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-rw-r--r--nerv/examples/lmptb/lstmlm_v2_ptb_main.lua470
1 files changed, 0 insertions, 470 deletions
diff --git a/nerv/examples/lmptb/lstmlm_v2_ptb_main.lua b/nerv/examples/lmptb/lstmlm_v2_ptb_main.lua
deleted file mode 100644
index a3d7584..0000000
--- a/nerv/examples/lmptb/lstmlm_v2_ptb_main.lua
+++ /dev/null
@@ -1,470 +0,0 @@
-require 'lmptb.lmvocab'
-require 'lmptb.lmfeeder'
-require 'lmptb.lmutil'
-require 'lmptb.layer.init'
---require 'tnn.init'
-require 'lmptb.lmseqreader'
-require 'lm_trainer'
-require 'lmptb.lstm_t_v2'
-
---[[global function rename]]--
---local printf = nerv.printf
-local LMTrainer = nerv.LMTrainer
---[[global function rename ends]]--
-
---global_conf: table
---first_time: bool
---Returns: a ParamRepo
-function prepare_parameters(global_conf, iter)
- nerv.printf("%s preparing parameters...\n", global_conf.sche_log_pre)
-
- global_conf.paramRepo = nerv.ParamRepo()
- local paramRepo = global_conf.paramRepo
-
- if iter == -1 then --first time
- nerv.printf("%s first time, prepare some pre-set parameters, and leaving other parameters to auto-generation...\n", global_conf.sche_log_pre)
- local f = nerv.ChunkFile(global_conf.param_fn .. '.0', 'w')
- f:close()
- --[[
- ltp_ih = nerv.LinearTransParam("ltp_ih", global_conf)
- ltp_ih.trans = global_conf.cumat_type(global_conf.vocab:size(), global_conf.hidden_size) --index 0 is for zero, others correspond to vocab index(starting from 1)
- ltp_ih.trans:generate(global_conf.param_random)
-
- ltp_hh = nerv.LinearTransParam("ltp_hh", global_conf)
- ltp_hh.trans = global_conf.cumat_type(global_conf.hidden_size, global_conf.hidden_size)
- ltp_hh.trans:generate(global_conf.param_random)
-
- --ltp_ho = nerv.LinearTransParam("ltp_ho", global_conf)
- --ltp_ho.trans = global_conf.cumat_type(global_conf.hidden_size, global_conf.vocab:size())
- --ltp_ho.trans:generate(global_conf.param_random)
-
- bp_h = nerv.BiasParam("bp_h", global_conf)
- bp_h.trans = global_conf.cumat_type(1, global_conf.hidden_size)
- bp_h.trans:generate(global_conf.param_random)
-
- --bp_o = nerv.BiasParam("bp_o", global_conf)
- --bp_o.trans = global_conf.cumat_type(1, global_conf.vocab:size())
- --bp_o.trans:generate(global_conf.param_random)
-
- local f = nerv.ChunkFile(global_conf.param_fn .. '.0', 'w')
- f:write_chunk(ltp_ih)
- f:write_chunk(ltp_hh)
- --f:write_chunk(ltp_ho)
- f:write_chunk(bp_h)
- --f:write_chunk(bp_o)
- f:close()
- ]]--
- return nil
- end
-
- nerv.printf("%s loading parameter from file %s...\n", global_conf.sche_log_pre, global_conf.param_fn .. '.' .. tostring(iter))
- paramRepo:import({global_conf.param_fn .. '.' .. tostring(iter)}, nil, global_conf)
-
- nerv.printf("%s preparing parameters end.\n", global_conf.sche_log_pre)
-
- return nil
-end
-
---global_conf: table
---Returns: nerv.LayerRepo
-function prepare_layers(global_conf)
- nerv.printf("%s preparing layers...\n", global_conf.sche_log_pre)
-
- local pr = global_conf.paramRepo
-
- local du = false
-
- --local recurrentLconfig = {{["bp"] = "bp_h", ["ltp_hh"] = "ltp_hh"}, {["dim_in"] = {global_conf.hidden_size, global_conf.hidden_size}, ["dim_out"] = {global_conf.hidden_size}, ["break_id"] = global_conf.vocab:get_sen_entry().id, ["independent"] = global_conf.independent, ["clip"] = 10}}
