modify preprocessing example to use layers

1 files changed, 31 insertions, 7 deletions

diff --git a/examples/tnet_preprocessing_example.lua b/examples/tnet_preprocessing_example.lua
index c36463b..4f36aa8 100644
--- a/examples/tnet_preprocessing_example.lua
+++ b/examples/tnet_preprocessing_example.lua
@@ -1,5 +1,7 @@
 require 'libspeech'
 require 'layer.affine'
+require 'layer.bias'
+require 'layer.window'
 frm_ext = 5
 feat_repo = nerv.TNetFeatureRepo(
                                 "/slfs1/users/mfy43/swb_ivec/train_bp.scp",
@@ -22,7 +24,7 @@ window1 = cf:read_chunk("window1", gconf)
 bias2 = cf:read_chunk("bias2", gconf)
 window2 = cf:read_chunk("window2", gconf)
 
-cf2 = nerv.ChunkFile("input.param", "r")
+cf2 = nerv.ChunkFile("feat_256", "r")
 input = cf2:read_chunk("input", gconf)
 
 step = frm_ext * 2 + 1
@@ -32,13 +34,35 @@ expanded:expand_frm(nerv.CuMatrixFloat.new_from_host(feat_utter), frm_ext)
 rearranged = expanded:create()
 rearranged:rearrange_frm(expanded, step)
 
-rearranged:add_row(bias1.trans, 1)
-rearranged:scale_row(window1.trans)
-rearranged:add_row(bias2.trans, 1)
-rearranged:scale_row(window2.trans)
+output1 = {expanded:create()}
+output2 = {expanded:create()}
+output3 = {expanded:create()}
+output4 = {expanded:create()}
 
-for i = 0, 9 do
-    row_diff = input.trans[i] - rearranged[i + 5]
+blayer1 = nerv.BiasLayer("b1", gconf, {bias = bias1,
+                                dim_in = {429},
+                                dim_out = {429}})
+wlayer1 = nerv.WindowLayer("w1", gconf, {window = window1,
+                                dim_in = {429},
+                                dim_out = {429}})
+blayer2 = nerv.BiasLayer("b1", gconf, {bias = bias2,
+                                dim_in = {429},
+                                dim_out = {429}})
+wlayer2 = nerv.WindowLayer("w1", gconf, {window = window2,
+                                dim_in = {429},
+                                dim_out = {429}})
+blayer1:init()
+wlayer1:init()
+blayer2:init()
+wlayer2:init()
+
+blayer1:propagate({rearranged}, output1)
+wlayer1:propagate(output1, output2)
+blayer2:propagate(output2, output3)
+wlayer2:propagate(output3, output4)
+
+for i = 0, 157 - 10 do
+    row_diff = input.trans[i] - output4[1][i + 5]
     for j = 0, row_diff:ncol() - 1 do
         nerv.utils.printf("%.8f ", row_diff[j])
     end