diff options
Diffstat (limited to 'nerv/examples/lmptb/lm_trainer.lua')
| -rw-r--r-- | nerv/examples/lmptb/lm_trainer.lua | 44 |
1 files changed, 34 insertions, 10 deletions
diff --git a/nerv/examples/lmptb/lm_trainer.lua b/nerv/examples/lmptb/lm_trainer.lua index 3b8b5c3..8a9744b 100644 --- a/nerv/examples/lmptb/lm_trainer.lua +++ b/nerv/examples/lmptb/lm_trainer.lua @@ -23,14 +23,16 @@ function LMTrainer.lm_process_file_rnn(global_conf, fn, tnn, do_train, p_conf) end local reader local r_conf = {} + if p_conf.compressed_label ~= nil then + r_conf.compressed_label = p_conf.compressed_label + end local chunk_size, batch_size - if p_conf.one_sen_report == true then --report log prob one by one sentence + if p_conf.one_sen_report == true or p_conf.word_prob_report == true then --report log prob one by one sentence if do_train == true then - nerv.warning("LMTrainer.lm_process_file_rnn: warning, one_sen_report is true while do_train is also true, strange") + nerv.error("LMTrainer.lm_process_file_rnn: warning, one_sen_report(or word_prob_report) is true while do_train is also true, strange") end - nerv.printf("lm_process_file_rnn: one_sen report mode, set batch_size to 1 and chunk_size to max_sen_len(%d)\n", - global_conf.max_sen_len) - batch_size = 1 + nerv.printf("lm_process_file_rnn: one_sen(or word_prob) report mode, set chunk_size to max_sen_len(%d)\n", global_conf.max_sen_len) + batch_size = global_conf.batch_size chunk_size = global_conf.max_sen_len r_conf["se_mode"] = true else @@ -94,12 +96,15 @@ function LMTrainer.lm_process_file_rnn(global_conf, fn, tnn, do_train, p_conf) global_conf.timer:tic('tnn_afterprocess') local sen_logp = {} + local word_prob = {} for t = 1, chunk_size, 1 do + word_prob[t] = {} tnn.outputs_m[t][1]:copy_toh(neto_bakm) for i = 1, batch_size, 1 do if (feeds.labels_s[t][i] ~= global_conf.vocab.null_token) then --result:add("rnn", feeds.labels_s[t][i], math.exp(tnn.outputs_m[t][1][i - 1][0])) result:add("rnn", feeds.labels_s[t][i], math.exp(neto_bakm[i - 1][0])) + word_prob[t][i] = math.exp(neto_bakm[i - 1][0]) if sen_logp[i] == nil then sen_logp[i] = 0 end @@ -107,9 +112,22 @@ function LMTrainer.lm_process_file_rnn(global_conf, fn, tnn, do_train, p_conf) end end end + + if p_conf.word_prob_report == true then + for i = 1, batch_size do + for t = 1, chunk_size do + if feeds.labels_s[t][i] ~= global_conf.vocab.null_token then + nerv.printf("LMTrainer.lm_process_file_rnn: word_prob_report_output, %.8f %s\n", word_prob[t][i], feeds.labels_s[t][i]) + end + end + end + end + if p_conf.one_sen_report == true then for i = 1, batch_size do - nerv.printf("LMTrainer.lm_process_file_rnn: one_sen_report_output, %f\n", sen_logp[i]) + if feeds.labels_s[1][i] ~= global_conf.vocab.null_token then + nerv.printf("LMTrainer.lm_process_file_rnn: one_sen_report_output, %f\n", sen_logp[i]) + end end end @@ -156,13 +174,16 @@ function LMTrainer.lm_process_file_birnn(global_conf, fn, tnn, do_train, p_conf) local reader local chunk_size, batch_size local r_conf = {["se_mode"] = true} + if p_conf.compressed_label ~= nil then + r_conf.compressed_label = p_conf.compressed_label + end if p_conf.one_sen_report == true then --report log prob one by one sentence if do_train == true then nerv.warning("LMTrainer.lm_process_file_birnn: warning, one_sen_report is true while do_train is also true, strange") end - nerv.printf("lm_process_file_birnn: one_sen report mode, set batch_size to 1 and chunk_size to max_sen_len(%d)\n", + nerv.printf("lm_process_file_birnn: one_sen report mode, set chunk_size to max_sen_len(%d)\n", global_conf.max_sen_len) - batch_size = 1 + batch_size = global_conf.batch_size chunk_size = global_conf.max_sen_len else batch_size = global_conf.batch_size @@ -196,7 +217,6 @@ function LMTrainer.lm_process_file_birnn(global_conf, fn, tnn, do_train, p_conf) if r == false then break end - for t = 1, chunk_size do tnn.err_inputs_m[t][1]:fill(1) for i = 1, batch_size do @@ -240,13 +260,17 @@ function LMTrainer.lm_process_file_birnn(global_conf, fn, tnn, do_train, p_conf) end if p_conf.one_sen_report == true then for i = 1, batch_size do - nerv.printf("LMTrainer.lm_process_file_birnn: one_sen_report_output, %f\n", sen_logp[i]) + if sen_logp[i] ~= nil then + nerv.printf("LMTrainer.lm_process_file_birnn: one_sen_report_output, %f\n", sen_logp[i]) + end end end --tnn:move_right_to_nextmb({0}) --do not need history for bi directional model global_conf.timer:toc('tnn_afterprocess') + --tnn:flush_all() --you need this for bilstmlm_ptb_v2, because it has connection across 2 time steps + global_conf.timer:toc('most_out_loop_lmprocessfile') --print log |