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# 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
<http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.21.1417>_.

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)

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:
loss = closure()

for group in self.param_groups:
params = []
prevs = []
step_sizes = []

for p in group['params']:
continue
params.append(p)
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)

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,
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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