function build_trainer(ifname)
    local param_repo = make_param_repo(ifname)
    local sublayer_repo = make_sublayer_repo(param_repo)
    local layer_repo = make_layer_repo(sublayer_repo, param_repo)
    local crit = get_criterion_layer(sublayer_repo)
    local network = get_network(layer_repo)
    local iterative_trainer = function (prefix, scp_file, bp)
        gconf.randomize = bp
        -- build buffer
        local buffer = make_buffer(make_reader(scp_file, layer_repo))
        -- initialize the network
        network:init(gconf.batch_size)
        gconf.cnt = 0
        for data in buffer.get_data, buffer do
            -- prine stat periodically
            gconf.cnt = gconf.cnt + 1
            if gconf.cnt == 1000 then
                print_stat(crit)
                gconf.cnt = 0
            end
--            if gconf.cnt == 100 then break end

            input = {data.main_scp, data.phone_state}
            output = {}
            err_input = {}
            err_output = {input[1]:create()}
            network:propagate(input, output)
            if bp then
                network:back_propagate(err_output, err_input, input, output)
                network:update(err_input, input, output)
            end
            -- collect garbage in-time to save GPU memory
            collectgarbage("collect")
        end
        print_stat(crit)
        if (not bp) and prefix ~= nil then
            nerv.info("writing back...")
            local accu_cv = get_accuracy(crit)
            cf = nerv.ChunkFile(prefix .. "_cv" .. accu_cv .. ".nerv", "w")
            for i, p in ipairs(network:get_params()) do
                cf:write_chunk(p)
            end
            cf:close()
        end
        return get_accuracy(crit)
    end
    return iterative_trainer
end

dofile(arg[1])
start_halving_inc = 0.5
halving_factor = 0.6
end_halving_inc = 0.1
min_iter = 1
max_iter = 20
min_halving = 6
gconf.batch_size = 256
gconf.buffer_size = 81920

local pf0 = gconf.initialized_param
local trainer = build_trainer(pf0)
--local trainer = build_trainer("c3.nerv")
local accu_best = trainer(nil, gconf.cv_scp, false)
local do_halving = false

nerv.info("initial cross validation: %.3f", accu_best)
for i = 1, max_iter do
    nerv.info("iteration %d with lrate = %.6f", i, gconf.lrate)
    local accu_tr = trainer(nil, gconf.tr_scp, true)
    nerv.info("[TR] training set %d: %.3f", i, accu_tr)
    local accu_new = trainer(pf0 .. "_iter" .. i .. "_tr" .. accu_tr,
                            gconf.cv_scp, false)
    nerv.info("[CV] cross validation %d: %.3f", i, accu_new)
    -- TODO: revert the weights
    local accu_diff = accu_new - accu_best
    if do_halving and accu_diff < end_halving_inc and i > min_iter then
        break
    end
    if accu_diff < start_halving_inc and i >= min_halving then
        do_halving = true
    end
    if do_halving then
        gconf.lrate = gconf.lrate * halving_factor
    end
    if accu_new > accu_best then
        accu_best = accu_new
    end
end
nerv.Matrix.print_profile()