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