diff options
Diffstat (limited to 'nerv/examples/lmptb/lm_trainer.lua')
| -rw-r--r-- | nerv/examples/lmptb/lm_trainer.lua | 28 |
1 files changed, 18 insertions, 10 deletions
diff --git a/nerv/examples/lmptb/lm_trainer.lua b/nerv/examples/lmptb/lm_trainer.lua index 44862dc..62d8b50 100644 --- a/nerv/examples/lmptb/lm_trainer.lua +++ b/nerv/examples/lmptb/lm_trainer.lua @@ -15,28 +15,32 @@ function LMTrainer.lm_process_file(global_conf, fn, tnn, do_train) reader:open_file(fn) local result = nerv.LMResult(global_conf, global_conf.vocab) result:init("rnn") - + global_conf.timer:flush() tnn:flush_all() --caution: will also flush the inputs from the reader! local next_log_wcn = global_conf.log_w_num + local neto_bakm = global_conf.mmat_type(global_conf.batch_size, 1) --space backup matrix for network output while (1) do global_conf.timer:tic('most_out_loop_lmprocessfile') local r, feeds - - r, feeds = tnn:getFeedFromReader(reader) - if (r == false) then break end + global_conf.timer:tic('tnn_beforeprocess') + r, feeds = tnn:getfeed_from_reader(reader) + if r == false then + break + end for t = 1, global_conf.chunk_size do tnn.err_inputs_m[t][1]:fill(1) for i = 1, global_conf.batch_size do - if (bit.band(feeds.flags_now[t][i], nerv.TNN.FC.HAS_LABEL) == 0) then + if bit.band(feeds.flags_now[t][i], nerv.TNN.FC.HAS_LABEL) == 0 then tnn.err_inputs_m[t][1][i - 1][0] = 0 end end end + global_conf.timer:toc('tnn_beforeprocess') --[[ for j = 1, global_conf.chunk_size, 1 do @@ -50,24 +54,28 @@ function LMTrainer.lm_process_file(global_conf, fn, tnn, do_train) tnn:net_propagate() - if (do_train == true) then + if do_train == true then tnn:net_backpropagate(false) tnn:net_backpropagate(true) end - + + global_conf.timer:tic('tnn_afterprocess') for t = 1, global_conf.chunk_size, 1 do + tnn.outputs_m[t][1]:copy_toh(neto_bakm) for i = 1, global_conf.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(tnn.outputs_m[t][1][i - 1][0])) + result:add("rnn", feeds.labels_s[t][i], math.exp(neto_bakm[i - 1][0])) end end end + tnn:move_right_to_nextmb({0}) --only copy for time 0 + global_conf.timer:toc('tnn_afterprocess') - tnn:moveRightToNextMB() global_conf.timer:toc('most_out_loop_lmprocessfile') --print log - if (result["rnn"].cn_w > next_log_wcn) then + if result["rnn"].cn_w > next_log_wcn then next_log_wcn = next_log_wcn + global_conf.log_w_num printf("%s %d words processed %s.\n", global_conf.sche_log_pre, result["rnn"].cn_w, os.date()) printf("\t%s log prob per sample :%f.\n", global_conf.sche_log_pre, result:logp_sample("rnn")) |