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authortxh18 <[email protected]>2015-11-16 11:44:43 +0800
committertxh18 <[email protected]>2015-11-16 11:44:43 +0800
commit267a486fb78a985cbfdc60ef8549b3128f716713 (patch)
treec60697e60ef5053203b5148cb3f0bfbf88a81c94
parentef40688d5a0a3b7eae18dc364a40ae4e8e7619e7 (diff)
fixed direct update, did not know the result
-rw-r--r--nerv/examples/lmptb/lm_trainer.lua1
-rw-r--r--nerv/examples/lmptb/tnn_ptb_main.lua8
-rw-r--r--nerv/layer/affine.lua23
-rw-r--r--nerv/layer/affine_recurrent.lua25
4 files changed, 35 insertions, 22 deletions
diff --git a/nerv/examples/lmptb/lm_trainer.lua b/nerv/examples/lmptb/lm_trainer.lua
index 226873b..2be97c8 100644
--- a/nerv/examples/lmptb/lm_trainer.lua
+++ b/nerv/examples/lmptb/lm_trainer.lua
@@ -16,6 +16,7 @@ function LMTrainer.lm_process_file(global_conf, fn, tnn, do_train)
local result = nerv.LMResult(global_conf, global_conf.vocab)
result:init("rnn")
+ global_conf.timer:flush()
tnn:flush_all() --caution: will also flush the inputs from the reader!
local next_log_wcn = global_conf.log_w_num
diff --git a/nerv/examples/lmptb/tnn_ptb_main.lua b/nerv/examples/lmptb/tnn_ptb_main.lua
index 891487c..19d0f8a 100644
--- a/nerv/examples/lmptb/tnn_ptb_main.lua
+++ b/nerv/examples/lmptb/tnn_ptb_main.lua
@@ -63,9 +63,11 @@ end
--Returns: nerv.LayerRepo
function prepare_layers(global_conf, paramRepo)
printf("%s preparing layers...\n", global_conf.sche_log_pre)
+
+ local du = true
--local recurrentLconfig = {{["bp"] = "bp_h", ["ltp_hh"] = "ltp_hh"}, {["dim_in"] = {global_conf.hidden_size, global_conf.hidden_size}, ["dim_out"] = {global_conf.hidden_size}, ["break_id"] = global_conf.vocab:get_sen_entry().id, ["independent"] = global_conf.independent, ["clip"] = 10}}
- local recurrentLconfig = {{["bp"] = "bp_h", ["ltp_hh"] = "ltp_hh"}, {["dim_in"] = {global_conf.hidden_size, global_conf.hidden_size}, ["dim_out"] = {global_conf.hidden_size}, ["clip"] = 10}}
+ local recurrentLconfig = {{["bp"] = "bp_h", ["ltp_hh"] = "ltp_hh"}, {["dim_in"] = {global_conf.hidden_size, global_conf.hidden_size}, ["dim_out"] = {global_conf.hidden_size}, ["clip"] = 10, ["direct_update"] = du}}
local layers = {
["nerv.AffineRecurrentLayer"] = {
@@ -85,7 +87,7 @@ function prepare_layers(global_conf, paramRepo)
},
["nerv.AffineLayer"] = {
- ["outputL"] = {{["ltp"] = "ltp_ho", ["bp"] = "bp_o"}, {["dim_in"] = {global_conf.hidden_size}, ["dim_out"] = {global_conf.vocab:size()}}},
+ ["outputL"] = {{["ltp"] = "ltp_ho", ["bp"] = "bp_o"}, {["dim_in"] = {global_conf.hidden_size}, ["dim_out"] = {global_conf.vocab:size()}, ["direct_update"] = du}},
},
["nerv.SoftmaxCELayerT"] = {
@@ -168,7 +170,7 @@ global_conf = {
mmat_type = nerv.MMatrixFloat,
nn_act_default = 0,
- hidden_size = 300, --set to 400 for a stable good test PPL
+ hidden_size = 400, --set to 400 for a stable good test PPL
chunk_size = 15,
batch_size = 10,
max_iter = 35,
diff --git a/nerv/layer/affine.lua b/nerv/layer/affine.lua
index 0462383..a2809bf 100644
--- a/nerv/layer/affine.lua
+++ b/nerv/layer/affine.lua
@@ -72,18 +72,25 @@ function AffineLayer:batch_resize(batch_size)
end
