From 4f5b45b79b8d5f6a9094888cf6b929fe86ac24a3 Mon Sep 17 00:00:00 2001 From: txh18 Date: Fri, 20 Nov 2015 19:58:14 +0800 Subject: working on automatic parameter for layers --- nerv/examples/lmptb/tnn_ptb_main.lua | 42 ++++++++++++++++++++---------------- 1 file changed, 23 insertions(+), 19 deletions(-) (limited to 'nerv/examples/lmptb/tnn_ptb_main.lua') diff --git a/nerv/examples/lmptb/tnn_ptb_main.lua b/nerv/examples/lmptb/tnn_ptb_main.lua index 059d52a..6afecbf 100644 --- a/nerv/examples/lmptb/tnn_ptb_main.lua +++ b/nerv/examples/lmptb/tnn_ptb_main.lua @@ -17,6 +17,9 @@ local LMTrainer = nerv.LMTrainer function prepare_parameters(global_conf, iter) 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 printf("%s first time, generating parameters...\n", global_conf.sche_log_pre) ltp_ih = nerv.LinearTransParam("ltp_ih", global_conf) @@ -27,43 +30,44 @@ function prepare_parameters(global_conf, iter) 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) + --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) + --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(ltp_ho) f:write_chunk(bp_h) - f:write_chunk(bp_o) + --f:write_chunk(bp_o) f:close() return nil end printf("%s loading parameter from file %s...\n", global_conf.sche_log_pre, global_conf.param_fn .. '.' .. tostring(iter)) - local paramRepo = nerv.ParamRepo() paramRepo:import({global_conf.param_fn .. '.' .. tostring(iter)}, nil, global_conf) printf("%s preparing parameters end.\n", global_conf.sche_log_pre) - return paramRepo + return nil end --global_conf: table --Returns: nerv.LayerRepo -function prepare_layers(global_conf, paramRepo) +function prepare_layers(global_conf) printf("%s preparing layers...\n", global_conf.sche_log_pre) + local paramRepo = 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}} @@ -146,10 +150,10 @@ function prepare_tnn(global_conf, layerRepo) end function load_net(global_conf, next_iter) - local paramRepo = prepare_parameters(global_conf, next_iter) - local layerRepo = prepare_layers(global_conf, paramRepo) + prepare_parameters(global_conf, next_iter) + local layerRepo = prepare_layers(global_conf) local tnn = prepare_tnn(global_conf, layerRepo) - return tnn, paramRepo + return tnn end local train_fn, valid_fn, test_fn @@ -233,7 +237,7 @@ global_conf = { hidden_size = 20, chunk_size = 2, - batch_size = 3, + batch_size = 10, max_iter = 3, param_random = function() return (math.random() / 5 - 0.1) end, @@ -297,7 +301,7 @@ end if start_iter == -1 or start_iter == 0 then print("===INITIAL VALIDATION===") - local tnn, paramRepo = load_net(global_conf, 0) + local tnn = load_net(global_conf, 0) local result = LMTrainer.lm_process_file(global_conf, global_conf.valid_fn, tnn, false) --false update! nerv.LMUtil.wait(1) ppl_rec[0] = {} @@ -316,7 +320,7 @@ local final_iter 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, paramRepo = load_net(global_conf, iter - 1) + tnn = load_net(global_conf, iter - 1) printf("===ITERATION %d LR %f===\n", iter, global_conf.lrate) result = LMTrainer.lm_process_file(global_conf, global_conf.train_fn_shuf, tnn, true) --true update! ppl_rec[iter] = {} @@ -337,7 +341,7 @@ for iter = start_iter, global_conf.max_iter, 1 do end if ppl_rec[iter].valid < ppl_last then printf("%s PPL improves, saving net to file %s.%d...\n", global_conf.sche_log_pre, global_conf.param_fn, iter) - paramRepo:export(global_conf.param_fn .. '.' .. tostring(iter), nil) + global_conf.paramRepo:export(global_conf.param_fn .. '.' .. tostring(iter), nil) else 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)) @@ -358,6 +362,6 @@ end printf("\n") printf("===FINAL TEST===\n") global_conf.sche_log_pre = "[SCHEDULER FINAL_TEST]:" -tnn, paramRepo = load_net(global_conf, final_iter) +tnn = load_net(global_conf, final_iter) LMTrainer.lm_process_file(global_conf, global_conf.test_fn, tnn, false) --false update! -- cgit v1.2.3