 <[email protected]>2015-11-15 22:44:02 +0800
commitef40688d5a0a3b7eae18dc364a40ae4e8e7619e7 (patch)
treeec12c403ddc5a2c0d0928f3a8249e74b4c7d5916
parent5760914d95059777c5e475f3c42d1b32983235a3 (diff)
added timer
-rw-r--r--nerv/examples/lmptb/lm_trainer.lua4
-rw-r--r--nerv/examples/lmptb/rnn/tnn.lua8
-rw-r--r--nerv/examples/lmptb/tnn_ptb_main.lua3
3 files changed, 13 insertions, 2 deletions
diff --git a/nerv/examples/lmptb/lm_trainer.lua b/nerv/examples/lmptb/lm_trainer.lua
index d34634c..226873b 100644
--- a/nerv/examples/lmptb/lm_trainer.lua
+++ b/nerv/examples/lmptb/lm_trainer.lua
@@ -63,6 +63,10 @@ function LMTrainer.lm_process_file(global_conf, fn, tnn, do_train)
next_log_wcn = next_log_wcn + global_conf.log_w_num
printf("%s %d words processed %s.\n", global_conf.sche_log_pre, result["rnn"].cn_w, os.date())
printf("\t%s log prob per sample :%f.\n", global_conf.sche_log_pre, result:logp_sample("rnn"))
+ for key, value in pairs(global_conf.timer.rec) do
+ printf("\t [global_conf.timer]: time spent on %s:%.5fs\n", key, value)
+ end
+ global_conf.timer:flush()
nerv.LMUtil.wait(0.1)
end
diff --git a/nerv/examples/lmptb/rnn/tnn.lua b/nerv/examples/lmptb/rnn/tnn.lua
index 9850fe5..d6bf42e 100644
--- a/nerv/examples/lmptb/rnn/tnn.lua
+++ b/nerv/examples/lmptb/rnn/tnn.lua
@@ -384,8 +384,10 @@ function TNN:propagate_dfs(ref, t)
end
end
]]--
+ self.gconf.timer:tic("tnn_actual_layer_propagate")
ref.layer:propagate(ref.inputs_m[t], ref.outputs_m[t], t) --propagate!
-
+ self.gconf.timer:toc("tnn_actual_layer_propagate")
+
if (bit.band(self.feeds_now.flagsPack_now[t], bit.bor(nerv.TNN.FC.SEQ_START, nerv.TNN.FC.SEQ_END)) > 0) then --restore cross-border history
for i = 1, self.batch_size do
local seq_start = bit.band(self.feeds_now.flags_now[t][i], nerv.TNN.FC.SEQ_START)
@@ -487,10 +489,14 @@ function TNN:backpropagate_dfs(ref, t, do_update)
--ok, do back_propagate
--print("debug ok, back-propagating(or updating)")
if (do_update == false) then
+ self.gconf.timer:tic("tnn_actual_layer_backpropagate")
ref.layer:back_propagate(ref.err_inputs_m[t], ref.err_outputs_m[t], ref.inputs_m[t], ref.outputs_m[t], t)
+ self.gconf.timer:toc("tnn_actual_layer_backpropagate")
else
--print(ref.err_inputs_m[t][1])
+ self.gconf.timer:tic("tnn_actual_layer_update")
ref.layer:update(ref.err_inputs_m[t], ref.inputs_m[t], ref.outputs_m[t], t)
+ self.gconf.timer:toc("tnn_actual_layer_update")
end
if (do_update == false and bit.band(self.feeds_now.flagsPack_now[t], bit.bor(nerv.TNN.FC.SEQ_START, nerv.TNN.FC.SEQ_END)) > 0) then --flush cross-border errors
diff --git a/nerv/examples/lmptb/tnn_ptb_main.lua b/nerv/examples/lmptb/tnn_ptb_main.lua
index c875274..891487c 100644
--- a/nerv/examples/lmptb/tnn_ptb_main.lua
+++ b/nerv/examples/lmptb/tnn_ptb_main.lua
@@ -168,7 +168,7 @@ global_conf = {
mmat_type = nerv.MMatrixFloat,
nn_act_default = 0,
- hidden_size = 400,
+ hidden_size = 300, --set to 400 for a stable good test PPL
chunk_size = 15,
batch_size = 10,
max_iter = 35,
@@ -203,6 +203,7 @@ global_conf = {
chunk_size = 15,
batch_size = 10,
max_iter = 30,
+ decay_iter = 10,
param_random = function() return (math.random() / 5 - 0.1) end,
train_fn = train_fn,