local function build_trainer(ifname)
    local param_repo = nerv.ParamRepo()
    param_repo:import(ifname, nil, gconf)
    local layer_repo = make_layer_repo(param_repo)
    local network = get_network(layer_repo)
    local global_transf = get_global_transf(layer_repo)
    local input_order = get_input_order()
    local mat_type
    if gconf.use_cpu then
        mat_type = gconf.mmat_type
    else
        mat_type = gconf.cumat_type
    end
    local iterative_trainer = function (prefix, scp_file, bp)
        gconf.randomize = bp
        -- build buffer
        local buffer = make_buffer(make_readers(scp_file, layer_repo))
        -- initialize the network
        network:init(gconf.batch_size)
        gconf.cnt = 0
        err_input = {mat_type(gconf.batch_size, 1)}
        err_input[1]:fill(1)
        for data in buffer.get_data, buffer do
            -- prine stat periodically
            gconf.cnt = gconf.cnt + 1
            if gconf.cnt == 1000 then
                print_stat(layer_repo)
                mat_type.print_profile()
                mat_type.clear_profile()
                gconf.cnt = 0
                -- break
            end
            local input = {}
--            if gconf.cnt == 1000 then break end
            for i, e in ipairs(input_order) do
                local id = e.id
                if data[id] == nil then
                    nerv.error("input data %s not found", id)
                end
                local transformed
                if e.global_transf then
                    transformed = nerv.speech_utils.global_transf(data[id],
                                        global_transf,
                                        gconf.frm_ext or 0, 0,
                                        gconf)
                else
                    transformed = data[id]
                end
                table.insert(input, transformed)
            end
            local output = {mat_type(gconf.batch_size, 1)}
            err_output = {}
            for i = 1, #input do
                table.insert(err_output, input[i]:create())
            end
            network:propagate(input, output)
            if bp then
                network:back_propagate(err_input, err_output, input, output)
                network:update(err_input, input, output)
            end
            -- collect garbage in-time to save GPU memory
            collectgarbage("collect")
        end
        print_stat(layer_repo)
        mat_type.print_profile()
        mat_type.clear_profile()
        if (not bp) and prefix ~= nil then
            nerv.info("writing back...")
            local fname = string.format("%s_cv%.3f.nerv",
                            prefix, get_accuracy(layer_repo))
            network:get_params():export(fname, nil)
        end
        return get_accuracy(layer_repo)
    end
    return iterative_trainer
end

local function check_and_add_defaults(spec)
    for k, v in pairs(spec) do
        gconf[k] = opts[string.gsub(k, '_', '-')].val or gconf[k] or v
    end
end

local function make_options(spec)
    local options = {}
    for k, v in pairs(spec) do
        table.insert(options,
                    {string.gsub(k, '_', '-'), nil, type(v), default = v})
    end
    return options
end

local function print_help(options)
    nerv.printf("Usage: <asr_trainer.lua> [options] network_config.lua\n")
    nerv.print_usage(options)
end

local function print_gconf()
    local key_maxlen = 0
    for k, v in pairs(gconf) do
        key_maxlen = math.max(key_maxlen, #k or 0)
    end
    local function pattern_gen()
        return string.format("%%-%ds = %%s\n", key_maxlen)
    end
    nerv.info("ready to train with the following gconf settings:")
    nerv.printf(pattern_gen(), "Key", "Value")
    for k, v in pairs(gconf) do
        nerv.printf(pattern_gen(), k or "", v or "")
    end
end

local trainer_defaults = {
    lrate = 0.8,
    batch_size = 256,
    buffer_size = 81920,
    wcost = 1e-6,
    momentum = 0.9,
    start_halving_inc = 0.5,
    halving_factor = 0.6,
    end_halving_inc = 0.1,
    min_iter = 1,
    max_iter = 20,
    min_halving = 5,
    do_halving = false,
    tr_scp = nil,
    cv_scp = nil,
    cumat_type = nerv.CuMatrixFloat,
    mmat_type = nerv.MMatrixFloat,
    debug = false
}

local options = make_options(trainer_defaults)
table.insert(options, {"help", "h", "boolean",
                        default = false, desc = "show this help information"})

arg, opts = nerv.parse_args(arg, options)

if #arg < 1 or opts["help"].val then
    print_help(options)
    return
end

dofile(arg[1])

--[[

Rule: command-line option overrides network config overrides trainer default.
Note: config key like aaa_bbbb_cc could be overriden by specifying
--aaa-bbbb-cc to command-line arguments.

]]--

check_and_add_defaults(trainer_defaults)

local pf0 = gconf.initialized_param
local trainer = build_trainer(pf0)
local accu_best = trainer(nil, gconf.cv_scp, false)

print_gconf()
nerv.info("initial cross validation: %.3f", accu_best)
for i = 1, gconf.max_iter do
    nerv.info("[NN] begin 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(
                        string.format("%s_%s_iter_%d_lr%f_tr%.3f",
                            string.gsub(
                                (string.gsub(pf0[1], "(.*/)(.*)", "%2")),
                                "(.*)%..*", "%1"),
                            os.date("%Y%m%d%H%M%S"),
                            i, gconf.lrate,
                            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 gconf.do_halving and
        accu_diff < gconf.end_halving_inc and
        i > gconf.min_iter then
        break
    end
    if accu_diff < gconf.start_halving_inc and
        i >= gconf.min_halving then
        gconf.do_halving = true
    end
    if gconf.do_halving then
        gconf.lrate = gconf.lrate * gconf.halving_factor
    end
    if accu_new > accu_best then
        accu_best = accu_new
    end
--    nerv.Matrix.print_profile()
end