Source code for torch.optim.adagrad

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

[docs]class Adagrad(Optimizer): r"""Implements Adagrad algorithm. .. math:: \begin{aligned} &\rule{110mm}{0.4pt} \\ &\textbf{input} : \gamma \text{ (lr)}, \: \theta_0 \text{ (params)}, \: f(\theta) \text{ (objective)}, \: \lambda \text{ (weight decay)}, \\ &\hspace{12mm} \tau \text{ (initial accumulator value)}, \: \eta\text{ (lr decay)}\\ &\textbf{initialize} : state\_sum_0 \leftarrow 0 \\[-1.ex] &\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} \tilde{\gamma} \leftarrow \gamma / (1 +(t-1) \eta) \\ &\hspace{5mm} \textbf{if} \: \lambda \neq 0 \\ &\hspace{10mm} g_t \leftarrow g_t + \lambda \theta_{t-1} \\ &\hspace{5mm}state\_sum_t \leftarrow state\_sum_{t-1} + g^2_t \\ &\hspace{5mm}\theta_t \leftarrow \theta_{t-1}- \tilde{\gamma} \frac{g_t}{\sqrt{state\_sum_t}+\epsilon} \\ &\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 `Adaptive Subgradient Methods for Online Learning and Stochastic Optimization`_. Args: params (iterable): iterable of parameters to optimize or dicts defining parameter groups lr (float, optional): learning rate (default: 1e-2) lr_decay (float, optional): learning rate decay (default: 0) weight_decay (float, optional): weight decay (L2 penalty) (default: 0) eps (float, optional): term added to the denominator to improve numerical stability (default: 1e-10) .. _Adaptive Subgradient Methods for Online Learning and Stochastic Optimization: """ def __init__(self, params, lr=1e-2, lr_decay=0, weight_decay=0, initial_accumulator_value=0, eps=1e-10): if not 0.0 <= lr: raise ValueError("Invalid learning rate: {}".format(lr)) if not 0.0 <= lr_decay: raise ValueError("Invalid lr_decay value: {}".format(lr_decay)) if not 0.0 <= weight_decay: raise ValueError("Invalid weight_decay value: {}".format(weight_decay)) if not 0.0 <= initial_accumulator_value: raise ValueError("Invalid initial_accumulator_value value: {}".format(initial_accumulator_value)) if not 0.0 <= eps: raise ValueError("Invalid epsilon value: {}".format(eps)) defaults = dict(lr=lr, lr_decay=lr_decay, eps=eps, weight_decay=weight_decay, initial_accumulator_value=initial_accumulator_value) super(Adagrad, self).__init__(params, defaults) for group in self.param_groups: for p in group['params']: state = self.state[p] state['step'] = 0 init_value = complex(initial_accumulator_value, initial_accumulator_value) if torch.is_complex(p) \ else initial_accumulator_value state['sum'] = torch.full_like(p, init_value, memory_format=torch.preserve_format) def share_memory(self): for group in self.param_groups: for p in group['params']: state = self.state[p] state['sum'].share_memory_()
[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_with_grad = [] grads = [] state_sums = [] state_steps = [] for p in group['params']: if p.grad is not None: params_with_grad.append(p) grads.append(p.grad) state = self.state[p] state_sums.append(state['sum']) # update the steps for each param group update state['step'] += 1 # record the step after step update state_steps.append(state['step']) F.adagrad(params_with_grad, grads, state_sums, state_steps, lr=group['lr'], weight_decay=group['weight_decay'], lr_decay=group['lr_decay'], eps=group['eps']) return loss


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