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--[[ ADAGRAD implementation for SGD
ARGS:
- `opfunc` : a function that takes a single input (X), the point of
evaluation, and returns f(X) and df/dX
- `x` : the initial point
- `state` : a table describing the state of the optimizer; after each
call the state is modified
- `state.learningRate` : learning rate
- `state.paramVariance` : vector of temporal variances of parameters
- `state.weightDecay` : scalar that controls weight decay
RETURN:
- `x` : the new x vector
- `f(x)` : the function, evaluated before the update
]]
function optim.adagrad(opfunc, x, config, state)
-- (0) get/update state
if config == nil and state == nil then
print('no state table, ADAGRAD initializing')
end
local config = config or {}
local state = state or config
local lr = config.learningRate or 1e-3
local lrd = config.learningRateDecay or 0
local wd = config.weightDecay or 0
state.evalCounter = state.evalCounter or 0
local nevals = state.evalCounter
-- (1) evaluate f(x) and df/dx
local fx,dfdx = opfunc(x)
-- (2) weight decay with a single parameter
if wd ~= 0 then
dfdx:add(wd, x)
end
-- (3) learning rate decay (annealing)
local clr = lr / (1 + nevals*lrd)
-- (4) parameter update with single or individual learning rates
if not state.paramVariance then
state.paramVariance = torch.Tensor():typeAs(x):resizeAs(dfdx):zero()
state.paramStd = torch.Tensor():typeAs(x):resizeAs(dfdx)
end
state.paramVariance:addcmul(1,dfdx,dfdx)
state.paramStd:resizeAs(state.paramVariance):copy(state.paramVariance):sqrt()
x:addcdiv(-clr, dfdx,state.paramStd:add(1e-10))
-- (5) update evaluation counter
state.evalCounter = state.evalCounter + 1
-- return x*, f(x) before optimization
return x,{fx}
end
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