...

Merge remote-tracking branch 'upstream/master'

2 files changed, 120 insertions, 20 deletions

diff --git a/examples/test_dnn_layers.lua b/examples/test_dnn_layers.lua
index 866e685..6e4d98d 100644
--- a/examples/test_dnn_layers.lua
+++ b/examples/test_dnn_layers.lua
@@ -11,10 +11,14 @@ bp = pf:read_chunk("b", global_conf)
 
 -- print(bp.trans)
 
-af = nerv.AffineLayer("test", global_conf, ltp, bp)
-sg = nerv.SigmoidLayer("test2", global_conf)
-sm = nerv.SoftmaxCELayer("test3", global_conf)
-
+af = nerv.AffineLayer("test", global_conf, {["ltp"] = ltp,
+                                            ["bp"] = bp,
+                                            dim_in = {429},
+                                            dim_out = {2048}})
+sg = nerv.SigmoidLayer("test2", global_conf, {dim_in = {2048},
+                                                dim_out = {2048}})
+sm = nerv.SoftmaxCELayer("test3", global_conf, {dim_in = {2048, 2048},
+                                                dim_out = {}})
 af:init()
 sg:init()
 sm:init()
@@ -27,18 +31,18 @@ for i = 0, 9 do
     label[i][i] = 1.0
 end
 
-input1 = {[0] = df:read_chunk("input", global_conf).trans}
-output1 = {[0] = nerv.CuMatrixFloat(10, 2048)}
+input1 = {df:read_chunk("input", global_conf).trans}
+output1 = {nerv.CuMatrixFloat(10, 2048)}
 input2 = output1
-output2 = {[0] = nerv.CuMatrixFloat(10, 2048)}
-input3 = {[0] = output2[0], [1] = label}
+output2 = {nerv.CuMatrixFloat(10, 2048)}
+input3 = {output2[1], label}
 output3 = nil
 err_input1 = nil
-err_output1 = {[0] = nerv.CuMatrixFloat(10, 2048)}
+err_output1 = {nerv.CuMatrixFloat(10, 2048)}
 err_input2 = err_output1
-err_output2 = {[0] = nerv.CuMatrixFloat(10, 2048)}
+err_output2 = {nerv.CuMatrixFloat(10, 2048)}
 err_input3 = err_output2
-err_output3 = {[0] = input1[0]:create()}
+err_output3 = {input1[1]:create()}
 
 for i = 0, 3 do
     -- propagate
@@ -46,26 +50,25 @@ for i = 0, 3 do
     sg:propagate(input2, output2)
     sm:propagate(input3, output3)
 
-
     -- back_propagate
     sm:back_propagate(err_output1, err_input1, input3, output3)
-    sm:update(err_input1, input3, output3)
-
     sg:back_propagate(err_output2, err_input2, input2, output2)
-    sg:update(err_input2, input2, output2)
-
     af:back_propagate(err_output3, err_input3, input1, output1)
+
+    -- update
+    sm:update(err_input1, input3, output3)
+    sg:update(err_input2, input2, output2)
     af:update(err_input3, input1, output1)
 
 
     print("output1")
-    print(output1[0])
+    print(output1[1])
     print("output2")
-    print(output2[0])
+    print(output2[1])
     print("err_output1")
-    print(err_output1[0])
+    print(err_output1[1])
     print("err_output2")
-    print(err_output2[0])
+    print(err_output2[1])
     nerv.utils.printf("cross entropy: %.8f\n", sm.total_ce)
     nerv.utils.printf("frames: %.8f\n", sm.total_frames)
 end
diff --git a/examples/test_nn_lib.lua b/examples/test_nn_lib.lua
new file mode 100644
index 0000000..ec338fe
--- /dev/null
+++ b/examples/test_nn_lib.lua
@@ -0,0 +1,97 @@
+-- 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({"converted.nerv"})
+sublayer_repo = nerv.LayerRepo(
+    {
+        ["nerv.AffineLayer"] =
+        {
+            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"] =
+        {
+            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_ce0 = {{}, {dim_in = {3001, 3001}, dim_out = {}}}
+        }
+    }, param_repo, gconf)
+
+layer_repo = nerv.LayerRepo(
+    {
+        ["nerv.DAGLayer"] =
+        {
+            main = {{}, {
+                dim_in = {429, 3001}, dim_out = {},
+                sub_layers = sublayer_repo,
+                connections = {
+                    ["<input>[1]"] = "affine0[1]",
+                    ["affine0[1]"] = "sigmoid0[1]",
+                    ["sigmoid0[1]"] = "affine1[1]",
+                    ["affine1[1]"] = "sigmoid1[1]",
+                    ["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, 3001)
+label:fill(0)
+for i = 0, 9 do
+    label[i][i] = 1.0
+end
+
+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: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)
+end