coding style change

3 files changed, 29 insertions, 33 deletions

diff --git a/nerv/examples/lmptb/lm_trainer.lua b/nerv/examples/lmptb/lm_trainer.lua
index 44862dc..7dd70e2 100644
--- a/nerv/examples/lmptb/lm_trainer.lua
+++ b/nerv/examples/lmptb/lm_trainer.lua
@@ -26,13 +26,15 @@ function LMTrainer.lm_process_file(global_conf, fn, tnn, do_train)
 
         local r, feeds
 
-        r, feeds = tnn:getFeedFromReader(reader)
-        if (r == false) then break end
+        r, feeds = tnn:getfeed_from_reader(reader)
+        if r == false then 
+            break 
+        end
     
         for t = 1, global_conf.chunk_size do
             tnn.err_inputs_m[t][1]:fill(1)
             for i = 1, global_conf.batch_size do
-                if (bit.band(feeds.flags_now[t][i], nerv.TNN.FC.HAS_LABEL) == 0) then
+                if bit.band(feeds.flags_now[t][i], nerv.TNN.FC.HAS_LABEL) == 0 then
                     tnn.err_inputs_m[t][1][i - 1][0] = 0
                 end
             end
@@ -50,7 +52,7 @@ function LMTrainer.lm_process_file(global_conf, fn, tnn, do_train)
 
         tnn:net_propagate()
  
-        if (do_train == true) then
+        if do_train == true then
             tnn:net_backpropagate(false) 
             tnn:net_backpropagate(true)
         end
@@ -62,12 +64,11 @@ function LMTrainer.lm_process_file(global_conf, fn, tnn, do_train)
                 end
             end            
         end
-
-        tnn:moveRightToNextMB()
+        tnn:move_right_to_nextmb()
         global_conf.timer:toc('most_out_loop_lmprocessfile')
 
         --print log
-        if (result["rnn"].cn_w > next_log_wcn) then
+        if result["rnn"].cn_w > next_log_wcn then
             next_log_wcn = next_log_wcn + global_conf.log_w_num
             printf("%s %d words processed %s.\n", global_conf.sche_log_pre, result["rnn"].cn_w, os.date()) 
             printf("\t%s log prob per sample :%f.\n", global_conf.sche_log_pre, result:logp_sample("rnn"))
diff --git a/nerv/examples/lmptb/rnn/tnn.lua b/nerv/examples/lmptb/rnn/tnn.lua
index d6bf42e..d10ab82 100644
--- a/nerv/examples/lmptb/rnn/tnn.lua
+++ b/nerv/examples/lmptb/rnn/tnn.lua
@@ -58,7 +58,7 @@ nerv.TNN.FC.HAS_INPUT = 1
 nerv.TNN.FC.HAS_LABEL = 2
 nerv.TNN.FC.SEQ_NORM = bit.bor(nerv.TNN.FC.HAS_INPUT, nerv.TNN.FC.HAS_LABEL) --This instance have both input and label
 
-function TNN.makeInitialStore(st, p, dim, batch_size, chunk_size, global_conf, st_c, p_c, t_c)
+function TNN.make_initial_store(st, p, dim, batch_size, chunk_size, global_conf, st_c, p_c, t_c)
     --Return a table of matrix storage from time (1-chunk_size)..(2*chunk_size)
     if (type(st) ~= "table") then
         nerv.error("st should be a table")
@@ -78,7 +78,7 @@ function TNN.makeInitialStore(st, p, dim, batch_size, chunk_size, global_conf, s
     end
 end
 
