SimulatedAnnealing
Pytorch Optimizer for Simulated Annealing
MIT License
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Python
SimulatedAnnealing
Pytorch Optimizer for Simulated Annealing
Usage
You need to define a sampler, eg:
sampler = UniformSampler(minval=-0.5, maxval=0.5, cuda=args.cuda)
# or
sampler = GaussianSampler(mu=0, sigma=1, cuda=args.cuda)
The sampler is used for the annealing schedule for Simulated Annealing.
The optimizer is a standard pytorch optimizer, however you need to pass a closure into the step call:
optimizer = SimulatedAnnealing(model.parameters(), sampler=sampler)
def closure():
output = model(data)
loss = F.nll_loss(output, target)
return loss
optimizer.step(closure)