big fixes about lm_sampler

2 files changed, 25 insertions, 10 deletions

diff --git a/nerv/examples/lmptb/lm_sampler.lua b/nerv/examples/lmptb/lm_sampler.lua
index c165127..c25a75c 100644
--- a/nerv/examples/lmptb/lm_sampler.lua
+++ b/nerv/examples/lmptb/lm_sampler.lua
@@ -37,14 +37,16 @@ function LMSampler:load_dagL(dagL)
         self.store[i][1].id = self.sen_end_id
         self.store[i][1].p = 0
     end
+    self.repo = {}
 end
 
 function LMSampler:sample_to_store(smout)
     for i = 1, self.batch_size do
         local ran = math.random()
-        local s = 0, id = self.vocab:size()
+        local s = 0
+        local id = self.vocab:size()
         for j = 0, self.vocab:size() - 1 do
-            s = s + smout[i][j]
+            s = s + smout[i - 1][j]
             if s >= ran then 
                 id = j + 1
                 break
@@ -56,7 +58,7 @@ function LMSampler:sample_to_store(smout)
         local tmp = {}
         tmp.w = self.vocab:get_word_id(id).str
         tmp.id = id
-        tmp.p = smout[i][id]
+        tmp.p = smout[i - 1][id - 1]
         table.insert(self.store[i], tmp)
     end
 end
@@ -66,9 +68,8 @@ function LMSampler:lm_sample_rnn_dagL(sample_num, p_conf)
     local dagL = self.dagL
     local inputs = self.dagL_inputs
     local outputs = self.dagL_outputs
-
-    local res = {}
-    while #res < sample_num do
+    
+    while #self.repo < sample_num do
         dagL:propagate(inputs, outputs)
         inputs[2]:copy_fromd(outputs[2]) --copy hidden activation
     
@@ -80,7 +81,7 @@ function LMSampler:lm_sample_rnn_dagL(sample_num, p_conf)
             inputs[1][i - 1][0] = self.store[i][#self.store[i]].id - 1
             if self.store[i][#self.store[i]].id == self.sen_end_id then --meet a sentence end
                 if #self.store[i] >= 3 then
-                    res[#res + 1] = self.store[i]
+                    self.repo[#self.repo + 1] = self.store[i]
                 end
                 inputs[2][i - 1]:fill(0)
                 self.store[i] = {}
@@ -94,5 +95,10 @@ function LMSampler:lm_sample_rnn_dagL(sample_num, p_conf)
         collectgarbage("collect")                                              
     end
 
+    local res = {}
+    for i = 1, sample_num do
+        res[i] = self.repo[#self.repo]
+        self.repo[#self.repo] = nil
+    end
     return res
 end 
diff --git a/nerv/examples/lmptb/sample_grulm_ptb_main.lua b/nerv/examples/lmptb/sample_grulm_ptb_main.lua
index b6351bd..9a13d36 100644
--- a/nerv/examples/lmptb/sample_grulm_ptb_main.lua
+++ b/nerv/examples/lmptb/sample_grulm_ptb_main.lua
@@ -178,7 +178,7 @@ function prepare_dagL(global_conf, layerRepo)
     dagL:init(global_conf.batch_size)
 
     nerv.printf("%s Initing DAGL end.\n", global_conf.sche_log_pre)
-    return tnn
+    return dagL
 end
 
 function load_net_tnn(global_conf, fn)
@@ -191,8 +191,8 @@ end
 function load_net_dagL(global_conf, fn)
     prepare_parameters(global_conf, fn)
     local layerRepo = prepare_layers(global_conf)
-    local tnn = prepare_dagL(global_conf, layerRepo)
-    return tnn
+    local dagL = prepare_dagL(global_conf, layerRepo)
+    return dagL
 end
 
 local train_fn, valid_fn, test_fn
@@ -424,6 +424,15 @@ if commands["sampling"] == 1 then
     local dagL = load_net_dagL(global_conf, global_conf.fn_to_sample) 
     local sampler = nerv.LMSampler(global_conf)
     sampler:load_dagL(dagL)
+    for k = 1, 5 do
+        local res = sampler:lm_sample_rnn_dagL(10, {})
+        for i = 1, #res do
+            for j = 1, #res[i] do
+                nerv.printf("%s ", res[i][j].w)
+            end
+            nerv.printf("\n")
+        end
+    end
     --global_conf.dropout_rate = 0
     --LMTrainer.lm_process_file_rnn(global_conf, global_conf.test_fn, tnn, false) --false update!
 end --if commands["sampling"]