- --local recurrentLconfig = {{}, {["dim_in"] = {global_conf.hidden_size, global_conf.hidden_size}, ["dim_out"] = {global_conf.hidden_size}, ["clip"] = 10, ["direct_update"] = du, ["pr"] = pr}}
-
- local layers = {
- --["nerv.AffineRecurrentLayer"] = {
- -- ["recurrentL1"] = recurrentLconfig,
- --},
-
- ["nerv.LSTMLayerTv2"] = {
- ["lstmL1"] = {{}, {["dim_in"] = {global_conf.hidden_size, global_conf.hidden_size, global_conf.hidden_size}, ["dim_out"] = {global_conf.hidden_size, global_conf.hidden_size}, ["pr"] = pr}},
- },
-
- ["nerv.DropoutLayerT"] = {
- ["dropoutL1"] = {{}, {["dim_in"] = {global_conf.hidden_size}, ["dim_out"] = {global_conf.hidden_size}}},
- },
-
- ["nerv.SelectLinearLayer"] = {
- ["selectL1"] = {{}, {["dim_in"] = {1}, ["dim_out"] = {global_conf.hidden_size}, ["vocab"] = global_conf.vocab, ["pr"] = pr}},
- },
-
- --["nerv.SigmoidLayer"] = {
- -- ["sigmoidL1"] = {{}, {["dim_in"] = {global_conf.hidden_size}, ["dim_out"] = {global_conf.hidden_size}}}
- --},
-
- ["nerv.CombinerLayer"] = {
- ["combinerL1"] = {{}, {["dim_in"] = {global_conf.hidden_size}, ["dim_out"] = {global_conf.hidden_size, global_conf.hidden_size}, ["lambda"] = {1}}},
- },
-
- ["nerv.AffineLayer"] = {
- ["outputL"] = {{}, {["dim_in"] = {global_conf.hidden_size}, ["dim_out"] = {global_conf.vocab:size()}, ["direct_update"] = du, ["pr"] = pr}},
- },
-
- ["nerv.SoftmaxCELayerT"] = {
- ["softmaxL"] = {{}, {["dim_in"] = {global_conf.vocab:size(), global_conf.vocab:size()}, ["dim_out"] = {1}}},
- },
- }
-
- for l = 2, global_conf.layer_num do
- layers["nerv.DropoutLayerT"]["dropoutL" .. l] = {{}, {["dim_in"] = {global_conf.hidden_size}, ["dim_out"] = {global_conf.hidden_size}}}
- layers["nerv.LSTMLayerTv2"]["lstmL" .. l] = {{}, {["dim_in"] = {global_conf.hidden_size, global_conf.hidden_size, global_conf.hidden_size}, ["dim_out"] = {global_conf.hidden_size, global_conf.hidden_size}, ["pr"] = pr}}
- layers["nerv.CombinerLayer"]["combinerL" .. l] = {{}, {["dim_in"] = {global_conf.hidden_size}, ["dim_out"] = {global_conf.hidden_size, global_conf.hidden_size}, ["lambda"] = {1}}}
- end
- --[[ --we do not need those in the new tnn framework
- printf("%s adding %d bptt layers...\n", global_conf.sche_log_pre, global_conf.bptt)
- for i = 1, global_conf.bptt do
- layers["nerv.IndRecurrentLayer"]["recurrentL" .. (i + 1)] = recurrentLconfig
- layers["nerv.SigmoidLayer"]["sigmoidL" .. (i + 1)] = {{}, {["dim_in"] = {global_conf.hidden_size}, ["dim_out"] = {global_conf.hidden_size}}}
- layers["nerv.SelectLinearLayer"]["selectL" .. (i + 1)] = {{["ltp"] = "ltp_ih"}, {["dim_in"] = {1}, ["dim_out"] = {global_conf.hidden_size}}}
- end
- --]]
-
- local layerRepo = nerv.LayerRepo(layers, pr, global_conf)
- nerv.printf("%s preparing layers end.\n", global_conf.sche_log_pre)
- return layerRepo
-end
-
---global_conf: table
---layerRepo: nerv.LayerRepo
---Returns: a nerv.TNN
-function prepare_tnn(global_conf, layerRepo)
- nerv.printf("%s Generate and initing TNN ...\n", global_conf.sche_log_pre)
-
- --input: input_w, input_w, ... input_w_now, last_activation
- local connections_t = {
- {"<input>[1]", "selectL1[1]", 0},
