demo now works (without random shuffle)

1 files changed, 70 insertions, 21 deletions

diff --git a/examples/test_nn_lib.lua b/examples/test_nn_lib.lua
index ec338fe..9600917 100644
--- a/examples/test_nn_lib.lua
+++ b/examples/test_nn_lib.lua
@@ -1,14 +1,24 @@
--- require 'layer.affine'
--- require 'layer.sigmoid'
--- require 'layer.softmax_ce'
-
+require 'speech.init'
 gconf = {lrate = 0.8, wcost = 1e-6, momentum = 0.9,
-        mat_type = nerv.CuMatrixFloat,
-        batch_size = 10}
+        cumat_type = nerv.CuMatrixFloat,
+        mmat_type = nerv.MMatrixFloat,
+        batch_size = 256}
 
-param_repo = nerv.ParamRepo({"converted.nerv"})
+param_repo = nerv.ParamRepo({"converted.nerv", "global_transf.nerv"})
 sublayer_repo = nerv.LayerRepo(
     {
+        -- global transf
+        ["nerv.BiasLayer"] =
+        {
+            blayer1 = {{bias = "bias1"}, {dim_in = {429}, dim_out = {429}}},
+            blayer2 = {{bias = "bias2"}, {dim_in = {429}, dim_out = {429}}}
+        },
+        ["nerv.WindowLayer"] =
+        {
+            wlayer1 = {{window = "window1"}, {dim_in = {429}, dim_out = {429}}},
+            wlayer2 = {{window = "window2"}, {dim_in = {429}, dim_out = {429}}}
+        },
+        -- biased linearity
         ["nerv.AffineLayer"] =
         {
             affine0 = {{ltp = "affine0_ltp", bp = "affine0_bp"},
@@ -40,7 +50,7 @@ sublayer_repo = nerv.LayerRepo(
         },
         ["nerv.SoftmaxCELayer"] =
         {
-            softmax_ce0 = {{}, {dim_in = {3001, 3001}, dim_out = {}}}
+            softmax_ce0 = {{}, {dim_in = {3001, 1}, dim_out = {}, compressed = true}}
         }
     }, param_repo, gconf)
 
@@ -48,8 +58,19 @@ layer_repo = nerv.LayerRepo(
     {
         ["nerv.DAGLayer"] =
         {
+            global_transf = {{}, {
+                dim_in = {429}, dim_out = {429},
+                sub_layers = sublayer_repo,
+                connections = {
+                    ["<input>[1]"] = "blayer1[1]",
+                    ["blayer1[1]"] = "wlayer1[1]",
+                    ["wlayer1[1]"] = "blayer2[1]",
+                    ["blayer2[1]"] = "wlayer2[1]",
+                    ["wlayer2[1]"] = "<output>[1]"
+                }
+            }},
             main = {{}, {
-                dim_in = {429, 3001}, dim_out = {},
+                dim_in = {429, 1}, dim_out = {},
                 sub_layers = sublayer_repo,
                 connections = {
                     ["<input>[1]"] = "affine0[1]",
@@ -74,24 +95,52 @@ layer_repo = nerv.LayerRepo(
         }
     }, param_repo, gconf)
 
-df = nerv.ChunkFile("input.param", "r")
-label = nerv.CuMatrixFloat(10, 3001)
-label:fill(0)
-for i = 0, 9 do
-    label[i][i] = 1.0
-end
+tnet_reader = nerv.TNetReader(gconf,
+    {
+        id = "main_scp",
+        scp_file = "/slfs1/users/mfy43/swb_ivec/train_bp.scp",
+--        scp_file = "t.scp",
+        conf_file = "/slfs1/users/mfy43/swb_ivec/plp_0_d_a.conf",
+        frm_ext = 5,
+        mlfs = {
+            ref = {
+                file = "/slfs1/users/mfy43/swb_ivec/ref.mlf",
+                format = "map",
+                format_arg = "/slfs1/users/mfy43/swb_ivec/dict",
+                dir = "*/",
+                ext = "lab"
+            }
+        },
+        global_transf = layer_repo:get_layer("global_transf")
+    })
+
+buffer = nerv.SGDBuffer(gconf,
+    {
+        buffer_size = 8192,
+        readers = {
+            { reader = tnet_reader,
+              data = {main_scp = 429, ref = 1}}
+        }
+    })
 
-input = {df:read_chunk("input", gconf).trans, label}
-output = {}
-err_input = {}
-err_output = {input[1]:create()}
 sm = sublayer_repo:get_layer("softmax_ce0")
 main = layer_repo:get_layer("main")
-main:init()
-for i = 0, 3 do
+main:init(gconf.batch_size)
+cnt = 0
+for data in buffer.get_data, buffer do
+    if cnt == 1000 then break end
+    cnt = cnt + 1
+    input = {data.main_scp, data.ref}
+    output = {}
+    err_input = {}
+    err_output = {input[1]:create()}
+    
     main:propagate(input, output)
     main:back_propagate(err_output, err_input, input, output)
     main:update(err_input, input, output)
+
     nerv.utils.printf("cross entropy: %.8f\n", sm.total_ce)
     nerv.utils.printf("frames: %.8f\n", sm.total_frames)
+    nerv.utils.printf("err/frm: %.8f\n", sm.total_ce / sm.total_frames)
+    collectgarbage("collect")
 end