major change: use luarocks to manage project

1 files changed, 0 insertions, 164 deletions

diff --git a/examples/test_nn_lib.lua b/examples/test_nn_lib.lua
deleted file mode 100644
index 5444810..0000000
--- a/examples/test_nn_lib.lua
+++ /dev/null
@@ -1,164 +0,0 @@
-require 'speech.init'
-gconf = {lrate = 0.8, wcost = 1e-6, momentum = 0.9,
-        cumat_type = nerv.CuMatrixFloat,
-        mmat_type = nerv.MMatrixFloat,
-        batch_size = 256}
-
-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"},
-                        {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, 1}, dim_out = {}, compressed = true}}
-        }
-    }, param_repo, gconf)
-
-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, 1}, 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)
-
-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 = 81920,
-        randomize = true,
-        readers = {
-            { reader = tnet_reader,
-              data = {main_scp = 429, ref = 1}}
-        }
-    })
-
-sm = sublayer_repo:get_layer("softmax_ce0")
-main = layer_repo:get_layer("main")
-main:init(gconf.batch_size)
-gconf.cnt = 0
--- data = buffer:get_data()
--- input = {data.main_scp, data.ref}
--- while true do
-for data in buffer.get_data, buffer do
---    if gconf.cnt == 100 then break end
---    gconf.cnt = gconf.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.printf("cross entropy: %.8f\n", sm.total_ce)
---    nerv.printf("correct: %d\n", sm.total_correct)
---    nerv.printf("frames: %d\n", sm.total_frames)
---    nerv.printf("err/frm: %.8f\n", sm.total_ce / sm.total_frames)
---    nerv.printf("accuracy: %.8f\n", sm.total_correct / sm.total_frames)
-    collectgarbage("collect")
-end
-nerv.printf("cross entropy: %.8f\n", sm.total_ce)
-nerv.printf("correct: %d\n", sm.total_correct)
-nerv.printf("accuracy: %.3f%%\n", sm.total_correct / sm.total_frames * 100)
-nerv.printf("writing back...\n")
-cf = nerv.ChunkFile("output.nerv", "w")
-for i, p in ipairs(main:get_params()) do
-    print(p)
-    cf:write_chunk(p)
-end
-cf:close()
-nerv.Matrix.print_profile()