93 lines
3.4 KiB
Python
93 lines
3.4 KiB
Python
import torch
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from . import _functional as F
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from .optimizer import Optimizer
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class Adagrad(Optimizer):
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"""Implements Adagrad algorithm.
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It has been proposed in `Adaptive Subgradient Methods for Online Learning
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and Stochastic Optimization`_.
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Args:
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params (iterable): iterable of parameters to optimize or dicts defining
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parameter groups
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lr (float, optional): learning rate (default: 1e-2)
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lr_decay (float, optional): learning rate decay (default: 0)
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weight_decay (float, optional): weight decay (L2 penalty) (default: 0)
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eps (float, optional): term added to the denominator to improve
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numerical stability (default: 1e-10)
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.. _Adaptive Subgradient Methods for Online Learning and Stochastic
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Optimization: http://jmlr.org/papers/v12/duchi11a.html
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"""
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def __init__(self, params, lr=1e-2, lr_decay=0, weight_decay=0, initial_accumulator_value=0, eps=1e-10):
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if not 0.0 <= lr:
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raise ValueError("Invalid learning rate: {}".format(lr))
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if not 0.0 <= lr_decay:
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raise ValueError("Invalid lr_decay value: {}".format(lr_decay))
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if not 0.0 <= weight_decay:
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raise ValueError("Invalid weight_decay value: {}".format(weight_decay))
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if not 0.0 <= initial_accumulator_value:
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raise ValueError("Invalid initial_accumulator_value value: {}".format(initial_accumulator_value))
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if not 0.0 <= eps:
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raise ValueError("Invalid epsilon value: {}".format(eps))
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defaults = dict(lr=lr, lr_decay=lr_decay, eps=eps, weight_decay=weight_decay,
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initial_accumulator_value=initial_accumulator_value)
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super(Adagrad, self).__init__(params, defaults)
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for group in self.param_groups:
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for p in group['params']:
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state = self.state[p]
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state['step'] = 0
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state['sum'] = torch.full_like(p, initial_accumulator_value, memory_format=torch.preserve_format)
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def share_memory(self):
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for group in self.param_groups:
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for p in group['params']:
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state = self.state[p]
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state['sum'].share_memory_()
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@torch.no_grad()
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def step(self, closure=None):
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"""Performs a single optimization step.
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Args:
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closure (callable, optional): A closure that reevaluates the model
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and returns the loss.
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"""
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loss = None
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if closure is not None:
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with torch.enable_grad():
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loss = closure()
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for group in self.param_groups:
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params_with_grad = []
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grads = []
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state_sums = []
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state_steps = []
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for p in group['params']:
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if p.grad is not None:
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params_with_grad.append(p)
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grads.append(p.grad)
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state = self.state[p]
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state_sums.append(state['sum'])
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# update the steps for each param group update
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state['step'] += 1
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# record the step after step update
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state_steps.append(state['step'])
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F.adagrad(params_with_grad,
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grads,
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state_sums,
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state_steps,
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group['lr'],
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group['weight_decay'],
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group['lr_decay'],
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group['eps'])
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return loss
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