diff options
| -rw-r--r-- | nerv/examples/lmptb/tnn_ptb_main.lua | 4 | ||||
| -rw-r--r-- | nerv/layer/affine.lua | 37 | ||||
| -rw-r--r-- | nerv/layer/affine_recurrent.lua | 4 |
3 files changed, 32 insertions, 13 deletions
diff --git a/nerv/examples/lmptb/tnn_ptb_main.lua b/nerv/examples/lmptb/tnn_ptb_main.lua index 491d4b7..f68311c 100644 --- a/nerv/examples/lmptb/tnn_ptb_main.lua +++ b/nerv/examples/lmptb/tnn_ptb_main.lua @@ -64,7 +64,7 @@ end function prepare_layers(global_conf, paramRepo) printf("%s preparing layers...\n", global_conf.sche_log_pre) - local du = true + local du = false --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, ["direct_update"] = du}} @@ -165,7 +165,7 @@ test_fn = data_dir .. '/ptb.test.txt.adds' vocab_fn = data_dir .. '/vocab' global_conf = { - lrate = 1, wcost = 1e-5, momentum = 0.9, + lrate = 1, wcost = 1e-5, momentum = 0, cumat_type = nerv.CuMatrixFloat, mmat_type = nerv.MMatrixFloat, nn_act_default = 0, diff --git a/nerv/layer/affine.lua b/nerv/layer/affine.lua index 0fcff36..c24af16 100644 --- a/nerv/layer/affine.lua +++ b/nerv/layer/affine.lua @@ -19,14 +19,33 @@ end function MatrixParam:update(gradient) local gconf = self.gconf - self.correction:add(self.correction, gradient, gconf.momentum, 1.0) - -- momentum gain - local mmt_gain = 1.0 / (1.0 - gconf.momentum); - local n = self.gconf.batch_size * mmt_gain - -- perform update - self.trans:add(self.trans, self.correction, 1.0 - gconf.lrate*gconf.wcost/gconf.batch_size, -gconf.lrate / n) + if (gconf.momentum > 0) then + self.correction:add(self.correction, gradient, gconf.momentum, 1.0) + -- momentum gain + local mmt_gain = 1.0 / (1.0 - gconf.momentum); + local n = self.gconf.batch_size * mmt_gain + -- perform update + self.trans:add(self.trans, self.correction, 1.0-gconf.lrate*gconf.wcost/gconf.batch_size, -gconf.lrate/n) + else + self.trans:add(self.trans, gradient, 1.0-gconf.lrate*gconf.wcost/gconf.batch_size, -gconf.lrate/gconf.batch_size) + end +end + +function MatrixParam:updateEI(err, input) + local gconf = self.gconf + if (gconf.momentum > 0) then + self.correction:mul(input, err, 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.trans:add(self.trans, self.correction, 1.0-gconf.lrate*gconf.wcost/gconf.batch_size, -gconf.lrate/n) + else + self.trans:mul(input, err, -gconf.lrate/gconf.batch_size, 1.0-gconf.lrate*gconf.wcost/gconf.batch_size, 'T', 'N') + end end +--[[ --these updates are the same function LinearTransParam:update(gradient) MatrixParam.update(self, gradient) -- local gconf = self.gconf @@ -36,10 +55,11 @@ end function BiasParam:update(gradient) MatrixParam.update(self, gradient) - -- local gconf = self.gconf + --local gconf = self.gconf -- weight decay -- self.trans:add(self.trans, self.trans, 1.0, -gconf.lrate * gconf.wcost / gconf.batch_size) end +]]-- function AffineLayer:__init(id, global_conf, layer_conf) self.id = id @@ -88,8 +108,7 @@ function AffineLayer:update(bp_err, input, output) 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.ltp:updateEI(bp_err[1], input[1]) self.bp:update(bp_err[1]:colsum()) end end diff --git a/nerv/layer/affine_recurrent.lua b/nerv/layer/affine_recurrent.lua index c8cd382..b465e95 100644 --- a/nerv/layer/affine_recurrent.lua +++ b/nerv/layer/affine_recurrent.lua @@ -61,8 +61,8 @@ function Recurrent:update(bp_err, input, output) 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) + --self.ltp_hh_grad:mul(input[2], bp_err[1], 1.0, 0.0, 'T', 'N') + self.ltp_hh:updateEI(bp_err[1], input[2]) self.bp:update(bp_err[1]:colsum()) end end |