diff options
| -rw-r--r-- | nerv/examples/lmptb/lmptb/layer/select_linear.lua | 4 | ||||
| -rw-r--r-- | nerv/examples/lmptb/rnn/layers/gate_fff.lua | 14 | ||||
| -rw-r--r-- | nerv/examples/lmptb/tnn_ptb_main.lua | 16 | ||||
| -rw-r--r-- | nerv/layer/affine_recurrent.lua | 4 |
4 files changed, 21 insertions, 17 deletions
diff --git a/nerv/examples/lmptb/lmptb/layer/select_linear.lua b/nerv/examples/lmptb/lmptb/layer/select_linear.lua index e96296f..580b9c5 100644 --- a/nerv/examples/lmptb/lmptb/layer/select_linear.lua +++ b/nerv/examples/lmptb/lmptb/layer/select_linear.lua @@ -10,9 +10,9 @@ function SL:__init(id, global_conf, layer_conf) self.dim_out = layer_conf.dim_out self.gconf = global_conf - self.ltp = layer_conf.ltp self.vocab = layer_conf.vocab - + self.ltp = self:find_param("ltp", layer_conf, global_conf, nerv.LinearTransParam, {self.vocab:size(), self.dim_out[1]}) --layer_conf.ltp + self:check_dim_len(1, 1) end diff --git a/nerv/examples/lmptb/rnn/layers/gate_fff.lua b/nerv/examples/lmptb/rnn/layers/gate_fff.lua index 74e19ce..6a588fc 100644 --- a/nerv/examples/lmptb/rnn/layers/gate_fff.lua +++ b/nerv/examples/lmptb/rnn/layers/gate_fff.lua @@ -1,6 +1,6 @@ local GateFFFLayer = nerv.class('nerv.GateFFFLayer', 'nerv.Layer') -function AffineLayer:__init(id, global_conf, layer_conf) +function GateFFFLayer:__init(id, global_conf, layer_conf) self.id = id self.ltp = layer_conf.ltp self.bp = layer_conf.bp @@ -10,7 +10,7 @@ function AffineLayer:__init(id, global_conf, layer_conf) self:check_dim_len(1, 1) -- exactly one input and one output end -function AffineLayer:init(batch_size) +function GateFFFLayer:init(batch_size) if self.ltp.trans:ncol() ~= self.bp.trans:ncol() then nerv.error("mismatching dimensions of linear transform and bias paramter") end @@ -25,11 +25,11 @@ function AffineLayer:init(batch_size) self.bp:train_init() end -function AffineLayer:batch_resize(batch_size) +function GateFFFLayer:batch_resize(batch_size) -- do nothing end -function AffineLayer:update(bp_err, input, output) +function GateFFFLayer:update(bp_err, input, output) if self.direct_update == true then local gconf = self.gconf if gconf.momentum > 0 then @@ -51,17 +51,17 @@ function AffineLayer:update(bp_err, input, output) end end -function AffineLayer:propagate(input, output) +function GateFFFLayer:propagate(input, output) -- apply linear transform output[1]:mul(input[1], self.ltp.trans, 1.0, 0.0, 'N', 'N') -- add bias output[1]:add_row(self.bp.trans, 1.0) end -function AffineLayer:back_propagate(bp_err, next_bp_err, input, output) +function GateFFFLayer:back_propagate(bp_err, next_bp_err, input, output) next_bp_err[1]:mul(bp_err[1], self.ltp.trans, 1.0, 0.0, 'N', 'T') end -function AffineLayer:get_params() +function GateFFFLayer:get_params() return nerv.ParamRepo({self.ltp, self.bp}) end diff --git a/nerv/examples/lmptb/tnn_ptb_main.lua b/nerv/examples/lmptb/tnn_ptb_main.lua index 6afecbf..3096a3f 100644 --- a/nerv/examples/lmptb/tnn_ptb_main.lua +++ b/nerv/examples/lmptb/tnn_ptb_main.lua @@ -21,7 +21,10 @@ function prepare_parameters(global_conf, iter) local paramRepo = global_conf.paramRepo if iter == -1 then --first time - printf("%s first time, generating parameters...\n", global_conf.sche_log_pre) + printf("%s first time, prepare some pre-set parameters, and leaving other parameters to auto-generation...\n", global_conf.sche_log_pre) + local f = nerv.ChunkFile(global_conf.param_fn .. '.0', 'w') + f:close() + --[[ ltp_ih = nerv.LinearTransParam("ltp_ih", global_conf) ltp_ih.trans = global_conf.cumat_type(global_conf.vocab:size(), global_conf.hidden_size) --index 0 is for zero, others correspond to vocab index(starting from 1) ltp_ih.trans:generate(global_conf.param_random) @@ -49,7 +52,7 @@ function prepare_parameters(global_conf, iter) f:write_chunk(bp_h) --f:write_chunk(bp_o) f:close() - + ]]-- return nil end @@ -71,7 +74,7 @@ function prepare_layers(global_conf) 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}} + local recurrentLconfig = {{}, {["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"] = { @@ -79,7 +82,7 @@ function prepare_layers(global_conf) }, ["nerv.SelectLinearLayer"] = { - ["selectL1"] = {{["ltp"] = "ltp_ih"}, {["dim_in"] = {1}, ["dim_out"] = {global_conf.hidden_size}}}, + ["selectL1"] = {{}, {["dim_in"] = {1}, ["dim_out"] = {global_conf.hidden_size}, ["vocab"] = global_conf.vocab}}, }, ["nerv.SigmoidLayer"] = { @@ -91,7 +94,7 @@ function prepare_layers(global_conf) }, ["nerv.AffineLayer"] = { - ["outputL"] = {{["ltp"] = "ltp_ho", ["bp"] = "bp_o"}, {["dim_in"] = {global_conf.hidden_size}, ["dim_out"] = {global_conf.vocab:size()}, ["direct_update"] = du}}, + ["outputL"] = {{}, {["dim_in"] = {global_conf.hidden_size}, ["dim_out"] = {global_conf.vocab:size()}, ["direct_update"] = du}}, }, ["nerv.SoftmaxCELayerT"] = { @@ -296,12 +299,13 @@ global_conf.vocab:build_file(global_conf.vocab_fn, false) ppl_rec = {} if start_iter == -1 then - prepare_parameters(global_conf, -1) --randomly generate parameters + prepare_parameters(global_conf, -1) --write pre_generated params to param.0 file end if start_iter == -1 or start_iter == 0 then print("===INITIAL VALIDATION===") local tnn = load_net(global_conf, 0) + global_conf.paramRepo:export(global_conf.param_fn .. '.0', nil) --some parameters are auto-generated, saved again to param.0 file local result = LMTrainer.lm_process_file(global_conf, global_conf.valid_fn, tnn, false) --false update! nerv.LMUtil.wait(1) ppl_rec[0] = {} diff --git a/nerv/layer/affine_recurrent.lua b/nerv/layer/affine_recurrent.lua index da189e0..d537f4a 100644 --- a/nerv/layer/affine_recurrent.lua +++ b/nerv/layer/affine_recurrent.lua @@ -10,8 +10,8 @@ function Recurrent:__init(id, global_conf, layer_conf) 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.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 |