diff options
Diffstat (limited to 'nerv/examples/lmptb/lmptb/layer')
| -rw-r--r-- | nerv/examples/lmptb/lmptb/layer/affine_recurrent.lua | 93 | ||||
| -rw-r--r-- | nerv/examples/lmptb/lmptb/layer/init.lua | 5 | ||||
| -rw-r--r-- | nerv/examples/lmptb/lmptb/layer/lm_affine_recurrent.lua | 25 |
3 files changed, 123 insertions, 0 deletions
diff --git a/nerv/examples/lmptb/lmptb/layer/affine_recurrent.lua b/nerv/examples/lmptb/lmptb/layer/affine_recurrent.lua new file mode 100644 index 0000000..0a762f0 --- /dev/null +++ b/nerv/examples/lmptb/lmptb/layer/affine_recurrent.lua @@ -0,0 +1,93 @@ +local Recurrent = nerv.class('nerv.AffineRecurrentLayer', 'nerv.Layer') + +--id: string +--global_conf: table +--layer_conf: table +--Get Parameters +function Recurrent:__init(id, global_conf, layer_conf) + self.id = id + self.dim_in = layer_conf.dim_in + self.dim_out = layer_conf.dim_out + self.gconf = global_conf + + self.bp = layer_conf.bp + self.ltp_ih = layer_conf.ltp_ih --from input to hidden + self.ltp_hh = layer_conf.ltp_hh --from hidden to hidden + + self:check_dim_len(2, 1) + self.direct_update = layer_conf.direct_update +end + +--Check parameter +function Recurrent:init(batch_size) + if (self.ltp_ih.trans:ncol() ~= self.bp.trans:ncol() or + self.ltp_hh.trans:ncol() ~= self.bp.trans:ncol()) then + nerv.error("mismatching dimensions of ltp and bp") + end + if (self.dim_in[1] ~= self.ltp_ih.trans:nrow() or + self.dim_in[2] ~= self.ltp_hh.trans:nrow()) then + nerv.error("mismatching dimensions of ltp and input") + end + if (self.dim_out[1] ~= self.bp.trans:ncol()) then + nerv.error("mismatching dimensions of bp and output") + end + + self.ltp_ih_grad = self.ltp_ih.trans:create() + self.ltp_hh_grad = self.ltp_hh.trans:create() + self.ltp_ih:train_init() + self.ltp_hh:train_init() + self.bp:train_init() +end + +function Recurrent:update(bp_err, input, output) + if (self.direct_update == true) then + local ltp_ih = self.ltp_ih.trans + local ltp_hh = self.ltp_hh.trans + local bp = self.bp.trans + local ltc_ih = self.ltc_ih + local ltc_hh = self.ltc_hh + local bc = self.bc + 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_ih.correction:mul(input[1], bp_err[1], 1.0, gconf.momentum, 'T', 'N') + self.ltc_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_ih:add(ltp_ih, self.ltp_ih.correction, 1.0, -gconf.lrate / n) + 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_ih:add(ltp_ih, ltp_ih, 1.0, -gconf.lrate * gconf.wcost) + ltp_hh:add(ltp_hh, ltp_hh, 1.0, -gconf.lrate * gconf.wcost) + else + self.ltp_ih_grad:mul(input[1], bp_err[1], 1.0, 0.0, 'T', 'N') + self.ltp_ih:update(self.ltp_ih_grad) + 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.bp:update(bp_err[1]:colsum()) + end +end + +function Recurrent:propagate(input, output) + output[1]:mul(input[1], self.ltp_ih.trans, 1.0, 0.0, 'N', 'N') + output[1]:mul(input[2], self.ltp_hh.trans, 1.0, 1.0, 'N', 'N') + output[1]:add_row(self.bp.trans, 1.0) +end + +function Recurrent:back_propagate(bp_err, next_bp_err, input, output) + next_bp_err[1]:mul(bp_err[1], self.ltp_ih.trans, 1.0, 0.0, 'N', 'T') + next_bp_err[2]:mul(bp_err[1], self.ltp_hh.trans, 1.0, 0.0, 'N', 'T') + for i = 0, next_bp_err[2]:nrow() - 1 do + for j = 0, next_bp_err[2]:ncol() - 1 do + if (next_bp_err[2][i][j] > 10) then next_bp_err[2][i][j] = 10 end + if (next_bp_err[2][i][j] < -10) then next_bp_err[2][i][j] = -10 end + end + end +end + +function Recurrent:get_params() + return {self.ltp_ih, self.ltp_hh, self.bp} +end diff --git a/nerv/examples/lmptb/lmptb/layer/init.lua b/nerv/examples/lmptb/lmptb/layer/init.lua new file mode 100644 index 0000000..b3b00f6 --- /dev/null +++ b/nerv/examples/lmptb/lmptb/layer/init.lua @@ -0,0 +1,5 @@ +require 'lmptb.layer.affine_recurrent' +require 'lmptb.layer.lm_affine_recurrent' + + + diff --git a/nerv/examples/lmptb/lmptb/layer/lm_affine_recurrent.lua b/nerv/examples/lmptb/lmptb/layer/lm_affine_recurrent.lua new file mode 100644 index 0000000..f1eb4a1 --- /dev/null +++ b/nerv/examples/lmptb/lmptb/layer/lm_affine_recurrent.lua @@ -0,0 +1,25 @@ +local LMRecurrent = nerv.class('nerv.LMAffineRecurrentLayer', 'nerv.AffineRecurrentLayer') --breaks at sentence end, when </s> is met, input will be set to zero + +--id: string +--global_conf: table +--layer_conf: table +--Get Parameters +function LMRecurrent:__init(id, global_conf, layer_conf) + nerv.AffineRecurrentLayer.__init(self, id, global_conf, layer_conf) + self.break_id = layer_conf.break_id --int, breaks recurrent input when the input (word) is break_id + self.independent = layer_conf.independent --bool, whether break +end + +function LMRecurrent:propagate(input, output) + output[1]:mul(input[1], self.ltp_ih.trans, 1.0, 0.0, 'N', 'N') + if (self.independent == true) then + for i = 1, input[1]:nrow() do + if (input[1][i - 1][self.break_id - 1] > 0.1) then --here is sentence break + input[2][i - 1]:fill(0) + end + end + end + output[1]:mul(input[2], self.ltp_hh.trans, 1.0, 1.0, 'N', 'N') + output[1]:add_row(self.bp.trans, 1.0) +end + |