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-- This is code common to container modules, which are collections of
-- smaller constituent modules like Parallel, Sequential, etc.
local Container, parent = torch.class('nn.Container', 'nn.Module')
function Container:__init(...)
parent.__init(self, ...)
self.modules = {}
end
function Container:add(module)
table.insert(self.modules, module)
return self
end
function Container:get(index)
return self.modules[index]
end
function Container:size()
return #self.modules
end
function Container:zeroGradParameters()
for i=1,#self.modules do
self.modules[i]:zeroGradParameters()
end
end
function Container:updateParameters(learningRate)
for _,module in ipairs(self.modules) do
module:updateParameters(learningRate)
end
end
function Container:training()
for i=1,#self.modules do
self.modules[i]:training()
end
end
function Container:evaluate()
for i=1,#self.modules do
self.modules[i]:evaluate()
end
end
function Container:share(mlp, ...)
for i=1,#self.modules do
self.modules[i]:share(mlp.modules[i], ...);
end
end
function Container:reset(stdv)
for i=1,#self.modules do
self.modules[i]:reset(stdv)
end
end
function Container:parameters()
local function tinsert(to, from)
if type(from) == 'table' then
for i=1,#from do
tinsert(to,from[i])
end
else
table.insert(to,from)
end
end
local w = {}
local gw = {}
for i=1,#self.modules do
local mw,mgw = self.modules[i]:parameters()
if mw then
tinsert(w,mw)
tinsert(gw,mgw)
end
end
return w,gw
end
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