add ParamRepo, LayerRepo, DAGLayer

1 files changed, 63 insertions, 0 deletions

diff --git a/examples/test_nn_lib.lua b/examples/test_nn_lib.lua
new file mode 100644
index 0000000..fd7167a
--- /dev/null
+++ b/examples/test_nn_lib.lua
@@ -0,0 +1,63 @@
+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"})
+sublayer_repo = nerv.LayerRepo(
+    {
+        ["nerv.AffineLayer"] =
+        {
+            affine1 = {{ltp = "a", bp = "b"}, {dim_in = {429}, dim_out = {2048}}}
+        },
+        ["nerv.SigmoidLayer"] =
+        {
+            sigmoid1 = {{}, {dim_in = {2048}, dim_out = {2048}}}
+        },
+        ["nerv.SoftmaxCELayer"] =
+        {
+            softmax_ce1 = {{}, {dim_in = {2048, 2048}, dim_out = {}}}
+        }
+    }, param_repo, gconf)
+
+layer_repo = nerv.LayerRepo(
+    {
+        ["nerv.DAGLayer"] =
+        {
+            main = {{}, {
+                dim_in = {429, 2048}, dim_out = {},
+                sub_layers = sublayer_repo,
+                connections = {
+                    ["<input>[1]"] = "affine1[1]",
+                    ["affine1[1]"] = "sigmoid1[1]",
+                    ["sigmoid1[1]"] = "softmax_ce1[1]",
+                    ["<input>[2]"] = "softmax_ce1[2]"
+                }
+            }}
+        }
+    }, param_repo, gconf)
+
+df = nerv.ChunkFile("input.param", "r")
+label = nerv.CuMatrixFloat(10, 2048)
+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_ce1")
+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