fix a bug: input[1] should be input[2] (since Lua arrays are 1-based)

3 files changed, 54 insertions, 14 deletions

diff --git a/examples/test_nn_lib.lua b/examples/test_nn_lib.lua
index fd7167a..ec338fe 100644
--- a/examples/test_nn_lib.lua
+++ b/examples/test_nn_lib.lua
@@ -1,25 +1,46 @@
-require 'layer.affine'
-require 'layer.sigmoid'
-require 'layer.softmax_ce'
+-- require 'layer.affine'
+-- require 'layer.sigmoid'
+-- require 'layer.softmax_ce'
 
 gconf = {lrate = 0.8, wcost = 1e-6, momentum = 0.9,
         mat_type = nerv.CuMatrixFloat,
         batch_size = 10}
 
-param_repo = nerv.ParamRepo({"affine.param"})
+param_repo = nerv.ParamRepo({"converted.nerv"})
 sublayer_repo = nerv.LayerRepo(
     {
         ["nerv.AffineLayer"] =
         {
-            affine1 = {{ltp = "a", bp = "b"}, {dim_in = {429}, dim_out = {2048}}}
+            affine0 = {{ltp = "affine0_ltp", bp = "affine0_bp"},
+                        {dim_in = {429}, dim_out = {2048}}},
+            affine1 = {{ltp = "affine1_ltp", bp = "affine1_bp"},
+                        {dim_in = {2048}, dim_out = {2048}}},
+            affine2 = {{ltp = "affine2_ltp", bp = "affine2_bp"},
+                        {dim_in = {2048}, dim_out = {2048}}},
+            affine3 = {{ltp = "affine3_ltp", bp = "affine3_bp"},
+                        {dim_in = {2048}, dim_out = {2048}}},
+            affine4 = {{ltp = "affine4_ltp", bp = "affine4_bp"},
+                        {dim_in = {2048}, dim_out = {2048}}},
+            affine5 = {{ltp = "affine5_ltp", bp = "affine5_bp"},
+                        {dim_in = {2048}, dim_out = {2048}}},
+            affine6 = {{ltp = "affine6_ltp", bp = "affine6_bp"},
+                        {dim_in = {2048}, dim_out = {2048}}},
+            affine7 = {{ltp = "affine7_ltp", bp = "affine7_bp"},
+                        {dim_in = {2048}, dim_out = {3001}}}
         },
         ["nerv.SigmoidLayer"] =
         {
-            sigmoid1 = {{}, {dim_in = {2048}, dim_out = {2048}}}
+            sigmoid0 = {{}, {dim_in = {2048}, dim_out = {2048}}},
+            sigmoid1 = {{}, {dim_in = {2048}, dim_out = {2048}}},
+            sigmoid2 = {{}, {dim_in = {2048}, dim_out = {2048}}},
+            sigmoid3 = {{}, {dim_in = {2048}, dim_out = {2048}}},
+            sigmoid4 = {{}, {dim_in = {2048}, dim_out = {2048}}},
+            sigmoid5 = {{}, {dim_in = {2048}, dim_out = {2048}}},
+            sigmoid6 = {{}, {dim_in = {2048}, dim_out = {2048}}}
         },
         ["nerv.SoftmaxCELayer"] =
         {
-            softmax_ce1 = {{}, {dim_in = {2048, 2048}, dim_out = {}}}
+            softmax_ce0 = {{}, {dim_in = {3001, 3001}, dim_out = {}}}
         }
     }, param_repo, gconf)
 
@@ -28,20 +49,33 @@ layer_repo = nerv.LayerRepo(
         ["nerv.DAGLayer"] =
         {
             main = {{}, {
-                dim_in = {429, 2048}, dim_out = {},
+                dim_in = {429, 3001}, dim_out = {},
                 sub_layers = sublayer_repo,
                 connections = {
-                    ["<input>[1]"] = "affine1[1]",
+                    ["<input>[1]"] = "affine0[1]",
+                    ["affine0[1]"] = "sigmoid0[1]",
+                    ["sigmoid0[1]"] = "affine1[1]",
                     ["affine1[1]"] = "sigmoid1[1]",
-                    ["sigmoid1[1]"] = "softmax_ce1[1]",
-                    ["<input>[2]"] = "softmax_ce1[2]"
+                    ["sigmoid1[1]"] = "affine2[1]",
+                    ["affine2[1]"] = "sigmoid2[1]",
+                    ["sigmoid2[1]"] = "affine3[1]",
+                    ["affine3[1]"] = "sigmoid3[1]",
+                    ["sigmoid3[1]"] = "affine4[1]",
+                    ["affine4[1]"] = "sigmoid4[1]",
+                    ["sigmoid4[1]"] = "affine5[1]",
+                    ["affine5[1]"] = "sigmoid5[1]",
+                    ["sigmoid5[1]"] = "affine6[1]",
+                    ["affine6[1]"] = "sigmoid6[1]",
+                    ["sigmoid6[1]"] = "affine7[1]",
+                    ["affine7[1]"] = "softmax_ce0[1]",
+                    ["<input>[2]"] = "softmax_ce0[2]"
                 }
             }}
         }
     }, param_repo, gconf)
 
 df = nerv.ChunkFile("input.param", "r")
-label = nerv.CuMatrixFloat(10, 2048)
+label = nerv.CuMatrixFloat(10, 3001)
 label:fill(0)
 for i = 0, 9 do
     label[i][i] = 1.0
@@ -51,7 +85,7 @@ input = {df:read_chunk("input", gconf).trans, label}
 output = {}
 err_input = {}
 err_output = {input[1]:create()}
-sm = sublayer_repo:get_layer("softmax_ce1")
+sm = sublayer_repo:get_layer("softmax_ce0")
 main = layer_repo:get_layer("main")
 main:init()
 for i = 0, 3 do
diff --git a/layer/softmax_ce.lua b/layer/softmax_ce.lua
index 3dfebc5..09eb3a9 100644
--- a/layer/softmax_ce.lua
+++ b/layer/softmax_ce.lua
@@ -27,6 +27,8 @@ function SoftmaxCELayer:propagate(input, output)
     local ce = soutput:create()
     ce:log_elem(soutput)
     ce:mul_elem(ce, input[2])
+--     print(input[1][0])
+--     print(soutput[1][0])
      -- add total ce
     self.total_ce = self.total_ce - ce:rowsum():colsum()[0]
     self.total_frames = self.total_frames + soutput:nrow()
@@ -34,5 +36,5 @@ end
 
 function SoftmaxCELayer:back_propagate(next_bp_err, bp_err, input, output)
     -- softmax output - label
-    next_bp_err[1]:add(self.soutput, input[1], 1.0, -1.0)
+    next_bp_err[1]:add(self.soutput, input[2], 1.0, -1.0)
 end
diff --git a/nn/layer_dag.lua b/nn/layer_dag.lua
index 8ea28a0..1ab18fa 100644
--- a/nn/layer_dag.lua
+++ b/nn/layer_dag.lua
@@ -219,6 +219,10 @@ function nerv.DAGLayer:back_propagate(next_bp_err, bp_err, input, output)
     self:set_outputs(output)
     for i = #self.queue, 1, -1 do
         local ref = self.queue[i]
+        -- print(ref.layer.id)
         ref.layer:back_propagate(ref.err_outputs, ref.err_inputs, ref.inputs, ref.outputs)
+         -- if #ref.err_outputs > 0 then
+         --     print(ref.err_outputs[1])
+         -- end
     end
 end