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-rw-r--r--examples/test_nn_lib.lua164
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()