path: root/nerv/layer/dropout.lua

local DropoutLayer = nerv.class("nerv.DropoutLayer", "nerv.Layer")

function DropoutLayer:__init(id, global_conf, layer_conf)
    nerv.Layer.__init(self, id, global_conf, layer_conf)
    if self.gconf.dropout_rate == nil then
        nerv.warning("[DropoutLayer:propagate] dropout rate is not set")
    end
    self:check_dim_len(1, 1) -- two inputs: nn output and label
end

function DropoutLayer:bind_params()
    -- do nothing
end

function DropoutLayer:init(batch_size, chunk_size)
    if self.dim_in[1] ~= self.dim_out[1] then
        nerv.error("mismatching dimensions of input and output")
    end
    if chunk_size == nil then
        chunk_size = 1
    end
    self.mask = {}
    for t = 1, chunk_size do
        self.mask[t] = self.mat_type(batch_size, self.dim_in[1])
    end
end

function DropoutLayer:batch_resize(batch_size, chunk_size)
    if chunk_size == nil then
        chunk_size = 1
    end
    for t = 1, chunk_size do
        if self.mask[t] == nil or self.mask[t]:nrow() ~= batch_size then
            self.mask[t] = self.mat_type(batch_size, self.dim_in[1])
        end
    end
end

function DropoutLayer:propagate(input, output, t)
    if t == nil then
        t = 1
    end
    if self.gconf.dropout_rate then
        self.mask[t]:rand_uniform()
        -- since we will lose a portion of the actvations, we multiply the
        -- activations by 1 / (1 - rate) to compensate
        self.mask[t]:thres_mask(self.mask[t], self.gconf.dropout_rate,
                                0, 1 / (1.0 - self.gconf.dropout_rate))
        output[1]:mul_elem(input[1], self.mask[t])
    else
        output[1]:copy_fromd(input[1])
    end
end

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

function DropoutLayer:back_propagate(bp_err, next_bp_err, input, output, t)
    if t == nil then
        t = 1
    end
    if self.gconf.dropout_rate then
        next_bp_err[1]:mul_elem(bp_err[1], self.mask[t])
    else
        next_bp_err[1]:copy_fromd(bp_err[1])
    end
end

function DropoutLayer:get_params()
    return nerv.ParamRepo({}, self.loc_type)
end