diff options
| -rw-r--r-- | .gitignore | 1 | ||||
| -rw-r--r-- | nerv/Makefile | 4 | ||||
| -rw-r--r-- | nerv/layer/affine_recurrent.lua | 83 | ||||
| -rw-r--r-- | nerv/layer/init.lua | 1 |
4 files changed, 87 insertions, 2 deletions
@@ -1,4 +1,5 @@ *.o install/ +build/ *.swp *.swo diff --git a/nerv/Makefile b/nerv/Makefile index 7b75522..022e2fb 100644 --- a/nerv/Makefile +++ b/nerv/Makefile @@ -31,9 +31,9 @@ OBJS := $(CORE_OBJS) $(NERV_OBJS) $(LUAT_OBJS) LIBS := $(INST_LIBDIR)/libnerv.so $(LIB_PATH)/libnervcore.so $(LIB_PATH)/libluaT.so LUA_LIBS := matrix/init.lua io/init.lua init.lua \ layer/init.lua layer/affine.lua layer/sigmoid.lua layer/softmax_ce.lua \ - layer/window.lua layer/bias.lua layer/combiner.lua layer/mse.lua \ + layer/window.lua layer/bias.lua layer/combiner.lua layer/mse.lua layer/affine_recurrent.lua\ nn/init.lua nn/layer_repo.lua nn/param_repo.lua nn/layer_dag.lua \ - io/sgd_buffer.lua + io/sgd_buffer.lua INCLUDE := -I $(LUA_INCDIR) -DLUA_USE_APICHECK CUDA_BASE := /usr/local/cuda-6.5 diff --git a/nerv/layer/affine_recurrent.lua b/nerv/layer/affine_recurrent.lua new file mode 100644 index 0000000..5afdaa1 --- /dev/null +++ b/nerv/layer/affine_recurrent.lua @@ -0,0 +1,83 @@ +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_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_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 Recurrent:update(bp_err, input, output) + if (self.direct_update == true) then + 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) + 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.bp:update(bp_err[1]:colsum()) + end +end + +function Recurrent: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 Recurrent: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 + ]]-- + next_bp_err[2]:clip(-10, 10) +end + +function Recurrent:get_params() + return nerv.ParamRepo({self.ltp_hh, self.bp}) +end diff --git a/nerv/layer/init.lua b/nerv/layer/init.lua index 3c55a94..7172f99 100644 --- a/nerv/layer/init.lua +++ b/nerv/layer/init.lua @@ -77,3 +77,4 @@ nerv.include('bias.lua') nerv.include('window.lua') nerv.include('mse.lua') nerv.include('combiner.lua') +nerv.include('affine_recurrent.lua') |