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", initialized_param = {"/slfs1/users/mfy43/swb_init.nerv", "/slfs1/users/mfy43/swb_global_transf.nerv"}, debug = false} 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"] = { ce_crit = {{}, {dim_in = {3001, 1}, dim_out = {1}, 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 = { ["[1]"] = "blayer1[1]", ["blayer1[1]"] = "wlayer1[1]", ["wlayer1[1]"] = "blayer2[1]", ["blayer2[1]"] = "wlayer2[1]", ["wlayer2[1]"] = "[1]" } }}, main = {{}, { dim_in = {429, 1}, dim_out = {1}, sub_layers = sublayer_repo, connections = { ["[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]"] = "ce_crit[1]", ["[2]"] = "ce_crit[2]", ["ce_crit[1]"] = "[1]" } }} } }, param_repo, gconf) end function get_criterion_layer(sublayer_repo) return sublayer_repo:get_layer("ce_crit") end function get_network(layer_repo) return layer_repo:get_layer("main") end 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(readers) return nerv.SGDBuffer(gconf, { buffer_size = gconf.buffer_size, randomize = gconf.randomize, readers = readers }) end 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(sublayer_repo) local ce_crit = sublayer_repo:get_layer("ce_crit") nerv.info("*** training stat begin ***") 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