Source code for torch.optim.rprop

import torch
from .optimizer import Optimizer

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


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