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Diffstat (limited to 'nerv/examples/swb_baseline.lua')
-rw-r--r-- | nerv/examples/swb_baseline.lua | 204 |
1 files changed, 0 insertions, 204 deletions
diff --git a/nerv/examples/swb_baseline.lua b/nerv/examples/swb_baseline.lua deleted file mode 100644 index ece4d44..0000000 --- a/nerv/examples/swb_baseline.lua +++ /dev/null @@ -1,204 +0,0 @@ -require 'htk_io' -gconf = {lrate = 0.8, wcost = 1e-6, momentum = 0.9, - rearrange = true, -- just to make the context order consistent with old results, deprecated - frm_ext = 5, - frm_trim = 5, -- trim the first and last 5 frames, TNet just does this, deprecated - 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"}} - -function make_layer_repo(param_repo) - local layer_repo = nerv.LayerRepo( - { - -- global transf - ["nerv.BiasLayer"] = - { - blayer1 = {dim_in = {429}, dim_out = {429}, params = {bias = "bias1"}}, - blayer2 = {dim_in = {429}, dim_out = {429}, params = {bias = "bias2"}} - }, - ["nerv.WindowLayer"] = - { - wlayer1 = {dim_in = {429}, dim_out = {429}, params = {window = "window1"}}, - wlayer2 = {dim_in = {429}, dim_out = {429}, params = {window = "window2"}} - }, - -- biased linearity - ["nerv.AffineLayer"] = - { - affine0 = {dim_in = {429}, dim_out = {2048}, - params = {ltp = "affine0_ltp", bp = "affine0_bp"}}, - affine1 = {dim_in = {2048}, dim_out = {2048}, - params = {ltp = "affine1_ltp", bp = "affine1_bp"}}, - affine2 = {dim_in = {2048}, dim_out = {2048}, - params = {ltp = "affine2_ltp", bp = "affine2_bp"}}, - affine3 = {dim_in = {2048}, dim_out = {2048}, - params = {ltp = "affine3_ltp", bp = "affine3_bp"}}, - affine4 = {dim_in = {2048}, dim_out = {2048}, - params = {ltp = "affine4_ltp", bp = "affine4_bp"}}, - affine5 = {dim_in = {2048}, dim_out = {2048}, - params = {ltp = "affine5_ltp", bp = "affine5_bp"}}, - affine6 = {dim_in = {2048}, dim_out = {2048}, - params = {ltp = "affine6_ltp", bp = "affine6_bp"}}, - affine7 = {dim_in = {2048}, dim_out = {3001}, - params = {ltp = "affine7_ltp", bp = "affine7_bp"}} - }, - ["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 + ce criterion layer for finetune output - { - ce_crit = {dim_in = {3001, 1}, dim_out = {1}, compressed = true} - }, - ["nerv.SoftmaxLayer"] = -- softmax for decode output - { - softmax = {dim_in = {3001}, dim_out = {3001}} - } - }, param_repo, gconf) - - layer_repo:add_layers( - { - ["nerv.DAGLayer"] = - { - global_transf = { - dim_in = {429}, dim_out = {429}, - sub_layers = layer_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}, dim_out = {3001}, - sub_layers = layer_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]"] = "<output>[1]" - } - } - } - }, param_repo, gconf) - - layer_repo:add_layers( - { - ["nerv.DAGLayer"] = - { - ce_output = { - dim_in = {429, 1}, dim_out = {1}, - sub_layers = layer_repo, - connections = { - ["<input>[1]"] = "main[1]", - ["main[1]"] = "ce_crit[1]", - ["<input>[2]"] = "ce_crit[2]", - ["ce_crit[1]"] = "<output>[1]" - } - }, - softmax_output = { - dim_in = {429}, dim_out = {3001}, - sub_layers = layer_repo, - connections = { - ["<input>[1]"] = "main[1]", - ["main[1]"] = "softmax[1]", - ["softmax[1]"] = "<output>[1]" - } - } - } - }, param_repo, gconf) - - return layer_repo -end - -function get_network(layer_repo) - return layer_repo:get_layer("ce_output") -end - -function get_decode_network(layer_repo) - return layer_repo:get_layer("softmax_output") -end - -function get_global_transf(layer_repo) - return layer_repo:get_layer("global_transf") -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" - } - } - }), - data = {main_scp = 429, phone_state = 1}} - } -end - -function make_buffer(readers) - return nerv.SGDBuffer(gconf, - { - buffer_size = gconf.buffer_size, - batch_size = gconf.batch_size, - chunk_size = gconf.chunk_size, - randomize = gconf.randomize, - readers = readers, - use_gpu = true - }) -end - -function get_input_order() - return {{id = "main_scp", global_transf = true}, - {id = "phone_state"}} -end - -function get_decode_input_order() - return {{id = "main_scp", global_transf = true}} -end - -function get_accuracy(layer_repo) - local ce_crit = layer_repo:get_layer("ce_crit") - return ce_crit.total_correct / ce_crit.total_frames * 100 -end - -function print_stat(layer_repo) - local ce_crit = layer_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(layer_repo)) - nerv.info("*** training stat end ***") -end |