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local LSTMGateLayer = nerv.class('nerv.LSTMGateLayer', 'nerv.Layer')
-- NOTE: this is a full matrix gate
function LSTMGateLayer:__init(id, global_conf, layer_conf)
nerv.Layer.__init(self, id, global_conf, layer_conf)
self.param_type = layer_conf.param_type
self:check_dim_len(-1, 1) --accept multiple inputs
self:bind_params()
end
function LSTMGateLayer:bind_params()
local lconf = self.lconf
lconf.no_update_ltp1 = lconf.no_update_ltp1 or lconf.no_update_ltp
for i = 1, #self.dim_in do
local pid = "ltp" .. i
local pid_list = i == 1 and {pid, "ltp"} or pid
self["ltp" .. i] = self:find_param(pid_list, lconf, self.gconf,
nerv.LinearTransParam,
{self.dim_in[i], self.dim_out[1]})
if self.param_type[i] == 'D' then
self["ltp" .. i].trans:diagonalize()
end
local no_update = lconf["no_update_ltp" .. i]
if (no_update ~= nil) and no_update or lconf.no_update_all then
self["ltp" .. i].no_update = true
end
end
self.ltp = self.ltp1 -- alias of ltp1
self.bp = self:find_param("bp", lconf, self.gconf,
nerv.BiasParam, {1, self.dim_out[1]},
nerv.Param.gen_zero)
local no_update = lconf["no_update_bp"]
if (no_update ~= nil) and no_update or lconf.no_update_all then
self.bp.no_update = true
end
end
function LSTMGateLayer:init(batch_size)
if self.dim_out[1] ~= self.bp.trans:ncol() then
nerv.error("mismatching dimensions of linear transform and bias paramter")
end
for i = 1, #self.dim_in do
if self.dim_in[i] ~= self["ltp" .. i].trans:nrow() then
nerv.error("mismatching dimensions of linear transform parameter and input")
end
if self.dim_out[1] ~= self["ltp" .. i].trans:ncol() then
nerv.error("mismatching dimensions of linear transform parameter and output")
end
self["ltp" .. i]:train_init()
end
self.bp:train_init()
self.err_bakm = self.mat_type(batch_size, self.dim_out[1])
end
function LSTMGateLayer:batch_resize(batch_size)
if self.err_m:nrow() ~= batch_size then
self.err_bakm = self.mat_type(batch_size, self.dim_out[1])
end
end
function LSTMGateLayer:propagate(input, output)
-- apply linear transform
output[1]:mul(input[1], self.ltp1.trans, 1.0, 0.0, 'N', 'N')
for i = 2, #self.dim_in do
output[1]:mul(input[i], self["ltp" .. i].trans, 1.0, 1.0, 'N', 'N')
end
-- add bias
output[1]:add_row(self.bp.trans, 1.0)
output[1]:sigmoid(output[1])
end
function LSTMGateLayer:back_propagate(bp_err, next_bp_err, input, output)
self.err_bakm:sigmoid_grad(bp_err[1], output[1])
for i = 1, #self.dim_in do
next_bp_err[i]:mul(self.err_bakm, self["ltp" .. i].trans, 1.0, 0.0, 'N', 'T')
self["ltp" .. i]:back_propagate_by_err_input(self.err_bakm, input[i])
end
self.bp:back_propagate_by_gradient(self.err_bakm:colsum())
end
function LSTMGateLayer:update()
for i = 1, #self.dim_in do
self["ltp" .. i]:update_by_err_input()
if self.param_type[i] == 'D' then
self["ltp" .. i].trans:diagonalize()
end
end
self.bp:update_by_gradient()
end
function LSTMGateLayer:get_params()
local pr = nerv.ParamRepo({self.bp}, self.loc_type)
for i = 1, #self.dim_in do
pr:add(self["ltp" .. i])
end
return pr
end
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