path: root/nerv/examples/lmptb/rnn/softmax_ce_t.lua

local SoftmaxCELayer = nerv.class("nerv.SoftmaxCELayerT", "nerv.LayerT")

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(batch_size, chunk_size)
    if not self.compressed and (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
    self.softmax_t = {}
    self.ce_t = {}
    for t = 1, chunk_size do
        self.softmax_t[t] = self.gconf.cumat_type(batch_size, self.dim_in[1])
        self.ce_t[t] = self.gconf.cumat_type(batch_size, self.dim_in[1])
    end
end

function SoftmaxCELayer:batch_resize(batch_size)
    for t = 1, chunk_size do
        if self.softmax_t[t]:nrow() ~= batch_resize then
            self.softmax_t[t] = self.gconf.cumat_type(batch_size, self.dim_in[1])
            self.ce_t[t] = self.gconf.cumat_type(batch_size, self.dim_in[1])
        end
    end
end

function SoftmaxCELayer:update(bp_err, input, output, t)
    -- no params, therefore do nothing
end

function SoftmaxCELayer:propagate(input, output, t)
    local softmax = self.softmax_t[t]
    local ce = self.ce_t[t]
    local classified = softmax:softmax(input[1])
    local label = input[2]
    ce:log_elem(softmax)
    if self.compressed then
        label = label:decompress(input[1]:ncol())
    end
    ce:mul_elem(ce, label)
    ce = ce:rowsum()
    if output[1] ~= nil then
        output[1]:copy_fromd(ce)
    end
    -- add total ce
    self.total_ce = self.total_ce - ce:colsum()[0][0]
    self.total_frames = self.total_frames + softmax:nrow()
    -- TODO: add colsame for uncompressed label
    if self.compressed then
        self.total_correct = self.total_correct + classified:colsame(input[2])[0][0]
    end
end

function SoftmaxCELayer:back_propagate(bp_err, next_bp_err, input, output, t)
    -- softmax output - label
    local label = input[2]
    if self.compressed then
        label = label:decompress(input[1]:ncol())
    end
    local nbe = next_bp_err[1]
    nbe:add(self.softmax_t[t], label, 1.0, -1.0)
    if bp_err[1] ~= nil then
        nbe:scale_rows_by_col(bp_err[1])
    end
end

function SoftmaxCELayer:get_params()
    return nerv.ParamRepo({})
end