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local SoftmaxCELayer = nerv.class("nerv.SoftmaxCELayer", "nerv.Layer")
function SoftmaxCELayer:__init(id, global_conf, layer_conf)
self.id = id
self.gconf = global_conf
self.dim_in = layer_conf.dim_in
self.dim_out = layer_conf.dim_out
self.compressed = layer_conf.compressed
if self.compressed == nil then
self.compressed = false
end
self:check_dim_len(2, -1) -- two inputs: nn output and label
end
function SoftmaxCELayer:init()
if self.dim_in[1] ~= self.dim_in[2] then
nerv.error("mismatching dimensions of previous network output and labels")
end
self.total_ce = 0.0
self.total_correct = 0
self.total_frames = 0
end
function SoftmaxCELayer:update(bp_err, input, output)
-- no params, therefore do nothing
end
function SoftmaxCELayer:propagate(input, output)
local soutput = input[1]:create() -- temporary value for calc softmax
self.soutput = soutput
local classified = soutput:softmax(input[1])
local ce = soutput:create()
ce:log_elem(soutput)
local label = input[2]
if self.compressed then
label = label:decompress(input[1]:ncol())
end
ce:mul_elem(ce, label)
-- add total ce
self.total_ce = self.total_ce - ce:rowsum():colsum()[0]
self.total_frames = self.total_frames + soutput:nrow()
-- TODO: add colsame for uncompressed label
if self.compressed then
self.total_correct = self.total_correct + classified:colsame(input[2])[0]
end
end
function SoftmaxCELayer:back_propagate(next_bp_err, bp_err, input, output)
-- softmax output - label
local label = input[2]
if self.compressed then
label = label:decompress(input[1]:ncol())
end
next_bp_err[1]:add(self.soutput, label, 1.0, -1.0)
end
function SoftmaxCELayer:get_params()
return {}
end
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