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Source code for torch.optim.adagrad

import torch
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: http://jmlr.org/papers/v12/duchi11a.html """ 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: for p in group['params']: if p.grad is None: continue grad = p.grad state = self.state[p] state['step'] += 1 if group['weight_decay'] != 0: if p.grad.is_sparse: raise RuntimeError("weight_decay option is not compatible with sparse gradients") grad = grad.add(p, alpha=group['weight_decay']) clr = group['lr'] / (1 + (state['step'] - 1) * group['lr_decay']) if grad.is_sparse: grad = grad.coalesce() # the update is non-linear so indices must be unique grad_indices = grad._indices() grad_values = grad._values() size = grad.size() def make_sparse(values): constructor = grad.new if grad_indices.dim() == 0 or values.dim() == 0: return constructor().resize_as_(grad) return constructor(grad_indices, values, size) state['sum'].add_(make_sparse(grad_values.pow(2))) std = state['sum'].sparse_mask(grad) std_values = std._values().sqrt_().add_(group['eps']) p.add_(make_sparse(grad_values / std_values), alpha=-clr) else: state['sum'].addcmul_(grad, grad, value=1) std = state['sum'].sqrt().add_(group['eps']) p.addcdiv_(grad, std, value=-clr) return loss

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