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local Dropout = nerv.class("nerv.DropoutLayerT", "nerv.LayerT")
function Dropout:__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:check_dim_len(1, 1) -- two inputs: nn output and label
end
function Dropout: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_t = {}
for t = 1, chunk_size do
self.mask_t[t] = self.gconf.cumat_type(batch_size, self.dim_in[1])
end
end
function Dropout: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[t] == nil or self.mask_t[t]:nrow() ~= batch_size then
self.mask_t[t] = self.gconf.cumat_type(batch_size, self.dim_in[1])
end
end
end
function Dropout:propagate(input, output, t)
if t == nil then
t = 1
end
if self.gconf.dropout_rate == nil then
nerv.info("DropoutLayerT:propagate warning, global_conf.dropout_rate is nil, setting it zero")
self.gconf.dropout_rate = 0
end
if self.gconf.dropout_rate == 0 then
output[1]:copy_fromd(input[1])
else
self.mask_t[t]:rand_uniform()
--since we will lose a portion of the actvations, we multiply the activations by 1/(1-dr) to compensate
self.mask_t[t]:thres_mask(self.mask_t[t], self.gconf.dropout_rate, 0, 1 / (1.0 - self.gconf.dropout_rate))
output[1]:mul_elem(input[1], self.mask_t[t])
end
end
function Dropout:update(bp_err, input, output, t)
-- no params, therefore do nothing
end
function Dropout:back_propagate(bp_err, next_bp_err, input, output, t)
if t == nil then
t = 1
end
if self.gconf.dropout_rate == 0 then
next_bp_err[1]:copy_fromd(bp_err[1])
else
next_bp_err[1]:mul_elem(bp_err[1], self.mask_t[t])
end
end
function Dropout:get_params()
return nerv.ParamRepo({})
end
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