-
- --{"selectL1[1]", "recurrentL1[1]", 0},
- --{"recurrentL1[1]", "sigmoidL1[1]", 0},
- --{"sigmoidL1[1]", "combinerL1[1]", 0},
- --{"combinerL1[1]", "recurrentL1[2]", 1},
-
- {"selectL1[1]", "lstmL1[1]", 0},
- {"lstmL1[2]", "lstmL1[3]", 1},
- {"lstmL1[1]", "combinerL1[1]", 0},
- {"combinerL1[1]", "lstmL1[2]", 1},
- {"combinerL1[2]", "dropoutL1[1]", 0},
-
- {"dropoutL"..global_conf.layer_num.."[1]", "outputL[1]", 0},
- {"outputL[1]", "softmaxL[1]", 0},
- {"<input>[2]", "softmaxL[2]", 0},
- {"softmaxL[1]", "<output>[1]", 0}
- }
-
- for l = 2, global_conf.layer_num do
- table.insert(connections_t, {"dropoutL"..(l-1).."[1]", "lstmL"..l.."[1]", 0})
- table.insert(connections_t, {"lstmL"..l.."[2]", "lstmL"..l.."[3]", 1})
- table.insert(connections_t, {"lstmL"..l.."[1]", "combinerL"..l.."[1]", 0})
- table.insert(connections_t, {"combinerL"..l.."[1]", "lstmL"..l.."[2]", 1})
- table.insert(connections_t, {"combinerL"..l.."[2]", "dropoutL"..l.."[1]", 0})
- end
-
- --[[
- printf("%s printing DAG connections:\n", global_conf.sche_log_pre)
- for key, value in pairs(connections_t) do
- printf("\t%s->%s\n", key, value)
- end
- ]]--
-
- local tnn = nerv.TNN("TNN", global_conf, {["dim_in"] = {1, global_conf.vocab:size()},
- ["dim_out"] = {1}, ["sub_layers"] = layerRepo,
- ["connections"] = connections_t, ["clip_t"] = global_conf.clip_t,
- })
-
- tnn:init(global_conf.batch_size, global_conf.chunk_size)
-
- nerv.printf("%s Initing TNN end.\n", global_conf.sche_log_pre)
- return tnn
-end
-
-function load_net(global_conf, next_iter)
- prepare_parameters(global_conf, next_iter)
- local layerRepo = prepare_layers(global_conf)
- local tnn = prepare_tnn(global_conf, layerRepo)
- return tnn
-end
-
-local train_fn, valid_fn, test_fn
-global_conf = {}
-local set = arg[1] --"test"
-
-if (set == "ptb") then
-
-root_dir = '/home/slhome/txh18/workspace'
-data_dir = root_dir .. '/ptb/DATA'
-train_fn = data_dir .. '/ptb.train.txt.adds'
-valid_fn = data_dir .. '/ptb.valid.txt.adds'
-test_fn = data_dir .. '/ptb.test.txt.adds'
-vocab_fn = data_dir .. '/vocab'
-
-qdata_dir = root_dir .. '/ptb/questionGen/gen'
-
-global_conf = {
- lrate = 0.15, wcost = 1e-5, momentum = 0, clip_t = 5,
- cumat_type = nerv.CuMatrixFloat,
- mmat_type = nerv.MMatrixFloat,
- nn_act_default = 0,
-
- hidden_size = 300,
- layer_num = 1,
- chunk_size = 15,
- batch_size = 20,
- max_iter = 45,
- lr_decay = 1.003,
- decay_iter = 10,
- param_random = function() return (math.random() / 5 - 0.1) end,
- dropout_str = "0.5",
-
- train_fn = train_fn,
- valid_fn = valid_fn,
- test_fn = test_fn,
- vocab_fn = vocab_fn,
- max_sen_len = 90,
- sche_log_pre = "[SCHEDULER]:",
- log_w_num = 40000, --give a message when log_w_num words have been processed
- timer = nerv.Timer(),
- work_dir_base = '/home/slhome/txh18/workspace/ptb/EXP-nerv/lstmlm_v2.0'
-}
-
-elseif (set == "msr_sc") then
-
-data_dir = '/home/slhome/txh18/workspace/sentenceCompletion/DATA_PV2'
-train_fn = data_dir .. '/normed_all.sf.len60.adds.train'
-valid_fn = data_dir .. '/normed_all.sf.len60.adds.dev'
-test_fn = data_dir .. '/answer_normed.adds'
-vocab_fn = data_dir .. '/normed_all.choose.vocab30000.addqvocab'
-
-global_conf = {
- lrate = 1, wcost = 1e-6, momentum = 0,
