diff options
Diffstat (limited to 'nerv/examples/lmptb/lmptb/layer/affine_recurrent_plusvec.lua')
-rw-r--r-- | nerv/examples/lmptb/lmptb/layer/affine_recurrent_plusvec.lua | 74 |
1 files changed, 74 insertions, 0 deletions
diff --git a/nerv/examples/lmptb/lmptb/layer/affine_recurrent_plusvec.lua b/nerv/examples/lmptb/lmptb/layer/affine_recurrent_plusvec.lua new file mode 100644 index 0000000..5606a09 --- /dev/null +++ b/nerv/examples/lmptb/lmptb/layer/affine_recurrent_plusvec.lua @@ -0,0 +1,74 @@ +local RecurrentV = nerv.class('nerv.AffineRecurrentPlusVecLayer', 'nerv.Layer') + +--id: string +--global_conf: table +--layer_conf: table +--Get Parameters +function RecurrentV:__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 = self:find_param("bp", layer_conf, global_conf, nerv.BiasParam, {1, self.dim_out[1]}) --layer_conf.bp + self.ltp_hh = self:find_param("ltp_hh", layer_conf, global_conf, nerv.LinearTransParam, {self.dim_in[2], self.dim_out[1]}) --layer_conf.ltp_hh --from hidden to hidden + + self:check_dim_len(2, 1) + self.direct_update = layer_conf.direct_update + + self.clip = layer_conf.clip --clip error in back_propagate +end + +--Check parameter +function RecurrentV:init(batch_size) + if (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_hh.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_hh_grad = self.ltp_hh.trans:create() + self.ltp_hh:train_init() + self.bp:train_init() +end + +function RecurrentV:batch_resize(batch_size) + -- do nothing +end + +function RecurrentV:update(bp_err, input, output) + --self.ltp_hh_grad:mul(input[2], bp_err[1], 1.0, 0.0, 'T', 'N') + self.ltp_hh:update_by_err_input(bp_err[1], input[2]) + self.bp:update_by_gradient(bp_err[1]:colsum()) +end + +function RecurrentV:propagate(input, output) + output[1]:copy_fromd(input[1]) + 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 RecurrentV:back_propagate(bp_err, next_bp_err, input, output) + next_bp_err[1]:copy_fromd(bp_err[1]) + 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 + ]]-- + if (self.clip ~= nil) then + next_bp_err[2]:clip(- self.clip, self.clip) + end +end + +function RecurrentV:get_params() + return nerv.ParamRepo({self.ltp_hh, self.bp}) +end |