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author | Determinant <[email protected]> | 2015-06-22 19:01:29 +0800 |
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committer | Determinant <[email protected]> | 2015-06-22 19:01:29 +0800 |
commit | 2497fd9e7a0fae5ee4887890d7a312e0e08a93b8 (patch) | |
tree | 382f97575bd2df9ee6abb1662b11b279fc22d72b /nerv/examples/test_nn_lib.lua | |
parent | 196e9b48a3541caccdffc5743001cced70667091 (diff) |
major change: use luarocks to manage project
Diffstat (limited to 'nerv/examples/test_nn_lib.lua')
-rw-r--r-- | nerv/examples/test_nn_lib.lua | 164 |
1 files changed, 164 insertions, 0 deletions
diff --git a/nerv/examples/test_nn_lib.lua b/nerv/examples/test_nn_lib.lua new file mode 100644 index 0000000..5444810 --- /dev/null +++ b/nerv/examples/test_nn_lib.lua @@ -0,0 +1,164 @@ +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() |