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local LBFGS,parent = torch.class('nn.LBFGSOptimization', 'nn.BatchOptimization')
function LBFGS:__init(...)
require 'liblbfgs'
parent.__init(self, ...)
xlua.unpack_class(self, {...},
'LBFGSOptimization', nil,
{arg='maxEvaluation', type='number',
help='maximum nb of function evaluations per pass (0 = no max)', default=0},
{arg='maxIterations', type='number',
help='maximum nb of iterations per pass (0 = no max)', default=0},
{arg='maxLineSearch', type='number',
help='maximum nb of steps in line search', default=20},
{arg='sparsity', type='number',
help='sparsity coef (Orthantwise C)', default=0},
{arg='parallelize', type='number',
help='parallelize onto N cores (experimental!)', default=1}
)
self.parameters = nnx.flattenParameters(nnx.getParameters(self.module))
self.gradParameters = nnx.flattenParameters(nnx.getGradParameters(self.module))
end
function LBFGS:optimize()
lbfgs.evaluate = self.evaluate
-- the magic function: will update the parameter vector
-- according to the l-BFGS method
self.output = lbfgs.run(self.parameters, self.gradParameters,
self.maxEvaluation, self.maxIterations, self.maxLineSearch,
self.sparsity, self.verbose)
end
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