From 2497fd9e7a0fae5ee4887890d7a312e0e08a93b8 Mon Sep 17 00:00:00 2001 From: Determinant Date: Mon, 22 Jun 2015 19:01:29 +0800 Subject: major change: use luarocks to manage project --- examples/asr_trainer.lua | 106 ----------------------- examples/chunk_file_example.lua | 53 ------------ examples/cumatrix_example.lua | 31 ------- examples/cumatrix_from_mmatrix.lua | 32 ------- examples/mmatrix_example.lua | 20 ----- examples/oop_example.c | 101 ---------------------- examples/oop_example.lua | 16 ---- examples/swb_baseline.lua | 166 ------------------------------------- examples/test_dnn_layers.lua | 78 ----------------- examples/test_nn_lib.lua | 164 ------------------------------------ 10 files changed, 767 deletions(-) delete mode 100644 examples/asr_trainer.lua delete mode 100644 examples/chunk_file_example.lua delete mode 100644 examples/cumatrix_example.lua delete mode 100644 examples/cumatrix_from_mmatrix.lua delete mode 100644 examples/mmatrix_example.lua delete mode 100644 examples/oop_example.c delete mode 100644 examples/oop_example.lua delete mode 100644 examples/swb_baseline.lua delete mode 100644 examples/test_dnn_layers.lua delete mode 100644 examples/test_nn_lib.lua (limited to 'examples') diff --git a/examples/asr_trainer.lua b/examples/asr_trainer.lua deleted file mode 100644 index a5727be..0000000 --- a/examples/asr_trainer.lua +++ /dev/null @@ -1,106 +0,0 @@ -function build_trainer(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_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(sublayer_repo) - nerv.CuMatrix.print_profile() - nerv.CuMatrix.clear_profile() - gconf.cnt = 0 - -- break - end - local input = {} --- if gconf.cnt == 100 then break end - 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_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(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(sublayer_repo)) - network:get_params():export(fname, nil) - end - return get_accuracy(sublayer_repo) - 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 = 5 -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("[NN] begin iteration %d with lrate = %.6f", i, gconf.lrate) - local accu_tr = trainer(nil, gconf.tr_scp, true) - nerv.info("[TR] training set %d: %.3f", i, accu_tr) - local accu_new = trainer( - string.format("%s_%s_iter_%d_lr%f_tr%.3f", - string.gsub( - (string.gsub(pf0[1], "(.*/)(.*)", "%2")), - "(.*)%..*", "%1"), - os.date("%Y%m%d%H%M%S"), - i, gconf.lrate, - accu_tr), - 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 --- nerv.Matrix.print_profile() -end diff --git a/examples/chunk_file_example.lua b/examples/chunk_file_example.lua deleted file mode 100644 index 5961c98..0000000 --- a/examples/chunk_file_example.lua +++ /dev/null @@ -1,53 +0,0 @@ --- To define a readable and writable chunk, one must define a class with the --- following methods: __init(id, global_conf), read(handle), write(handle), --- get_info(), set_info(info) and an id attribute. This file demonstrates a --- basic chunk implementation which manages the I/O of a matrix - -local MatrixChunk = nerv.class("nerv.MatrixChunk") - -function MatrixChunk:__init(id, global_conf) - self.id = id - self.info = {} - self.gconf = global_conf -end - -function MatrixChunk:read(handle) - -- pass the read handle to the matrix method - self.data = nerv.MMatrixFloat.load(handle) -end - -function MatrixChunk:write(handle) - -- pass the write handle to the matrix method - self.data:save(handle) -end - -function MatrixChunk:get_info() - return self.info -end - -function MatrixChunk:set_info(info) - self.info = info -end - -function MatrixChunk.create_from_matrix(id, mat) - local ins = nerv.MatrixChunk(id) - ins.data = mat - return ins -end - -mat = nerv.MMatrixFloat(3, 4) -for i = 0, 2 do - for j = 0, 3 do - mat[i][j] = i + j - end -end - -cd = nerv.MatrixChunk.create_from_matrix("matrix1", mat) - -cf = nerv.ChunkFile("test.nerv", "w") -cf:write_chunk(cd) -cf:close() - -cf2 = nerv.ChunkFile("test.nerv", "r") -cd2 = cf2:read_chunk("matrix1") -print(cd2.data) diff --git a/examples/cumatrix_example.lua