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local CombinerLayer = nerv.class('nerv.CombinerLayer', 'nerv.Layer')
function CombinerLayer:__init(id, global_conf, layer_conf)
self.id = id
self.lambda = layer_conf.lambda
self.dim_in = layer_conf.dim_in
self.dim_out = layer_conf.dim_out
self.gconf = global_conf
self:check_dim_len(#self.lambda, -1)
if #self.dim_in < 1 then
nerv.error("no input specified")
end
if #self.dim_out < 1 then
nerv.error("no output specified")
end
end
function CombinerLayer:init(batch_size)
local dim = self.dim_in[1]
for i = 2, #self.dim_in do
if self.dim_in[i] ~= dim then
nerv.error("mismatching dimensions of inputs")
end
end
for i = 1, #self.dim_out do
if self.dim_out[i] ~= dim then
nerv.error("mismatching dimensions of inputs/outputs")
end
end
self.sum = self.gconf.cumat_type(batch_size, dim)
end
function CombinerLayer:update(bp_err, input, output)
end
function CombinerLayer:propagate(input, output)
output[1]:fill(0)
for i = 1, #self.dim_in do
output[1]:add(output[1], input[i], 1.0, self.lambda[i])
end
for i = 2, #self.dim_out do
output[i]:copy_fromd(output[1])
end
end
function CombinerLayer:back_propagate(bp_err, next_bp_err, input, output)
local sum = self.sum
sum:copy_fromd(bp_err[1])
for i = 2, #self.dim_out do
sum:add(sum, bp_err[i], 1.0, 1.0)
end
for i = 1, #self.dim_in do
next_bp_err[i]:add(next_bp_err[i], sum, 0.0, self.lambda[i])
end
end
function CombinerLayer:get_params()
return nerv.ParamRepo({})
end
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