- cumat_type = nerv.CuMatrixFloat,
- mmat_type = nerv.MMatrixFloat,
- nn_act_default = 0,
-
- hidden_size = 300,
- layer_num = 1,
- chunk_size = 15,
- batch_size = 10,
- max_iter = 30,
- decay_iter = 10,
- lr_decay = 1.003,
- param_random = function() return (math.random() / 5 - 0.1) end,
- dropout_str = "0",
-
- train_fn = train_fn,
- valid_fn = valid_fn,
- test_fn = test_fn,
- vocab_fn = vocab_fn,
- sche_log_pre = "[SCHEDULER]:",
- log_w_num = 400000, --give a message when log_w_num words have been processed
- timer = nerv.Timer(),
- work_dir_base = '/home/slhome/txh18/workspace/sentenceCompletion/EXP-Nerv/rnnlm_test'
-}
-
-else
-
-valid_fn = '/home/slhome/txh18/workspace/nerv/nerv/nerv/examples/lmptb/m-tests/some-text'
-train_fn = '/home/slhome/txh18/workspace/nerv/nerv/nerv/examples/lmptb/m-tests/some-text'
-test_fn = '/home/slhome/txh18/workspace/nerv/nerv/nerv/examples/lmptb/m-tests/some-text'
-vocab_fn = '/home/slhome/txh18/workspace/nerv/nerv/nerv/examples/lmptb/m-tests/some-text'
-
-global_conf = {
- lrate = 0.01, wcost = 1e-5, momentum = 0,
- cumat_type = nerv.CuMatrixFloat,
- mmat_type = nerv.MMatrixFloat,
- nn_act_default = 0,
-
- hidden_size = 20,
- layer_num = 1,
- chunk_size = 2,
- batch_size = 10,
- max_iter = 3,
- param_random = function() return (math.random() / 5 - 0.1) end,
- dropout_str = "0",
-
- train_fn = train_fn,
- valid_fn = valid_fn,
- test_fn = test_fn,
- lr_decay = 1.003,
- decay_iter = 10,
- vocab_fn = vocab_fn,
- sche_log_pre = "[SCHEDULER]:",
- log_w_num = 10, --give a message when log_w_num words have been processed
- timer = nerv.Timer(),
- work_dir_base = '/home/slhome/txh18/workspace/nerv/play/testEXP/tnn_lstmlm_test'
-}
-
-end
-
-local lr_half = false --can not be local, to be set by loadstring
-local start_iter = -1
-local ppl_last = 100000
-local commands_str = "train:test"
-local commands = {}
-local test_iter = -1
-
---for testout(question)
-local q_file = "ptb.test.txt.q10rs1_Msss.adds"
-
-if arg[2] ~= nil then
- nerv.printf("%s applying arg[2](%s)...\n", global_conf.sche_log_pre, arg[2])
- loadstring(arg[2])()
- nerv.LMUtil.wait(0.5)
-else
- nerv.printf("%s no user setting, all default...\n", global_conf.sche_log_pre)
-end
-
-global_conf.work_dir = global_conf.work_dir_base .. 'h' .. global_conf.hidden_size .. 'l' .. global_conf.layer_num .. 'ch' .. global_conf.chunk_size .. 'ba' .. global_conf.batch_size .. 'slr' .. global_conf.lrate .. 'wc' .. global_conf.wcost .. 'dr' .. global_conf.dropout_str
-global_conf.train_fn_shuf = global_conf.work_dir .. '/train_fn_shuf'
-global_conf.train_fn_shuf_bak = global_conf.train_fn_shuf .. '_bak'
-global_conf.param_fn = global_conf.work_dir .. "/params"
-global_conf.dropout_list = nerv.SUtil.parse_schedule(global_conf.dropout_str)
-global_conf.log_fn = global_conf.work_dir .. '/log_lstm_tnn_' .. commands_str ..os.date("_TT%X_%m_%d",os.time())
-commands = nerv.SUtil.parse_commands_set(commands_str)
-
-nerv.printf("%s creating work_dir(%s)...\n", global_conf.sche_log_pre, global_conf.work_dir)
-nerv.LMUtil.wait(2)
-os.execute("mkdir -p "..global_conf.work_dir)
-os.execute("cp " .. global_conf.train_fn .. " " .. global_conf.train_fn_shuf)
-
---redirecting log outputs!
