81 lines
2.8 KiB
Python
81 lines
2.8 KiB
Python
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import torch
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from .optimizer import Optimizer
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class Rprop(Optimizer):
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"""Implements the resilient backpropagation algorithm.
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Args:
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params (iterable): iterable of parameters to optimize or dicts defining
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parameter groups
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lr (float, optional): learning rate (default: 1e-2)
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etas (Tuple[float, float], optional): pair of (etaminus, etaplis), that
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are multiplicative increase and decrease factors
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(default: (0.5, 1.2))
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step_sizes (Tuple[float, float], optional): a pair of minimal and
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maximal allowed step sizes (default: (1e-6, 50))
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"""
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def __init__(self, params, lr=1e-2, etas=(0.5, 1.2), step_sizes=(1e-6, 50)):
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if not 0.0 <= lr:
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raise ValueError("Invalid learning rate: {}".format(lr))
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if not 0.0 < etas[0] < 1.0 < etas[1]:
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raise ValueError("Invalid eta values: {}, {}".format(etas[0], etas[1]))
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defaults = dict(lr=lr, etas=etas, step_sizes=step_sizes)
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super(Rprop, self).__init__(params, defaults)
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@torch.no_grad()
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def step(self, closure=None):
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"""Performs a single optimization step.
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Args:
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closure (callable, optional): A closure that reevaluates the model
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and returns the loss.
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"""
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loss = None
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if closure is not None:
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with torch.enable_grad():
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loss = closure()
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for group in self.param_groups:
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for p in group['params']:
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if p.grad is None:
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continue
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grad = p.grad
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if grad.is_sparse:
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raise RuntimeError('Rprop does not support sparse gradients')
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state = self.state[p]
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# State initialization
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if len(state) == 0:
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state['step'] = 0
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state['prev'] = torch.zeros_like(p, memory_format=torch.preserve_format)
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state['step_size'] = grad.new().resize_as_(grad).fill_(group['lr'])
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etaminus, etaplus = group['etas']
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step_size_min, step_size_max = group['step_sizes']
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step_size = state['step_size']
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state['step'] += 1
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sign = grad.mul(state['prev']).sign()
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sign[sign.gt(0)] = etaplus
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sign[sign.lt(0)] = etaminus
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sign[sign.eq(0)] = 1
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# update stepsizes with step size updates
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step_size.mul_(sign).clamp_(step_size_min, step_size_max)
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# for dir<0, dfdx=0
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# for dir>=0 dfdx=dfdx
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grad = grad.clone(memory_format=torch.preserve_format)
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grad[sign.eq(etaminus)] = 0
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# update parameters
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p.addcmul_(grad.sign(), step_size, value=-1)
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state['prev'].copy_(grad)
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return loss
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