-function TNN:outOfFeedRange(t) --out of chunk, or no input, for the current feed
+function TNN:out_of_feedrange(t) --out of chunk, or no input, for the current feed
     if (t < 1 or t > self.chunk_size) then
         return true
     end
@@ -165,9 +165,9 @@ function TNN:init(batch_size, chunk_size)
 
         print("TNN initing storage", ref_from.layer.id, "->", ref_to.layer.id)
         ref_to.inputs_matbak_p[port_to] = self.gconf.cumat_type(batch_size, dim)
-        self.makeInitialStore(ref_from.outputs_m, port_from, dim, batch_size, chunk_size, self.gconf, ref_to.inputs_m, port_to, time)
+        self.make_initial_store(ref_from.outputs_m, port_from, dim, batch_size, chunk_size, self.gconf, ref_to.inputs_m, port_to, time)
         ref_from.err_inputs_matbak_p[port_from] =  self.gconf.cumat_type(batch_size, dim)
-        self.makeInitialStore(ref_from.err_inputs_m, port_from, dim, batch_size, chunk_size, self.gconf, ref_to.err_outputs_m, port_to, time)
+        self.make_initial_store(ref_from.err_inputs_m, port_from, dim, batch_size, chunk_size, self.gconf, ref_to.err_outputs_m, port_to, time)
         
     end
 
@@ -176,8 +176,8 @@ function TNN:init(batch_size, chunk_size)
     for i = 1, #self.dim_out do --Init storage for output ports 
         local ref = self.outputs_p[i].ref
         local p = self.outputs_p[i].port
-        self.makeInitialStore(ref.outputs_m, p, self.dim_out[i], batch_size, chunk_size, self.gconf, self.outputs_m, i, 0)
-        self.makeInitialStore(ref.err_inputs_m, p, self.dim_out[i], batch_size, chunk_size, self.gconf, self.err_inputs_m, i, 0)
+        self.make_initial_store(ref.outputs_m, p, self.dim_out[i], batch_size, chunk_size, self.gconf, self.outputs_m, i, 0)
+        self.make_initial_store(ref.err_inputs_m, p, self.dim_out[i], batch_size, chunk_size, self.gconf, self.err_inputs_m, i, 0)
     end 
 
     self.inputs_m = {}
@@ -185,8 +185,8 @@ function TNN:init(batch_size, chunk_size)
     for i = 1, #self.dim_in do --Init storage for input ports 
         local ref = self.inputs_p[i].ref
         local p = self.inputs_p[i].port
-        self.makeInitialStore(ref.inputs_m, p, self.dim_in[i], batch_size, chunk_size, self.gconf, self.inputs_m, i, 0)
-        self.makeInitialStore(ref.err_outputs_m, p, self.dim_in[i], batch_size, chunk_size, self.gconf, self.err_outputs_m, i, 0)
+        self.make_initial_store(ref.inputs_m, p, self.dim_in[i], batch_size, chunk_size, self.gconf, self.inputs_m, i, 0)
+        self.make_initial_store(ref.err_outputs_m, p, self.dim_in[i], batch_size, chunk_size, self.gconf, self.err_outputs_m, i, 0)
     end 
 
     for id, ref in pairs(self.layers) do --Calling init for child layers
@@ -285,13 +285,13 @@ end
 --reader: some reader
 --Returns: bool, whether has new feed
 --Returns: feeds, a table that will be filled with the reader's feeds
-function TNN:getFeedFromReader(reader)
+function TNN:getfeed_from_reader(reader)
     local feeds_now = self.feeds_now
     local got_new = reader:get_batch(feeds_now)
     return got_new, feeds_now
 end
 