-nerv.SUtil.log_redirect(global_conf.log_fn)
-nerv.LMUtil.wait(2)
-
-----------------printing options---------------------------------
-nerv.printf("%s printing global_conf...\n", global_conf.sche_log_pre)
-for id, value in pairs(global_conf) do
- nerv.printf("%s:\t%s\n", id, tostring(value))
-end
-nerv.LMUtil.wait(2)
-
-nerv.printf("%s printing training scheduling options...\n", global_conf.sche_log_pre)
-nerv.printf("lr_half:\t%s\n", tostring(lr_half))
-nerv.printf("start_iter:\t%s\n", tostring(start_iter))
-nerv.printf("ppl_last:\t%s\n", tostring(ppl_last))
-nerv.printf("commds_str:\t%s\n", commands_str)
-nerv.printf("test_iter:\t%s\n", tostring(test_iter))
-nerv.printf("%s printing training scheduling end.\n", global_conf.sche_log_pre)
-nerv.LMUtil.wait(2)
-------------------printing options end------------------------------
-
-math.randomseed(1)
-
-local vocab = nerv.LMVocab()
-global_conf["vocab"] = vocab
-nerv.printf("%s building vocab...\n", global_conf.sche_log_pre)
-global_conf.vocab:build_file(global_conf.vocab_fn, false)
-ppl_rec = {}
-
-local final_iter = -1
-if commands["train"] == 1 then
- if start_iter == -1 then
- prepare_parameters(global_conf, -1) --write pre_generated params to param.0 file
- end
-
- if start_iter == -1 or start_iter == 0 then
- nerv.printf("===INITIAL VALIDATION===\n")
- local tnn = load_net(global_conf, 0)
- global_conf.paramRepo = tnn:get_params() --get auto-generted params
- global_conf.paramRepo:export(global_conf.param_fn .. '.0', nil) --some parameters are auto-generated, saved again to param.0 file
- global_conf.dropout_rate = 0
- local result = LMTrainer.lm_process_file_rnn(global_conf, global_conf.valid_fn, tnn, false) --false update!
- nerv.LMUtil.wait(1)
- ppl_rec[0] = {}
- ppl_rec[0].valid = result:ppl_all("rnn")
- ppl_last = ppl_rec[0].valid
- ppl_rec[0].train = 0
- ppl_rec[0].test = 0
- ppl_rec[0].lr = 0
-
- start_iter = 1
-
- nerv.printf("\n")
- end
-
- for iter = start_iter, global_conf.max_iter, 1 do
- final_iter = iter --for final testing
- global_conf.sche_log_pre = "[SCHEDULER ITER"..iter.." LR"..global_conf.lrate.."]:"
- tnn = load_net(global_conf, iter - 1)
- nerv.printf("===ITERATION %d LR %f===\n", iter, global_conf.lrate)
- global_conf.dropout_rate = nerv.SUtil.sche_get(global_conf.dropout_list, iter)
- result = LMTrainer.lm_process_file_rnn(global_conf, global_conf.train_fn_shuf, tnn, true) --true update!