b/examples/cumatrix_example.lua deleted file mode 100644 index 544fc7f..0000000 --- a/examples/cumatrix_example.lua +++ /dev/null @@ -1,31 +0,0 @@ -m = 4 -n = 4 -fm = nerv.CuMatrixFloat(m, n) -dm = nerv.CuMatrixDouble(m, n) -for i = 0, m - 1 do - for j = 0, n - 1 do - -- local t = math.random(10) - t = i / (j + 1) - fm[i][j] = t - dm[i][j] = t - end -end -print(fm) -fs = fm:create() -fs:softmax(fm) --- print(fs) -print(dm) -ds = dm:create() -ds:softmax(dm) --- print(ds) -print(fs) -print(fs + fs) -print(ds + ds) -print(fs - fs) -print(ds - ds) - -a = fs:create() -a:mul_elem(fs, fs) -print(a) -a:log_elem(fs) -print(a) diff --git a/examples/cumatrix_from_mmatrix.lua b/examples/cumatrix_from_mmatrix.lua deleted file mode 100644 index 2309e14..0000000 --- a/examples/cumatrix_from_mmatrix.lua +++ /dev/null @@ -1,32 +0,0 @@ -m = 3 -n = 4 -fm = nerv.MMatrixFloat(m, n) -dm = nerv.MMatrixDouble(m, n) -for i = 0, m - 1 do - for j = 0, n - 1 do - -- local t = math.random(10) - t = i / (j + 1) - fm[i][j] = t - dm[i][j] = t - end -end -print(fm) -print(dm) - -fc = nerv.CuMatrixFloat(m, n) -dc = nerv.CuMatrixDouble(m, n) -fc:copy_fromh(fm) -dc:copy_fromh(dm) -print("fc and dc") -print(fc) -print(dc) -dc[1]:copy_tod(dc[0]) -print("dc[1] copied to dc[0]") -print(dc) -print("softmax of fc and dc") -sfc = fc:create() -sdc = dc:create() -sfc:softmax(fc) -print(sfc) -sdc:softmax(dc) -print(sdc) diff --git a/examples/mmatrix_example.lua b/examples/mmatrix_example.lua deleted file mode 100644 index 8ddfe84..0000000 --- a/examples/mmatrix_example.lua +++ /dev/null @@ -1,20 +0,0 @@ -m = 10 -n = 10 -fm = nerv.MMatrixFloat(m, n) -dm = nerv.MMatrixDouble(m, n) -for i = 0, m - 1 do - for j = 0, n - 1 do - -- local t = math.random(10) - t = i / (j + 1) - fm[i][j] = t - dm[i][j] = t - end -end -print("test fm:get_dataref_value:", fm:get_dataref_value()) -print("forced a garbade collect") -collectgarbage("collect") -print("test fm:get_dataref_value:", fm:get_dataref_value()) -print(fm) --- print(fm:softmax()) -print(dm) --- print(dm:softmax()) diff --git a/examples/oop_example.c b/examples/oop_example.c deleted file mode 100644 index 59dfc5a..0000000 --- a/examples/oop_example.c +++ /dev/null @@ -1,101 +0,0 @@ -#include -#include -#include -#include "../common.h" - -#define SQR(x) ((x) * (x)) - -const char *point_tname = "nerv.Point"; -const char *better_point_tname = "nerv.BetterPoint"; - -typedef struct { - double x, y; -} Point; - -static int point_norm (lua_State *L) { - Point *p = luaT_checkudata(L, 1, point_tname); - lua_pushnumber(L, sqrt(SQR(p->x) + SQR(p->y))); - return 1; -} - -static int point_set_x (lua_State *L) { - Point *p = luaT_checkudata(L, 1, point_tname); - p->x = luaL_checknumber(L, 2); - return 0; -} - -static int point_set_y (lua_State *L) { - Point *p = luaT_checkudata(L, 1, point_tname); - p->y = luaL_checknumber(L, 2); - return 0; -} - -/* generic constructor */ -void point_new_(Point *self, double x, double y) { - self->x = x; - self->y = y; -} - -int point_new(lua_State *L) { - /* `_new` function should create the object itself */ - Point *self = (Point *)malloc(sizeof(Point)); - point_new_(self, luaL_checknumber(L, 1), luaL_checknumber(L, 2)); - luaT_pushudata(L, self, point_tname); - fprintf(stderr, "[example] %s constructor is invoked\n", - point_tname); - return 1; -} - -static const luaL_Reg point_methods[] = { - {"set_x", point_set_x}, - {"set_y", point_set_y}, - {"norm", point_norm}, - {NULL, NULL} -}; - - -/* the subclass method overrides the one from baseclass */ -static int better_point_norm (lua_State *L) { - Point *p = luaT_checkudata(L, 1, point_tname); - lua_pushnumber(L, fabs(p->x) + fabs(p->y)); - return 1; -} - -int better_point_new(lua_State *L) { - /* `_new` function should create the object itself */ - Point *self = (Point *)malloc(sizeof(Point)); - point_new_(self, luaL_checknumber(L, 1), luaL_checknumber(L, 2)); - luaT_pushudata(L, self, better_point_tname); - fprintf(stderr, "[example] %s constructor is invoked\n", - better_point_tname); - return 