diff options
Diffstat (limited to 'examples')
| -rw-r--r-- | examples/asr_trainer.lua | 42 | ||||
| -rw-r--r-- | examples/swb_baseline.lua | 87 | ||||
| -rw-r--r-- | examples/test_dnn_layers.lua | 4 | ||||
| -rw-r--r-- | examples/test_nn_lib.lua | 18 |
4 files changed, 81 insertions, 70 deletions
diff --git a/examples/asr_trainer.lua b/examples/asr_trainer.lua index 05d770f..a5727be 100644 --- a/examples/asr_trainer.lua +++ b/examples/asr_trainer.lua @@ -1,50 +1,58 @@ function build_trainer(ifname) - local param_repo = make_param_repo(ifname) + local param_repo = nerv.ParamRepo() + param_repo:import(ifname, nil, gconf) local sublayer_repo = make_sublayer_repo(param_repo) local layer_repo = make_layer_repo(sublayer_repo, param_repo) local crit = get_criterion_layer(sublayer_repo) local network = get_network(layer_repo) + local input_order = get_input_order() local iterative_trainer = function (prefix, scp_file, bp) gconf.randomize = bp -- build buffer - local buffer = make_buffer(make_reader(scp_file, layer_repo)) + local buffer = make_buffer(make_readers(scp_file, layer_repo)) -- initialize the network network:init(gconf.batch_size) gconf.cnt = 0 + err_input = {nerv.CuMatrixFloat(256, 1)} + err_input[1]:fill(1) for data in buffer.get_data, buffer do -- prine stat periodically gconf.cnt = gconf.cnt + 1 if gconf.cnt == 1000 then - print_stat(crit) + print_stat(sublayer_repo) + nerv.CuMatrix.print_profile() + nerv.CuMatrix.clear_profile() gconf.cnt = 0 + -- break end + local input = {} -- if gconf.cnt == 100 then break end - - input = {data.main_scp, data.phone_state} - output = {} - err_input = {} + for i, id in ipairs(input_order) do + if data[id] == nil then + nerv.error("input data %s not found", id) + end + table.insert(input, data[id]) + end + local output = {nerv.CuMatrixFloat(256, 1)} err_output = {input[1]:create()} network:propagate(input, output) if bp then - network:back_propagate(err_output, err_input, input, output) + network:back_propagate(err_input, err_output, input, output) network:update(err_input, input, output) end -- collect garbage in-time to save GPU memory collectgarbage("collect") end - print_stat(crit) + print_stat(sublayer_repo) nerv.CuMatrix.print_profile() + nerv.CuMatrix.clear_profile() if (not bp) and prefix ~= nil then nerv.info("writing back...") local fname = string.format("%s_cv%.3f.nerv", - prefix, get_accuracy(crit)) - cf = nerv.ChunkFile(fname, "w") - for i, p in ipairs(network:get_params()) do - cf:write_chunk(p) - end - cf:close() + prefix, get_accuracy(sublayer_repo)) + network:get_params():export(fname, nil) end - return get_accuracy(crit) + return get_accuracy(sublayer_repo) end return iterative_trainer end @@ -73,7 +81,7 @@ for i = 1, max_iter do local accu_new = trainer( string.format("%s_%s_iter_%d_lr%f_tr%.3f", string.gsub( - (string.gsub(pf0, "(.*/)(.*)", "%2")), + (string.gsub(pf0[1], "(.*/)(.*)", "%2")), "(.*)%..*", "%1"), os.date("%Y%m%d%H%M%S"), i, gconf.lrate, diff --git a/examples/swb_baseline.lua b/examples/swb_baseline.lua index 28cc6d5..8b7e01a 100644 --- a/examples/swb_baseline.lua +++ b/examples/swb_baseline.lua @@ -6,14 +6,10 @@ gconf = {lrate = 0.8, wcost = 1e-6, momentum = 0.9, tr_scp = "/slfs1/users/mfy43/swb_ivec/train_bp.scp", cv_scp = "/slfs1/users/mfy43/swb_ivec/train_cv.scp", htk_conf = "/slfs1/users/mfy43/swb_ivec/plp_0_d_a.conf", - global_transf = "/slfs1/users/mfy43/swb_global_transf.nerv", - initialized_param = "/slfs1/users/mfy43/swb_init.nerv", + initialized_param = {"/slfs1/users/mfy43/swb_init.nerv", + "/slfs1/users/mfy43/swb_global_transf.nerv"}, debug = false} -function make_param_repo(param_file) - return nerv.ParamRepo({param_file, gconf.global_transf}) -end - function make_sublayer_repo(param_repo) return nerv.LayerRepo( { @@ -60,7 +56,7 @@ function make_sublayer_repo(param_repo) }, ["nerv.SoftmaxCELayer"] = { - criterion = {{}, {dim_in = {3001, 1}, dim_out = {}, compressed = true}} + ce_crit = {{}, {dim_in = {3001, 1}, dim_out = {1}, compressed = true}} } }, param_repo, gconf) end @@ -82,7 +78,7 @@ function make_layer_repo(sublayer_repo, param_repo) } }}, main = {{}, { - dim_in = {429, 1}, dim_out = {}, + dim_in = {429, 1}, dim_out = {1}, sub_layers = sublayer_repo, connections = { ["<input>[1]"] = "affine0[1]", @@ -100,8 +96,9 @@ function make_layer_repo(sublayer_repo, param_repo) ["sigmoid5[1]"] = "affine6[1]", ["affine6[1]"] = "sigmoid6[1]", ["sigmoid6[1]"] = "affine7[1]", - ["affine7[1]"] = "criterion[1]", - ["<input>[2]"] = "criterion[2]" + ["affine7[1]"] = "ce_crit[1]", + ["<input>[2]"] = "ce_crit[2]", + ["ce_crit[1]"] = "<output>[1]" } }} } @@ -109,55 +106,61 @@ function make_layer_repo(sublayer_repo, param_repo) end function get_criterion_layer(sublayer_repo) - return sublayer_repo:get_layer("criterion") + return sublayer_repo:get_layer("ce_crit") end function get_network(layer_repo) return layer_repo:get_layer("main") end -function make_reader(scp_file, layer_repo) - return nerv.TNetReader(gconf, - { - id = "main_scp", - scp_file = scp_file, - conf_file = gconf.htk_conf, - frm_ext = gconf.frm_ext, - mlfs = { - phone_state = { - 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") - }) +function make_readers(scp_file, layer_repo) + return { + {reader = nerv.TNetReader(gconf, + { + id = "main_scp", + scp_file = scp_file, + conf_file = gconf.htk_conf, + frm_ext = gconf.frm_ext, + mlfs = { + phone_state = { + 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") + }), + data = {main_scp = 429, phone_state = 1}} + } end -function make_buffer(reader, buffer) +function make_buffer(readers) return nerv.SGDBuffer(gconf, { buffer_size = gconf.buffer_size, randomize = gconf.randomize, - readers = { - { reader = reader, - data = {main_scp = 429, phone_state = 1}} - } + readers = readers }) end -function get_accuracy(crit) - return crit.total_correct / crit.total_frames * 100 +function get_input_order() + return {"main_scp", "phone_state"} +end + +function get_accuracy(sublayer_repo) + local ce_crit = sublayer_repo:get_layer("ce_crit") + return ce_crit.total_correct / ce_crit.total_frames * 100 end -function print_stat(crit) +function print_stat(sublayer_repo) + local ce_crit = sublayer_repo:get_layer("ce_crit") nerv.info("*** training stat begin ***") - nerv.utils.printf("cross entropy:\t\t%.8f\n", crit.total_ce) - nerv.utils.printf("correct:\t\t%d\n", crit.total_correct) - nerv.utils.printf("frames:\t\t\t%d\n", crit.total_frames) - nerv.utils.printf("err/frm:\t\t%.8f\n", crit.total_ce / crit.total_frames) - nerv.utils.printf("accuracy:\t\t%.3f%%\n", get_accuracy(crit)) + nerv.printf("cross entropy:\t\t%.8f\n", ce_crit.total_ce) + nerv.printf("correct:\t\t%d\n", ce_crit.total_correct) + nerv.printf("frames:\t\t\t%d\n", ce_crit.total_frames) + nerv.printf("err/frm:\t\t%.8f\n", ce_crit.total_ce / ce_crit.total_frames) + nerv.printf("accuracy:\t\t%.3f%%\n", get_accuracy(sublayer_repo)) nerv.info("*** training stat end ***") end diff --git a/examples/test_dnn_layers.lua b/examples/test_dnn_layers.lua index bf81f7b..64c0dec 100644 --- a/examples/test_dnn_layers.lua +++ b/examples/test_dnn_layers.lua @@ -69,8 +69,8 @@ for i = 0, 3 do print(err_output1[1]) print("err_output2") print(err_output2[1]) - nerv.utils.printf("cross entropy: %.8f\n", sm.total_ce) - nerv.utils.printf("frames: %.8f\n", sm.total_frames) + nerv.printf("cross entropy: %.8f\n", sm.total_ce) + nerv.printf("frames: %.8f\n", sm.total_frames) end print("linear") print(af.ltp.trans) diff --git a/examples/test_nn_lib.lua b/examples/test_nn_lib.lua index 6fdbd67..5444810 100644 --- a/examples/test_nn_lib.lua +++ b/examples/test_nn_lib.lua @@ -144,17 +144,17 @@ for data in buffer.get_data, buffer do 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("correct: %d\n", sm.total_correct) --- nerv.utils.printf("frames: %d\n", sm.total_frames) --- nerv.utils.printf("err/frm: %.8f\n", sm.total_ce / sm.total_frames) --- nerv.utils.printf("accuracy: %.8f\n", sm.total_correct / sm.total_frames) +-- 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.utils.printf("cross entropy: %.8f\n", sm.total_ce) -nerv.utils.printf("correct: %d\n", sm.total_correct) -nerv.utils.printf("accuracy: %.3f%%\n", sm.total_correct / sm.total_frames * 100) -nerv.utils.printf("writing back...\n") +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) |