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Diffstat (limited to 'examples/test_nn_lib.lua')
| -rw-r--r-- | examples/test_nn_lib.lua | 164 |
1 files changed, 0 insertions, 164 deletions
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 = { - ["<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() |