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local MSELayer = nerv.class("nerv.MSELayer", "nerv.Layer")
function MSELayer:__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:check_dim_len(2, -1)
end
function MSELayer:init()
if self.dim_in[1] ~= self.dim_in[2] then
nerv.error("mismatching dimensions of previous network output and labels")
end
self.total_mse = 0.0
self.total_frames = 0
end
function MSELayer:update(bp_err, input, output)
-- no params, therefore do nothing
end
function MSELayer:propagate(input, output)
local mse = input[1]:create()
mse:add(input[1], input[2], 1.0, -1.0)
self.diff = mse:create()
self.diff:copy_fromd(mse)
mse:mul_elem(mse, mse)
mse = mse:rowsum(mse)
local scale = nerv.CuMatrixFloat(mse:nrow(), 1)
scale:fill(1 / input[1]:ncol())
mse:scale_rows_by_col(scale)
if output[1] ~= nil then
output[1]:copy_fromd(mse)
end
self.total_mse = self.total_mse + mse:colsum()[0]
self.total_frames = self.total_frames + mse:nrow()
end
-- NOTE: must call propagate before back_propagate
function MSELayer:back_propagate(next_bp_err, bp_err, input, output)
local nbe = next_bp_err[1]
nbe:copy_fromd(self.diff)
self.diff = nil
if bp_err[1] ~= nil then
nbe:scale_rows_by_col(bp_err[1])
end
end
function MSELayer:get_params()
return {}
end
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