ready to test on ptb

2 files changed, 57 insertions, 20 deletions

diff --git a/nerv/examples/lmptb/m-tests/tnn_test.lua b/nerv/examples/lmptb/m-tests/tnn_test.lua
index 7b2de4a..c033696 100644
--- a/nerv/examples/lmptb/m-tests/tnn_test.lua
+++ b/nerv/examples/lmptb/m-tests/tnn_test.lua
@@ -158,7 +158,8 @@ function lm_process_file(global_conf, fn, tnn, do_train)
         tnn.err_inputs_m[t][1]:fill(1)
     end
 
-    local batch_num = 1
+    local next_log_wcn = global_conf.log_w_num
+
     while (1) do
         local r, feeds
 
@@ -187,7 +188,11 @@ function lm_process_file(global_conf, fn, tnn, do_train)
                 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]))
                 end
-            end
+            end            
+        end
+        if (result["rnn"].cn_w > next_log_wcn) then
+            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"))
         end
          
         --[[
@@ -217,27 +222,26 @@ if (set == "ptb") then
 
 data_dir = '/home/slhome/txh18/workspace/nerv/nerv/nerv/examples/lmptb/PTBdata'
 train_fn = data_dir .. '/ptb.train.txt.cntk'
-test_fn = data_dir .. '/ptb.test.txt.cntk'
 valid_fn = data_dir .. '/ptb.valid.txt.cntk'
+test_fn = data_dir .. '/ptb.test.txt.cntk'
 
 global_conf = {
     lrate = 1, wcost = 1e-6, momentum = 0,
     cumat_type = nerv.CuMatrixFloat,
-    mmat_type = nerv.CuMatrixFloat,
+    mmat_type = nerv.MMatrixFloat,
     nn_act_default = 0, 
 
-    hidden_size = 20,
-    chunk_size = 5,
-    batch_size = 3, 
-    max_iter = 18,
+    hidden_size = 200,
+    chunk_size = 15,
+    batch_size = 10, 
+    max_iter = 25,
     param_random = function() return (math.random() / 5 - 0.1) end,
-    independent = true,
 
     train_fn = train_fn,
     valid_fn = valid_fn,
     test_fn = test_fn,
     sche_log_pre = "[SCHEDULER]:",
-    log_w_num = 10, --give a message when log_w_num words have been processed
+    log_w_num = 10000, --give a message when log_w_num words have been processed
     timer = nerv.Timer()
 }
 
@@ -256,7 +260,7 @@ global_conf = {
     hidden_size = 20,
     chunk_size = 5,
     batch_size = 3, 
-    max_iter = 18,
+    max_iter = 3,
     param_random = function() return (math.random() / 5 - 0.1) end,
     independent = true,
 
@@ -264,7 +268,7 @@ global_conf = {
     valid_fn = valid_fn,
     test_fn = test_fn,
     sche_log_pre = "[SCHEDULER]:",
-    log_w_num = 10, --give a message when log_w_num words have been processed
+    log_w_num = 20, --give a message when log_w_num words have been processed
     timer = nerv.Timer()
 }
 
@@ -310,11 +314,12 @@ for iter = 1, global_conf.max_iter, 1 do
     end 
     printf("\n") 
     nerv.LMUtil.wait(2) 
-end 
-a= "    printf(\"===VALIDATION PPL record===\\n\") \
-    for i = 0, #ppl_rec do printf(\"<ITER%d LR%.5f: %.3f> \", i, lr_rec[i], ppl_rec[i]) end \
-    printf(\"\\n\") \
-    printf(\"===FINAL TEST===\\n\") \
-    global_conf.sche_log_pre = \"[SCHEDULER FINAL_TEST]:\" \
-    dagL, _ = load_net(global_conf) \
-    propagateFile(global_conf, dagL, global_conf.test_fn, {do_train = false, report_word = false})"
+end
+printf("===VALIDATION PPL record===\n") 
+for i = 0, #ppl_rec do printf("<ITER%d LR%.5f: %.3f> ", i, lr_rec[i], ppl_rec[i]) end 
+printf("\n") 
+printf("===FINAL TEST===\n") 
+global_conf.sche_log_pre = "[SCHEDULER FINAL_TEST]:" 
+tnn, paramRepo = load_net(global_conf) 
+lm_process_file(global_conf, global_conf.test_fn, tnn, false) --false update!
+
diff --git a/nerv/examples/lmptb/rnn/tnn.lua b/nerv/examples/lmptb/rnn/tnn.lua
index 019d24c..ae9ed7a 100644
--- a/nerv/examples/lmptb/rnn/tnn.lua
+++ b/nerv/examples/lmptb/rnn/tnn.lua
@@ -321,6 +321,22 @@ function TNN:net_propagate() --propagate according to feeds_now
             end
         end
     end
+
+    local flag_out = true 
+    for t = 1, self.chunk_size do --check whether every output has been computed
+        if (bit.band(feeds_now.flagsPack_now[t], nerv.TNN.FC.HAS_LABEL) > 0) then
+            for i = 1, #self.dim_out do
+                local ref = self.outputs_p[i].ref
+                if (ref.outputs_b[t][1] ~= true) then
+                    flag_out = false 
+                    break
+                end
+            end
+        end
+    end
+    if (flag_out == false) then
+        nerv.error("some thing wrong, some labeled output is not propagated")
+    end
 end
 
 --ref: the TNN_ref of a layer
@@ -421,6 +437,22 @@ function TNN:net_backpropagate(do_update) --propagate according to feeds_now
             end
         end
     end
+
+    local flag_out = true 
+    for t = 1, self.chunk_size do --check whether every output has been computed
+        if (bit.band(feeds_now.flagsPack_now[t], nerv.TNN.FC.HAS_INPUT) > 0) then
+            for i = 1, #self.dim_in do
+                local ref = self.inputs_p[i].ref
+                if (ref.err_outputs_b[t][1] ~= true) then
+                    flag_out = false 
+                    break
+                end
+            end
+        end
+    end
+    if (flag_out == false) then
+        nerv.error("some thing wrong, some input is not back_propagated")
+    end
 end
 
 --ref: the TNN_ref of a layer