complete auto-generate params

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