/usr/local/lib64/python3.6/site-packages/torch/optim
Edit: /usr/local/lib64/python3.6/site-packages/torch/optim/rprop.py (5410B)
import torch
from . import _functional as F
from .optimizer import Optimizer
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)
@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'] = grad.new().resize_as_(grad).fill_(group['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