Source code for torch.optim.rprop

import torch
from . import _functional as F
from .optimizer import Optimizer

[docs]class Rprop(Optimizer): r"""Implements the resilient backpropagation algorithm. .. math:: \begin{aligned} &\rule{110mm}{0.4pt} \\ &\textbf{input} : \theta_0 \in \mathbf{R}^d \text{ (params)},f(\theta) \text{ (objective)}, \\ &\hspace{13mm} \eta_{+/-} \text{ (etaplus, etaminus)}, \Gamma_{max/min} \text{ (step sizes)} \\ &\textbf{initialize} : g^0_{prev} \leftarrow 0, \: \eta_0 \leftarrow \text{lr (learning rate)} \\ &\rule{110mm}{0.4pt} \\ &\textbf{for} \: t=1 \: \textbf{to} \: \ldots \: \textbf{do} \\ &\hspace{5mm}g_t \leftarrow \nabla_{\theta} f_t (\theta_{t-1}) \\ &\hspace{5mm} \textbf{for} \text{ } i = 0, 1, \ldots, d-1 \: \mathbf{do} \\ &\hspace{10mm} \textbf{if} \: g^i_{prev} g^i_t > 0 \\ &\hspace{15mm} \eta^i_t \leftarrow \mathrm{min}(\eta^i_{t-1} \eta_{+}, \Gamma_{max}) \\ &\hspace{10mm} \textbf{else if} \: g^i_{prev} g^i_t < 0 \\ &\hspace{15mm} \eta^i_t \leftarrow \mathrm{max}(\eta^i_{t-1} \eta_{-}, \Gamma_{min}) \\ &\hspace{10mm} \textbf{else} \: \\ &\hspace{15mm} \eta^i_t \leftarrow \eta^i_{t-1} \\ &\hspace{5mm}\theta_t \leftarrow \theta_{t-1}- \eta_t \mathrm{sign}(g_t) \\ &\hspace{5mm}g_{prev} \leftarrow g_t \\ &\rule{110mm}{0.4pt} \\[-1.ex] &\bf{return} \: \theta_t \\[-1.ex] &\rule{110mm}{0.4pt} \\[-1.ex] \end{aligned} For further details regarding the algorithm we refer to the paper `A Direct Adaptive Method for Faster Backpropagation Learning: The RPROP Algorithm <>`_. Args: 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. Args: 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: params = [] grads = [] prevs = [] step_sizes = [] for p in group['params']: if p.grad is None: continue params.append(p) grad = p.grad if grad.is_sparse: raise RuntimeError('Rprop does not support sparse gradients') grads.append(grad) 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']) prevs.append(state['prev']) step_sizes.append(state['step_size']) etaminus, etaplus = group['etas'] step_size_min, step_size_max = group['step_sizes'] state['step'] += 1 F.rprop(params, grads, prevs, step_sizes, step_size_min=step_size_min, step_size_max=step_size_max, etaminus=etaminus, etaplus=etaplus) return loss


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