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path: root/nerv/examples/swb_baseline2.lua
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require 'htk_io'
gconf = {lrate = 0.8,
        wcost = 1e-6,
        momentum = 0.9,
        frm_ext = 5,
        rearrange = true, -- just to make the context order consistent with old TNet results, deprecated
        frm_trim = 5, -- trim the first and last 5 frames, TNet just does this, deprecated
        chunk_size = 1,
        tr_scp = "/speechlab/users/mfy43/swb50/train_bp.scp",
        cv_scp = "/speechlab/users/mfy43/swb50/train_cv.scp",
        ali = {file = "/speechlab/users/mfy43/swb50/ref.mlf",
               format = "map",
               format_arg = "/speechlab/users/mfy43/swb50/dict",
               dir = "*/",
               ext = "lab"},
        htk_conf = "/speechlab/users/mfy43/swb50/plp_0_d_a.conf",
        initialized_param = {"/speechlab/users/mfy43/swb50/swb_init.nerv",
                            "/speechlab/users/mfy43/swb50/swb_global_transf.nerv"},
}

local input_size = 429
local output_size = 3001
local hidden_size = 2048
local trainer = nerv.Trainer

function trainer:make_layer_repo(param_repo)
    local layer_repo = nerv.LayerRepo(
    {
        -- global transf
        ["nerv.BiasLayer"] =
        {
            blayer1 = {dim_in = {input_size}, dim_out = {input_size}, params = {bias = "bias0"}},
            blayer2 = {dim_in = {input_size}, dim_out = {input_size}, params = {bias = "bias1"}}
        },
        ["nerv.WindowLayer"] =
        {
            wlayer1 = {dim_in = {input_size}, dim_out = {input_size}, params = {window = "window0"}},
            wlayer2 = {dim_in = {input_size}, dim_out = {input_size