path: root/nerv/examples/lmptb/lm_trainer.lua

require 'lmptb.lmvocab'
require 'lmptb.lmfeeder'
require 'lmptb.lmutil'
require 'lmptb.layer.init'
--require 'tnn.init'
require 'lmptb.lmseqreader'

local LMTrainer = nerv.class('nerv.LMTrainer')

--local printf = nerv.printf

--The bias param update in nerv don't have wcost added
function nerv.BiasParam:update_by_gradient(gradient) 
    local gconf = self.gconf
    local l2 = 1 - gconf.lrate * gconf.wcost
    self:_update_by_gradient(gradient, l2, l2)
end

--Returns: LMResult
function LMTrainer.lm_process_file_rnn(global_conf, fn, tnn, do_train, p_conf)
    if p_conf == nil then
        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 do_train == true then
            nerv.warning("LMTrainer.lm_process_file_rnn: warning, one_sen_report is true while do_train is also true, strange")
        end
        nerv.printf("lm_process_file_rnn: one_sen report mode, set chunk_size to max_sen_len(%d)\n", 
                global_conf.max_sen_len)
        batch_size