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local GateFFFLayer = nerv.class('nerv.GateFFFLayer', 'nerv.Layer')
function GateFFFLayer:__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.ltp1 = self:find_param("ltp1", layer_conf, global_conf, nerv.LinearTransParam, {self.dim_in[1], self.dim_out[1]}) --layer_conf.ltp
self.ltp2 = self:find_param("ltp2", layer_conf, global_conf, nerv.LinearTransParam, {self.dim_in[2], self.dim_out[1]}) --layer_conf.ltp
self.ltp3 = self:find_param("ltp3", layer_conf, global_conf, nerv.LinearTransParam, {self.dim_in[3], self.dim_out[1]}) --layer_conf.ltp
self.bp = self:find_param("bp", layer_conf, global_conf, nerv.BiasParam, {1, self.dim_out[1]})--layer_conf.bp
self:check_dim_len(3, 1) -- exactly one input and one output
end
function GateFFFLayer:init(batch_size)
if self.ltp1.trans:ncol() ~= self.bp.trans:ncol() or
self.ltp2.trans:ncol() ~= self.bp.trans:ncol() or
self.ltp3.trans:ncol() ~= self.bp.trans:ncol() then
nerv.error("mismatching dimensions of linear transform and bias paramter")
end
if self.dim_in[1] ~= self.ltp1.trans:nrow() or
self.dim_in[2] ~= self.ltp2.trans:nrow() or
self.dim_in[3] ~= self.ltp3.trans:nrow() then
nerv.error("mismatching dimensions of linear transform parameter and input")
end
if self.dim_out[1] ~= self.ltp1.trans:ncol() then
nerv.error("mismatching dimensions of linear transform parameter and output")
end
self.ltp1:train_init()
self.ltp2:train_init()
self.ltp3:train_init()
self.bp:train_init()
self.err_bakm = self.gconf.cumat_type(batch_size, self.dim_out[1])
end
function GateFFFLayer:batch_resize(batch_size)
if self.err_m:nrow() ~= batch_size then
self.err_bakm = self.gconf.cumat_type(batch_size, self.dim_out[1])
end
end
function GateFFFLayer:propagate(input, output)
-- apply linear transform
output[1]:mul(input[1], self.ltp1.trans, 1.0, 0.0, 'N', 'N')
output[1]:mul(input[2], self.ltp2.trans, 1.0, 1.0, 'N', 'N')
output[1]:mul(input[3], self.ltp3.trans, 1.0, 1.0, 'N', 'N')
-- add bias
output[1]:add_row(self.bp.trans, 1.0)
output[1]:sigmoid(output[1])
end
function GateFFFLayer:back_propagate(bp_err, next_bp_err, input, output)
self.err_bakm:sigmoid_grad(bp_err[1], output[1])
next_bp_err[1]:mul(self.err_bakm, self.ltp1.trans, 1.0, 0.0, 'N', 'T')
next_bp_err[2]:mul(self.err_bakm, self.ltp2.trans, 1.0, 0.0, 'N', 'T')
next_bp_err[3]:mul(self.err_bakm, self.ltp3.trans, 1.0, 0.0, 'N', 'T')
end
function GateFFFLayer:update(bp_err, input, output)
self.err_bakm:sigmoid_grad(bp_err[1], output[1])
self.ltp1:update_by_err_input(self.err_bakm, input[1])
self.ltp2:update_by_err_input(self.err_bakm, input[2])
self.ltp3:update_by_err_input(self.err_bakm, input[3])
self.bp:update_by_gradient(self.err_bakm:colsum())
end
function GateFFFLayer:get_params()
return nerv.ParamRepo({self.ltp1, self.ltp2, self.ltp3, self.bp})
end
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