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local Recurrent = nerv.class('nerv.AffineRecurrentLayer', 'nerv.Layer')
--id: string
--global_conf: table
--layer_conf: table
--Get Parameters
function Recurrent:__init(id, global_conf, layer_conf)
self.id = id
self.dim_in = layer_conf.dim_in
self.dim_out = layer_conf.dim_out
self.gconf = global_conf
self.bp = self:find_param("bp", layer_conf, global_conf, nerv.BiasParam, {1, self.dim_out[1]}) --layer_conf.bp
self.ltp_hh = self:find_param("ltp_hh", layer_conf, global_conf, nerv.LinearTransParam, {self.dim_in[2], self.dim_out[1]}) --layer_conf.ltp_hh --from hidden to hidden
self:check_dim_len(2, 1)
self.direct_update = layer_conf.direct_update
self.clip = layer_conf.clip --clip error in back_propagate
end
--Check parameter
function Recurrent:init(batch_size)
if (self.ltp_hh.trans:ncol() ~= self.bp.trans:ncol()) then
nerv.error("mismatching dimensions of ltp and bp")
end
if (self.dim_in[1] ~= self.ltp_hh.trans:nrow() or
self.dim_in[2] ~= self.ltp_hh.trans:nrow()) then
nerv.error("mismatching dimensions of ltp and input")
end
if (self.dim_out[1] ~= self.bp.trans:ncol()) then
nerv.error("mismatching dimensions of bp and output")
end
self.ltp_hh_grad = self.ltp_hh.trans:create()
self.ltp_hh:train_init()
self.bp:train_init()
end
function Recurrent:batch_resize(batch_size)
-- do nothing
end
function Recurrent:update(bp_err, input, output)
if self.direct_update == true then
local ltp_hh = self.ltp_hh.trans
local bp = self.bp.trans
local gconf = self.gconf
if (gconf.momentum > 0) then
-- momentum gain
local mmt_gain = 1.0 / (1.0 - gconf.momentum)
local n = input[1]:nrow() * mmt_gain
-- update corrections (accumulated errors)
self.ltp_hh.correction:mul(input[2], bp_err[1], 1.0, gconf.momentum, 'T', 'N')
self.bp.correction:add(self.bp.correction, bp_err[1]:colsum(), gconf.momentum, 1.0)
-- perform update and weight decay
ltp_hh:add(ltp_hh, self.ltp_hh.correction, 1.0 - gconf.lrate * gconf.wcost / gconf.batch_size, - gconf.lrate / n)
bp:add(bp, self.bp.correction, 1.0 - gconf.lrate * gconf.wcost / gconf.batch_size, - gconf.lrate / n)
else
ltp_hh:mul(input[2], bp_err[1], - gconf.lrate / gconf.batch_size, 1.0 - gconf.wcost * gconf.lrate / gconf.batch_size, 'T', 'N')
bp:add(bp, bp_err[1]:colsum(), 1.0 - gconf.lrate * gconf.wcost / gconf.batch_size, - gconf.lrate / gconf.batch_size)
end
else
--self.ltp_hh_grad:mul(input[2], bp_err[1], 1.0, 0.0, 'T', 'N')
self.ltp_hh:update_by_err_input(bp_err[1], input[2])
self.bp:update_by_gradient(bp_err[1]:colsum())
end
end
function Recurrent:propagate(input, output)
output[1]:copy_fromd(input[1])
output[1]:mul(input[2], self.ltp_hh.trans, 1.0, 1.0, 'N', 'N')
output[1]:add_row(self.bp.trans, 1.0)
end
function Recurrent:back_propagate(bp_err, next_bp_err, input, output)
next_bp_err[1]:copy_fromd(bp_err[1])
next_bp_err[2]:mul(bp_err[1], self.ltp_hh.trans, 1.0, 0.0, 'N', 'T')
--[[
for i = 0, next_bp_err[2]:nrow() - 1 do
for j = 0, next_bp_err[2]:ncol() - 1 do
if (next_bp_err[2][i][j] > 10) then next_bp_err[2][i][j] = 10 end
if (next_bp_err[2][i][j] < -10) then next_bp_err[2][i][j] = -10 end
end
end
]]--
if (self.clip ~= nil) then
next_bp_err[2]:clip(- self.clip, self.clip)
end
end
function Recurrent:get_params()
return nerv.ParamRepo({self.ltp_hh, self.bp})
end
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