optim package

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Optimization package

This package contains several optimization routines for Torch.
Each optimization algorithm is based on the same interface:

x*, {f}, ... = optim.method(func, x, state)

where:

func: a user-defined closure that respects this API: (f,df/dx)=func(x). Here f could be the loss for the network.
x: the current parameter vector (a 1D torch.Tensor)
state: a table of parameters, and state variables, dependent upon the algorithm
x*: the new parameter vector that minimizes f, x∗=argminxf(x)
{f}: a table of all f values, in the order they’ve been evaluated (for some simple algorithms, like SGD, #f == 1)

Important Note

The state table is used to hold the state of the algorihtm.
It’s usually initialized once, by the user, and then passed to the optim function
as a black box. Example:

state = {   learningRate = 1e-3,   momentum = 0.5}for i,sample in ipairs(training_samples) do    local func = function(x)       -- define eval function       return f,df_dx    end    optim.sgd(func,x,state)end

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