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local MatrixParam = nerv.class('nerv.MatrixParam', 'nerv.Param')
local LinearTransParam = nerv.class('nerv.LinearTransParam', 'nerv.MatrixParam')
local BiasParam = nerv.class('nerv.BiasParam', 'nerv.MatrixParam')
local AffineLayer = nerv.class('nerv.AffineLayer', 'nerv.Layer')
function MatrixParam:read(pcdata)
self.trans = self.gconf.mat_type.new_from_host(
nerv.MMatrixFloat.load(pcdata))
end
function MatrixParam:write(pfhandle)
self.trans:new_to_host():save(pfhandle)
end
function AffineLayer:__init(id, global_conf, layer_conf)
self.id = id
self.ltp = layer_conf.ltp
self.bp = layer_conf.bp
self.dim_in = layer_conf.dim_in
self.dim_out = layer_conf.dim_out
self.gconf = global_conf
self:check_dim_len(1, 1) -- exactly one input and one output
end
function AffineLayer:init()
if self.ltp.trans:ncol() ~= self.bp.trans:ncol() then
nerv.error("mismatching dimensions of linear transform and bias paramter")
end
if self.dim_in[1] ~= self.ltp.trans:nrow() then
nerv.error("mismatching dimensions of linear transform parameter and input")
end
if self.dim_out[1] ~= self.ltp.trans:ncol() then
nerv.error("mismatching dimensions of linear transform parameter and output")
end
-- linear transform correction
self.ltc = self.ltp.trans:create()
self.ltc:fill(0)
-- bias correction
self.bc = self.bp.trans:create()
self.bc:fill(0)
end
function nerv.AffineLayer:update(bp_err, input, output)
local ltp = self.ltp.trans
local bp = self.bp.trans
local ltc = self.ltc
local bc = self.bc
local gconf = self.gconf
-- momentum gain
local mmt_gain = 1.0 / (1.0 - gconf.momentum);
local n = input[1]:nrow() * mmt_gain
-- update corrections (accumulated errors)
ltc:mul(input[1], bp_err[1], 1.0, gconf.momentum, 'T', 'N')
bc:add(bc, bp_err[1]:colsum(), gconf.momentum, 1.0)
-- perform update
ltp:add(ltp, ltc, 1.0, -gconf.lrate / n)
bp:add(bp, bc, 1.0, -gconf.lrate / n)
-- weight decay
ltp:add(ltp, ltp, 1.0, -gconf.lrate * gconf.wcost)
end
function nerv.AffineLayer:propagate(input, output)
-- apply linear transform
output[1]:mul(input[1], self.ltp.trans, 1.0, 0.0, 'N', 'N')
-- add bias
output[1]:add_row(self.bp.trans, 1.0)
end
function nerv.AffineLayer:back_propagate(next_bp_err, bp_err, input, output)
next_bp_err[1]:mul(bp_err[1], self.ltp.trans, 1.0, 0.0, 'N', 'T')
end
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