still working on TNN

2 files changed, 36 insertions, 18 deletions

diff --git a/nerv/examples/lmptb/m-tests/dagl_test.lua b/nerv/examples/lmptb/m-tests/dagl_test.lua
index 5e90551..a50107d 100644
--- a/nerv/examples/lmptb/m-tests/dagl_test.lua
+++ b/nerv/examples/lmptb/m-tests/dagl_test.lua
@@ -98,14 +98,11 @@ end
 
 --global_conf: table
 --layerRepo: nerv.LayerRepo
---Returns: a nerv.TDAGLayer
+--Returns: a nerv.TNN
 function prepare_dagLayer(global_conf, layerRepo)
-    printf("%s Initing daglayer ...\n", global_conf.sche_log_pre)
+    printf("%s Initing TNN ...\n", global_conf.sche_log_pre)
 
     --input: input_w, input_w, ... input_w_now, last_activation
-    local dim_in_t = {}
-    dim_in_t[1] = 1 --input to select_linear layer
-    dim_in_t[2] = global_conf.vocab:size() --input to softmax label
     local connections_t = {
         {"<input>[1]", "selectL1[1]", 0},
         {"selectL1[1]", "recurrentL1[1]", 0},  
@@ -124,11 +121,11 @@ function prepare_dagLayer(global_conf, layerRepo)
     end
     ]]--
 
-    local dagL = nerv.TDAGLayer("dagL", global_conf, {["dim_in"] = dim_in_t, ["dim_out"] = {1}, ["sub_layers"] = layerRepo,
+    local tnn = nerv.TNN("TNN", global_conf, {["dim_in"] = {1, global_conf.vocab:size()}, ["dim_out"] = {1}, ["sub_layers"] = layerRepo,
             ["connections"] = connections_t, 
         })
-    printf("%s Initing DAGLayer end.\n", global_conf.sche_log_pre)
-    return dagL
+    printf("%s Initing TNN end.\n", global_conf.sche_log_pre)
+    return tnn
 end
 
 train_fn = '/home/slhome/txh18/workspace/nerv/nerv/nerv/examples/lmptb/m-tests/some-text'
@@ -160,7 +157,6 @@ global_conf["vocab"] = vocab
 global_conf.vocab:build_file(global_conf.train_fn, false)
 local paramRepo = prepare_parameters(global_conf, true)
 local layerRepo = prepare_layers(global_conf, paramRepo)
-local dagL = prepare_dagLayer(global_conf, layerRepo)
-dagL:init(global_conf.batch_size, global_conf.chunk_size)
-
+local tnn = prepare_dagLayer(global_conf, layerRepo)
+tnn:init(global_conf.batch_size, global_conf.chunk_size)
 
diff --git a/nerv/examples/lmptb/m-tests/lmseqreader_test.lua b/nerv/examples/lmptb/m-tests/lmseqreader_test.lua
index 504698f..cbcdcbe 100644
--- a/nerv/examples/lmptb/m-tests/lmseqreader_test.lua
+++ b/nerv/examples/lmptb/m-tests/lmseqreader_test.lua
@@ -1,4 +1,5 @@
 require 'lmptb.lmseqreader'
+require 'lmptb.lmutil'
 
 local printf = nerv.printf
 
@@ -8,15 +9,36 @@ local vocab = nerv.LMVocab()
 vocab:build_file(test_fn)
 local chunk_size = 5
 local batch_size = 3
-local reader = nerv.LMSeqReader({}, batch_size, chunk_size, vocab)
+local global_conf = {
+    lrate = 1, wcost = 1e-6, momentum = 0,
+    cumat_type = nerv.CuMatrixFloat,
+    mmat_type = nerv.CuMatrixFloat,
+
+    hidden_size = 20,
+    chunk_size = chunk_size,
+    batch_size = batch_size, 
+    max_iter = 18,
+    param_random = function() return (math.random() / 5 - 0.1) end,
+    independent = true,
+
+    train_fn = train_fn,
+    test_fn = test_fn,
+    sche_log_pre = "[SCHEDULER]:",
+    log_w_num = 10, --give a message when log_w_num words have been processed
+    timer = nerv.Timer(),
+    
+    vocab = vocab
+}
+
+local reader = nerv.LMSeqReader(global_conf, batch_size, chunk_size, vocab)
 reader:open_file(test_fn)
-local input = {}
-local label = {}
-for i = 1, batch_size, 1 do
-    input[i] = {}
-    label[i] = {}
-end
 local feeds = {}
+feeds.flags_now = {}
+feeds.inputs_m = {}
+for j = 1, chunk_size do
+    feeds.inputs_m[j] = {global_conf.cumat_type(batch_size, 1), global_conf.cumat_type(batch_size, global_conf.vocab:size())}
+    feeds.flags_now[j] = {}
+end
 while (1) do
     local r = reader:get_batch(feeds)
     if (r == false) then break end