added word_prob feature to lm_trainer

2 files changed, 32 insertions, 4 deletions

diff --git a/nerv/examples/lmptb/lm_trainer.lua b/nerv/examples/lmptb/lm_trainer.lua
index 9dfefe5..8a9744b 100644
--- a/nerv/examples/lmptb/lm_trainer.lua
+++ b/nerv/examples/lmptb/lm_trainer.lua
@@ -27,12 +27,11 @@ function LMTrainer.lm_process_file_rnn(global_conf, fn, tnn, do_train, p_conf)
         r_conf.compressed_label = p_conf.compressed_label
     end
     local chunk_size, batch_size
-    if p_conf.one_sen_report == true then --report log prob one by one sentence
+    if p_conf.one_sen_report == true or p_conf.word_prob_report == true then --report log prob one by one sentence
         if do_train == true then
-            nerv.warning("LMTrainer.lm_process_file_rnn: warning, one_sen_report is true while do_train is also true, strange")
+            nerv.error("LMTrainer.lm_process_file_rnn: warning, one_sen_report(or word_prob_report) is true while do_train is also true, strange")
         end
-        nerv.printf("lm_process_file_rnn: one_sen report mode, set chunk_size to max_sen_len(%d)\n", 
-                global_conf.max_sen_len)
+        nerv.printf("lm_process_file_rnn: one_sen(or word_prob) report mode, set chunk_size to max_sen_len(%d)\n", global_conf.max_sen_len)
         batch_size = global_conf.batch_size
         chunk_size = global_conf.max_sen_len
         r_conf["se_mode"] = true
@@ -97,12 +96,15 @@ function LMTrainer.lm_process_file_rnn(global_conf, fn, tnn, do_train, p_conf)
 
         global_conf.timer:tic('tnn_afterprocess')
         local sen_logp = {}
+        local word_prob = {}
         for t = 1, chunk_size, 1 do
+            word_prob[t] = {}
             tnn.outputs_m[t][1]:copy_toh(neto_bakm)
             for i = 1, batch_size, 1 do
                 if (feeds.labels_s[t][i] ~= global_conf.vocab.null_token) then
                     --result:add("rnn", feeds.labels_s[t][i], math.exp(tnn.outputs_m[t][1][i - 1][0]))
                     result:add("rnn", feeds.labels_s[t][i], math.exp(neto_bakm[i - 1][0]))
+                    word_prob[t][i] = math.exp(neto_bakm[i - 1][0])
                     if sen_logp[i] == nil then
                         sen_logp[i] = 0
                     end
@@ -110,6 +112,17 @@ function LMTrainer.lm_process_file_rnn(global_conf, fn, tnn, do_train, p_conf)
                 end
             end            
         end
+
+        if p_conf.word_prob_report == true then
+            for i = 1, batch_size do
+                for t = 1, chunk_size do
+                    if feeds.labels_s[t][i] ~= global_conf.vocab.null_token then
+                        nerv.printf("LMTrainer.lm_process_file_rnn: word_prob_report_output, %.8f %s\n", word_prob[t][i], feeds.labels_s[t][i])    
+                    end
+                end
+            end
+        end
+
         if p_conf.one_sen_report == true then
             for i = 1, batch_size do
                 if feeds.labels_s[1][i] ~= global_conf.vocab.null_token then
diff --git a/nerv/examples/lmptb/rnnlm_ptb_main.lua b/nerv/examples/lmptb/rnnlm_ptb_main.lua
index e2ca860..dc011fb 100644
--- a/nerv/examples/lmptb/rnnlm_ptb_main.lua
+++ b/nerv/examples/lmptb/rnnlm_ptb_main.lua
@@ -190,6 +190,7 @@ global_conf = {
     valid_fn = valid_fn,
     test_fn = test_fn,
     vocab_fn = vocab_fn,
+    max_sen_len = 90,
     sche_log_pre = "[SCHEDULER]:",
     log_w_num = 40000, --give a message when log_w_num words have been processed
     timer = nerv.Timer(),
@@ -398,3 +399,17 @@ if commands["test"] == 1 then
     LMTrainer.lm_process_file_rnn(global_conf, global_conf.test_fn, tnn, false) --false update!
 end --if commands["test"]
 
+if commands["wordprob"] == 1 then
+    if final_iter ~= -1 and test_iter == -1 then
+        test_iter = final_iter
+    end
+    if test_iter == -1 then
+        test_iter = "final"
+    end
+ 
+    printf("===FINAL TEST===\n") 
+    global_conf.sche_log_pre = "[SCHEDULER FINAL_TEST]:" 
+    tnn = load_net(global_conf, test_iter) 
+    LMTrainer.lm_process_file_rnn(global_conf, global_conf.test_fn, tnn, false, {["word_prob_report"] = true}) --false update!
+end --if commands["test"]
+