function get_global_conf() local global_conf = { lrate = 0.15, wcost = 1e-5, momentum = 0, clip = 5, cumat_type = nerv.CuMatrixFloat, mmat_type = nerv.MMatrixFloat, vocab_size = 10000, nn_act_default = 0, hidden_size = 300, layer_num = 1, chunk_size = 15, batch_size = 20, max_iter = 35, param_random = function() return (math.random() / 5 - 0.1) end, dropout_rate = 0.5, timer = nerv.Timer(), pr = nerv.ParamRepo(), } return global_conf end function get_layers(global_conf) local pr = global_conf.pr local layers = { ['nerv.LSTMLayer'] = {}, ['nerv.DropoutLayer'] = {}, ['nerv.SelectLinearLayer'] = { ['select'] = {dim_in = {1}, dim_out = {global_conf.hidden_size}, vocab = global_conf.vocab_size, pr = pr}, }, ['nerv.CombinerLayer'] = {}, ['nerv.AffineLayer'] = { output = {dim_in = {global_conf.hidden_size}, dim_out = {global_conf.vocab_size}, pr = pr} }, ['nerv.SoftmaxCELayer'] = { softmax = {dim_in = {global_conf.vocab_size, global_conf.vocab_size}, dim_out = {1}, compressed = true}, }, } for i = 1, global_conf.layer_num do layers['nerv.LSTMLayer']['lstm' .. i] = {dim_in = {global_conf.hidden_size, global_conf.hidden_size, global_conf.hidden_size}, dim_out = {global_conf.hidden_size, global_conf.hidden_size}, pr = pr} layers['nerv.DropoutLayer']['dropout' .. i] = {dim_in = {global_conf.hidden_size}, dim_out = {global_conf.hidden_size}} layers['nerv.CombinerLayer']['dup' .. i] = {dim_in = {global_conf.hidden_size}, dim_out = {global_conf.hidden_size, global_conf.hidden_size}, lambda = {1}} end return layers end function get_connections(global_conf) local connections = { {'[1]', 'select[1]', 0}, {'select[1]', 'lstm1[1]', 0}, {'dropout' .. global_conf.layer_num .. '[1]', 'output[1]', 0}, {'output[1]', 'softmax[1]', 0}, {'[2]', 'softmax[2]', 0}, {'softmax[1]', '[1]', 0}, } for i = 1, global_conf.layer_num do table.insert(connections, {'lstm' .. i .. '[1]', 'dup' .. i .. '[1]', 0}) table.insert(connections, {'lstm' .. i .. '[2]', 'lstm' .. i .. '[3]', 1}) table.insert(connections, {'dup' .. i .. '[1]', 'lstm' .. i .. '[2]', 1}) table.insert(connections, {'dup' .. i .. '[2]', 'dropout' .. i .. '[1]', 0}) if i > 1 then table.insert(connections, {'dropout' .. (i - 1) .. '[1]', 'lstm' .. i .. '[1]', 0}) end end return connections end