1; -} - -static const luaL_Reg better_point_methods[] = { - {"norm", better_point_norm}, - {NULL, NULL} -}; - -void nerv_point_init(lua_State *L) { - /* create a class and let luaT know */ - luaT_newmetatable(L, point_tname, NULL, point_new, NULL, NULL); - /* register member functions */ - luaL_register(L, NULL, point_methods); - /* keep the stack balanced, see `nerv.c` */ - lua_pop(L, 1); -} - -void nerv_better_point_init(lua_State *L) { - /* create a class and let luaT know */ - luaT_newmetatable(L, better_point_tname, point_tname, - better_point_new, NULL, NULL); - /* register member functions */ - luaL_register(L, NULL, better_point_methods); - /* keep the stack balanced, see `nerv.c` */ - lua_pop(L, 1); -} - -void nerv_example_init(lua_State *L) { - nerv_point_init(L); - nerv_better_point_init(L); -} diff --git a/examples/oop_example.lua b/examples/oop_example.lua deleted file mode 100644 index b753288..0000000 --- a/examples/oop_example.lua +++ /dev/null @@ -1,16 +0,0 @@ -p = nerv.Point(0, 0) -- create a Point instance -print(p) -print(p:norm()) -- get 2-norm of the Point -p:set_x(1.0) -p:set_y(2.0) -print(p:norm()) -- get 2-norm of the Point - -bp = nerv.BetterPoint(1, 2) --- use methods from base class -bp:set_x(1.0) -bp:set_y(2.0) -print(bp) -print(bp:norm()) --get 1-norm of the Point - -print(p.__typename) -print(bp.__typename) diff --git a/examples/swb_baseline.lua b/examples/swb_baseline.lua deleted file mode 100644 index 8b7e01a..0000000 --- a/examples/swb_baseline.lua +++ /dev/null @@ -1,166 +0,0 @@ -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 diff --git a/examples/test_dnn_layers.lua b/examples/test_dnn_layers.lua deleted file mode 100644 index 64c0dec..0000000 --- a/examples/test_dnn_layers.lua +++ /dev/null @@ -1,78 +0,0 @@ -require 'layer.affine' -require 'layer.sigmoid' -require 'layer.softmax_ce' - -global_conf = {lrate = 0.8, wcost = 1e-6, - momentum = 0.9, cumat_type = nerv.CuMatrixFloat} - -pf = nerv.ChunkFile("affine.param", "r") -ltp = pf:read_chunk("a", global_conf) -bp = pf:read_chunk("b", global_conf) - --- print(bp.trans) - -af = nerv.AffineLayer("test", global_conf, {["ltp"] = ltp, - ["bp"] = bp, - dim_in = {429}, - dim_out = {2048}}) -sg = nerv.SigmoidLayer("test2", global_conf, {dim_in = {2048}, - dim_out = {2048}}) -sm = nerv.SoftmaxCELayer("test3", global_conf, {dim_in = {2048, 2048}, - dim_out = {}}) -af:init() -sg:init() -sm:init() - -df = nerv.ChunkFile("input.param", "r") - -label = nerv.CuMatrixFloat(10, 2048) -label:fill(0) -for i = 0, 9 do - label[i][i] = 1.0 -end - -input1 = {df:read_chunk("input", global_conf).trans} -output1 = {nerv.CuMatrixFloat(10, 2048)} -input2 = output1 -output2 = {nerv.CuMatrixFloat(10, 2048)} -input3 = {output2[1], label} -output3 = {} -err_input1 = {} -err_output1 = {nerv.CuMatrixFloat(10, 2048)} -err_input2 = err_output1 -err_output2 = {nerv.CuMatrixFloat(10, 2048)} -err_input3 = err_output2 -err_output3 = {input1[1]:create()} - -for i = 0, 3 do - -- propagate - af:propagate(input1, output1) - sg:propagate(input2, output2) - sm:propagate(input3, output3) - - -- back_propagate - sm:back_propagate(err_output1, err_input1, input3, output3) - sg:back_propagate(err_output2, err_input2, input2, output2) - af:back_propagate(err_output3, err_input3, input1, output1) - - -- update - sm:update(err_input1, input3, output3) - sg:update(err_input2, input2, output2) - af:update(err_input3, input1, output1) - - - print("output1") - print(output1[1]) - print("output2") - print(output2[1]) - print("err_output1") - print(err_output1[1]) - print("err_output2") - print(err_output2[1]) - nerv.printf("cross entropy: %.8f\n", sm.total_ce) - nerv.printf("frames: %.8f\n", sm.total_frames) -end -print("linear") -print(af.ltp.trans) -print("linear2") -print(af.bp.trans) 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 = { - ["[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 = {}, - 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]"] = "softmax_ce0[1]", - ["[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() -- cgit v1.2.3