diff options
Diffstat (limited to 'examples')
| -rw-r--r-- | examples/asr_trainer.lua | 87 | ||||
| -rw-r--r-- | examples/swb_baseline.lua | 163 |
2 files changed, 250 insertions, 0 deletions
diff --git a/examples/asr_trainer.lua b/examples/asr_trainer.lua new file mode 100644 index 0000000..b43a547 --- /dev/null +++ b/examples/asr_trainer.lua @@ -0,0 +1,87 @@ +function build_trainer(ifname) + local param_repo = make_param_repo(ifname) + 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 iterative_trainer = function (ofname, scp_file, bp) + gconf.randomize = bp + -- build buffer + local buffer = make_buffer(make_reader(scp_file, layer_repo)) + -- initialize the network + network:init(gconf.batch_size) + gconf.cnt = 0 + for data in buffer.get_data, buffer do + -- prine stat periodically + gconf.cnt = gconf.cnt + 1 + if gconf.cnt == 1000 then + print_stat(crit) + gconf.cnt = 0 + end + if gconf.cnt == 100 then break end + + input = {data.main_scp, data.phone_state} + output = {} + err_input = {} + err_output = {input[1]:create()} + network:propagate(input, output) + if bp then + network:back_propagate(err_output, err_input, input, output) + network:update(err_input, input, output) + end + -- collect garbage in-time to save GPU memory + collectgarbage("collect") + end + print_stat(crit) + if bp then + nerv.info("writing back...") + cf = nerv.ChunkFile(ofname, "w") + for i, p in ipairs(network:get_params()) do + cf:write_chunk(p) + end + cf:close() + end + return get_accuracy(crit) + end + return iterative_trainer +end + +dofile(arg[1]) +start_halving_inc = 0.5 +halving_factor = 0.6 +end_halving_inc = 0.1 +min_iter = 1 +max_iter = 20 +min_halving = 6 +gconf.batch_size = 256 +gconf.buffer_size = 81920 + +local pf0 = gconf.initialized_param +local trainer = build_trainer(pf0) +--local trainer = build_trainer("c3.nerv") +local accu_best = trainer(nil, gconf.cv_scp, false) +local do_halving = false + +nerv.info("initial cross validation: %.3f", accu_best) +for i = 1, max_iter do + nerv.info("iteration %d with lrate = %.6f", i, gconf.lrate) + local accu_tr = trainer(pf0 .. "_iter" .. i .. ".nerv", gconf.tr_scp, true) + nerv.info("[TR] training set %d: %.3f", i, accu_tr) + local accu_new = trainer(nil, gconf.cv_scp, false) + nerv.info("[CV] cross validation %d: %.3f", i, accu_new) + -- TODO: revert the weights + local accu_diff = accu_new - accu_best + if do_halving and accu_diff < end_halving_inc and i > min_iter then + break + end + if accu_diff < start_halving_inc and i >= min_halving then + do_halving = true + end + if do_halving then + gconf.lrate = gconf.lrate * halving_factor + end + if accu_new > accu_best then + accu_best = accu_new + end +end +nerv.Matrix.print_profile() diff --git a/examples/swb_baseline.lua b/examples/swb_baseline.lua new file mode 100644 index 0000000..f536777 --- /dev/null +++ b/examples/swb_baseline.lua @@ -0,0 +1,163 @@ +require 'speech.init' +gconf = {lrate = 0.8, wcost = 1e-6, momentum = 0.9, + cumat_type = nerv.CuMatrixFloat, + mmat_type = nerv.MMatrixFloat, + frm_ext = 5, + 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 = "global_transf.nerv", + initialized_param = "converted.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( + { + -- 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"] = + { + criterion = {{}, {dim_in = {3001, 1}, dim_out = {}, compressed = true}} + } + }, param_repo, gconf) +end + +function make_layer_repo(sublayer_repo, param_repo) + return 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]"] = "criterion[1]", + ["<input>[2]"] = "criterion[2]" + } + }} + } + }, param_repo, gconf) +end + +function get_criterion_layer(sublayer_repo) + return sublayer_repo:get_layer("criterion") +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") + }) +end + +function make_buffer(reader, buffer) + return nerv.SGDBuffer(gconf, + { + buffer_size = gconf.buffer_size, + randomize = gconf.randomize, + readers = { + { reader = reader, + data = {main_scp = 429, phone_state = 1}} + } + }) +end + +function get_accuracy(crit) + return crit.total_correct / crit.total_frames * 100 +end + +function print_stat(crit) + nerv.info("*** training stat begin ***") + nerv.utils.printf("cross entropy:\t%.8f\n", crit.total_ce) + nerv.utils.printf("correct:\t%d\n", crit.total_correct) + nerv.utils.printf("frames:\t%d\n", crit.total_frames) + nerv.utils.printf("err/frm:\t%.8f\n", crit.total_ce / crit.total_frames) + nerv.utils.printf("accuracy:\t%.3f%%\n", get_accuracy(crit)) + nerv.info("*** training stat end ***") +end |