-function TNN:moveRightToNextMB() --move output history activations of 1..chunk_size to 1-chunk_size..0 
+function TNN:move_right_to_nextmb() --move output history activations of 1..chunk_size to 1-chunk_size..0 
     for t = 1, self.chunk_size, 1 do
         for id, ref in pairs(self.layers) do
             for p = 1, #ref.dim_out do
@@ -345,7 +345,7 @@ end
 --ref: the TNN_ref of a layer
 --t: the current time to propagate
 function TNN:propagate_dfs(ref, t)
-    if (self:outOfFeedRange(t)) then
+    if (self:out_of_feedrange(t)) then
         return
     end
     if (ref.outputs_b[t][1] == true) then --already propagated, 1 is just a random port
@@ -357,7 +357,7 @@ function TNN:propagate_dfs(ref, t)
     local flag = true --whether have all inputs
     for _, conn in pairs(ref.i_conns_p) do
         local p = conn.dst.port
-        if (not (ref.inputs_b[t][p] or self:outOfFeedRange(t - conn.time))) then
+        if (not (ref.inputs_b[t][p] or self:out_of_feedrange(t - conn.time))) then
             flag = false
             break
         end
@@ -465,7 +465,7 @@ end
 --ref: the TNN_ref of a layer
 --t: the current time to propagate
 function TNN:backpropagate_dfs(ref, t, do_update)
-    if (self:outOfFeedRange(t)) then
+    if (self:out_of_feedrange(t)) then
         return
     end
     if (ref.err_outputs_b[t][1] == true) then --already back_propagated, 1 is just a random port
@@ -477,7 +477,7 @@ function TNN:backpropagate_dfs(ref, t, do_update)
     local flag = true --whether have all inputs
     for _, conn in pairs(ref.o_conns_p) do
         local p = conn.src.port
-        if (not (ref.err_inputs_b[t][p] or self:outOfFeedRange(t + conn.time))) then
+        if (not (ref.err_inputs_b[t][p] or self:out_of_feedrange(t + conn.time))) then
             flag = false
             break
         end
diff --git a/nerv/examples/lmptb/tnn_ptb_main.lua b/nerv/examples/lmptb/tnn_ptb_main.lua
index f68311c..c37b217 100644
--- a/nerv/examples/lmptb/tnn_ptb_main.lua
+++ b/nerv/examples/lmptb/tnn_ptb_main.lua
@@ -17,7 +17,7 @@ local LMTrainer = nerv.LMTrainer
 function prepare_parameters(global_conf, iter)
     printf("%s preparing parameters...\n", global_conf.sche_log_pre) 
     
-    if (iter == -1) then --first time
+    if iter == -1 then --first time
         printf("%s first time, generating parameters...\n", global_conf.sche_log_pre) 
         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)
@@ -290,9 +290,11 @@ printf("%s building vocab...\n", global_conf.sche_log_pre)
 global_conf.vocab:build_file(global_conf.vocab_fn, false)
 ppl_rec = {} 
 
-if (start_iter == -1) then 
+if start_iter == -1 then 
     prepare_parameters(global_conf, -1) --randomly generate parameters
+end
 
+if start_iter == -1 or start_iter == 0 then
     print("===INITIAL VALIDATION===") 
     local tnn, paramRepo = load_net(global_conf, 0)
     local result = LMTrainer.lm_process_file(global_conf, global_conf.valid_fn, tnn, false) --false update!
@@ -309,9 +311,6 @@ if (start_iter == -1) then
     print() 
 end
 
-if (start_iter == 0) then
-    nerv.error("start_iter should not be zero")
-end
 local final_iter
 for iter = start_iter, global_conf.max_iter, 1 do
     final_iter = iter --for final testing
@@ -335,21 +334,17 @@ for iter = start_iter, global_conf.max_iter, 1 do
     if ((ppl_last / ppl_rec[iter].valid < 1.0003 or lr_half == true) and iter > global_conf.decay_iter) then 
         global_conf.lrate = (global_conf.lrate * 0.6)
     end 
-    if (ppl_rec[iter].valid < ppl_last) then 
+    if ppl_rec[iter].valid < ppl_last then 
         printf("%s PPL improves, saving net to file %s.%d...\n", global_conf.sche_log_pre, global_conf.param_fn, iter) 
         paramRepo:export(global_conf.param_fn .. '.' .. tostring(iter), nil) 
     else 
         printf("%s PPL did not improve, rejected, copying param file of last iter...\n", global_conf.sche_log_pre) 
         os.execute('cp ' .. global_conf.param_fn..'.'..tostring(iter - 1) .. ' ' .. global_conf.param_fn..'.'..tostring(iter))
-        --if (lr_half == true) then
-        --    printf("%s LR is already halfing, end training...\n", global_conf.sche_log_pre)
-        --    break
-        --end
     end 
-    if (ppl_last / ppl_rec[iter].valid < 1.0003 or lr_half == true) then 
+    if ppl_last / ppl_rec[iter].valid < 1.0003 or lr_half == true then 
         lr_half = true
     end
-    if (ppl_rec[iter].valid < ppl_last) then
+    if ppl_rec[iter].valid < ppl_last then
         ppl_last = ppl_rec[iter].valid
     end
     printf("\n")