function AffineLayer:update(bp_err, input, output)
- if self.direct_update then
- self.ltp.correction:mul(input[1], bp_err[1], 1.0, gconf.momentum, 'T', 'N')
- -- momentum gain
- local mmt_gain = 1.0 / (1.0 - gconf.momentum);
- local n = self.gconf.batch_size * mmt_gain
- -- perform update
- self.ltp.trans:add(self.ltp.trans, self.ltp.correction, 1.0, -gconf.lrate / n)
+ if (self.direct_update == true) then
+ local gconf = self.gconf
+ if (gconf.momentum > 0) then
+ self.ltp.correction:mul(input[1], bp_err[1], 1.0, gconf.momentum, 'T', 'N')
+ -- momentum gain
+ local mmt_gain = 1.0 / (1.0 - gconf.momentum);
+ local n = self.gconf.batch_size * mmt_gain
+ -- perform update
+ self.ltp.trans:add(self.ltp.trans, self.ltp.correction, 1.0, -gconf.lrate / n)
+ self.bp.trans:add(self.bp.trans, bp_err[1]:colsum(), 1.0-gconf.lrate*gconf.wcost, -gconf.lrate / gconf.batch_size)
+ else
+ self.ltp.trans:mul(input[1], bp_err[1], -gconf.lrate / gconf.batch_size, 1.0-gconf.lrate*gconf.wcost/gconf.batch_size, 'T', 'N')
+ self.bp.trans:add(self.bp.trans, bp_err[1]:colsum(), 1.0-gconf.lrate*gconf.wcost/gconf.batch_size, -gconf.lrate / gconf.batch_size)
+ end
else
self.ltp_grad:mul(input[1], bp_err[1], 1.0, 0.0, 'T', 'N')
self.ltp:update(self.ltp_grad)
+ self.bp:update(bp_err[1]:colsum())
end
- self.bp:update(bp_err[1]:colsum())
end
function AffineLayer:propagate(input, output)
diff --git a/nerv/layer/affine_recurrent.lua b/nerv/layer/affine_recurrent.lua
index 92d98e2..3d448d3 100644
--- a/nerv/layer/affine_recurrent.lua
+++ b/nerv/layer/affine_recurrent.lua
@@ -46,17 +46,20 @@ function Recurrent:update(bp_err, input, output)
local ltp_hh = self.ltp_hh.trans
local bp = self.bp.trans
local gconf = self.gconf
- -- momentum gain
- local mmt_gain = 1.0 / (1.0 - gconf.momentum);
- local n = input[1]:nrow() * mmt_gain
- -- update corrections (accumulated errors)
- self.ltp_hh.correction:mul(input[2], bp_err[1], 1.0, gconf.momentum, 'T', 'N')
- self.bp.correction:add(bc, bp_err[1]:colsum(), gconf.momentum, 1.0)
- -- perform update
- ltp_hh:add(ltp_hh, self.ltp_hh.correction, 1.0, -gconf.lrate / n)
- bp:add(bp, self.bp.correction, 1.0, -gconf.lrate / n)
- -- weight decay
- ltp_hh:add(ltp_hh, ltp_hh, 1.0, -gconf.lrate * gconf.wcost)
+ if (gconf.momentum > 0) then
+ -- momentum gain
+ local mmt_gain = 1.0 / (1.0 - gconf.momentum);
+ local n = input[1]:nrow() * mmt_gain
+ -- update corrections (accumulated errors)
+ self.ltp_hh.correction:mul(input[2], bp_err[1], 1.0, gconf.momentum, 'T', 'N')
+ self.bp.correction:add(self.bp.correction, bp_err[1]:colsum(), gconf.momentum, 1.0)
+ -- perform update and weight decay
+ ltp_hh:add(ltp_hh, self.ltp_hh.correction, 1.0 - gconf.lrate.gconf.wcost/n, -gconf.lrate / n)
+ bp:add(bp, self.bp.correction, 1.0 - gconf.lrate*gconf.wcost/n, -gconf.lrate / n)
+ else
+ ltp_hh:mul(input[2], bp_err[1], -gconf.lrate/gconf.batch_size, 1.0-gconf.wcost*gconf.lrate/gconf.batch_size, 'T', 'N')
+ bp:add(bp, bp_err[1]:colsum(), 1.0-gconf.lrate*gconf.wcost/gconf.batch_size, -gconf.lrate/gconf.batch_size)
+ end
else
self.ltp_hh_grad:mul(input[2], bp_err[1], 1.0, 0.0, 'T', 'N')
self.ltp_hh:update(self.ltp_hh_grad)