Source code for torch.optim.adagrad

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

[docs]class Adagrad(Optimizer): """Implements Adagrad algorithm. It has been proposed in `Adaptive Subgradient Methods for Online Learning and Stochastic Optimization`_. Arguments: 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 state['sum'] = torch.full_like(p, initial_accumulator_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. 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: 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, group['lr'], group['weight_decay'], group['lr_decay'], group['eps']) return loss


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