- global_conf.dropout_rate = 0
- ppl_rec[iter] = {}
- ppl_rec[iter].train = result:ppl_all("rnn")
- --shuffling training file
- nerv.printf("%s shuffling training file\n", global_conf.sche_log_pre)
- os.execute('cp ' .. global_conf.train_fn_shuf .. ' ' .. global_conf.train_fn_shuf_bak)
- os.execute('cat ' .. global_conf.train_fn_shuf_bak .. ' | sort -R --random-source=/dev/zero > ' .. global_conf.train_fn_shuf)
- nerv.printf("===PEEK ON TEST %d===\n", iter)
- result = LMTrainer.lm_process_file_rnn(global_conf, global_conf.test_fn, tnn, false) --false update!
- ppl_rec[iter].test = result:ppl_all("rnn")
- nerv.printf("===VALIDATION %d===\n", iter)
- result = LMTrainer.lm_process_file_rnn(global_conf, global_conf.valid_fn, tnn, false) --false update!
- ppl_rec[iter].valid = result:ppl_all("rnn")
- ppl_rec[iter].lr = global_conf.lrate
- if ((ppl_last / ppl_rec[iter].valid < global_conf.lr_decay or lr_half == true) and iter > global_conf.decay_iter) then
- global_conf.lrate = (global_conf.lrate * 0.6)
- end
- if ppl_rec[iter].valid < ppl_last then
- nerv.printf("%s PPL improves, saving net to file %s.%d...\n", global_conf.sche_log_pre, global_conf.param_fn, iter)
- global_conf.paramRepo:export(global_conf.param_fn .. '.' .. tostring(iter), nil)
- else
- nerv.printf("%s PPL did not improve, rejected, copying param file of last iter...\n", global_conf.sche_log_pre)
- os.execute('cp ' .. global_conf.param_fn..'.'..tostring(iter - 1) .. ' ' .. global_conf.param_fn..'.'..tostring(iter))
- end
- if ppl_last / ppl_rec[iter].valid < global_conf.lr_decay or lr_half == true then
- lr_half = true
- end
- if ppl_rec[iter].valid < ppl_last then
- ppl_last = ppl_rec[iter].valid
- end
- nerv.printf("\n")
- nerv.LMUtil.wait(2)
- end
- nerv.info("saving final nn to param.final")
- os.execute('cp ' .. global_conf.param_fn .. '.' .. tostring(final_iter) .. ' ' .. global_conf.param_fn .. '.final')
-
- nerv.printf("===VALIDATION PPL record===\n")
- for i, _ in pairs(ppl_rec) do
- nerv.printf("<ITER%d LR%.5f train:%.3f valid:%.3f test:%.3f> \n", i, ppl_rec[i].lr, ppl_rec[i].train, ppl_rec[i].valid, ppl_rec[i].test)
- end
- nerv.printf("\n")
-end --if commands["train"]
-
-if commands["test"] == 1 then
- nerv.printf("===FINAL TEST===\n")
- global_conf.sche_log_pre = "[SCHEDULER FINAL_TEST]:"
- if final_iter ~= -1 and test_iter == -1 then
- test_iter = final_iter
- end
- if test_iter == -1 then
- test_iter = "final"
- end
- tnn = load_net(global_conf, test_iter)
- global_conf.dropout_rate = 0
- LMTrainer.lm_process_file_rnn(global_conf, global_conf.test_fn, tnn, false) --false update!
-end --if commands["test"]
-
-if commands["testout"] == 1 then
- nerv.printf("===TEST OUT===\n")
- nerv.printf("q_file:\t%s\n", q_file)
- local q_fn = qdata_dir .. q_file
- global_conf.sche_log_pre = "[SCHEDULER FINAL_TEST]:"
- if final_iter ~= -1 and test_iter == -1 then
- test_iter = final_iter
- end
- if test_iter == -1 then
- test_iter = "final"
- end
- tnn = load_net(global_conf, test_iter)
- global_conf.dropout_rate = 0
- LMTrainer.lm_process_file_rnn(global_conf, q_fn, tnn, false) --false update!
-end --if commands["testout"]
-
-