91 lines
3.1 KiB
Python
91 lines
3.1 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 Adadelta(Optimizer):
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"""Implements Adadelta algorithm.
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It has been proposed in `ADADELTA: An Adaptive Learning Rate Method`__.
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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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rho (float, optional): coefficient used for computing a running average
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of squared gradients (default: 0.9)
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eps (float, optional): term added to the denominator to improve
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numerical stability (default: 1e-6)
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lr (float, optional): coefficient that scale delta before it is applied
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to the parameters (default: 1.0)
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weight_decay (float, optional): weight decay (L2 penalty) (default: 0)
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__ https://arxiv.org/abs/1212.5701
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"""
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def __init__(self, params, lr=1.0, rho=0.9, eps=1e-6, weight_decay=0):
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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 <= rho <= 1.0:
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raise ValueError("Invalid rho value: {}".format(rho))
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if not 0.0 <= eps:
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raise ValueError("Invalid epsilon value: {}".format(eps))
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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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defaults = dict(lr=lr, rho=rho, eps=eps, weight_decay=weight_decay)
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super(Adadelta, self).__init__(params, defaults)
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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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square_avgs = []
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acc_deltas = []
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for p in group['params']:
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if p.grad is None:
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continue
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params_with_grad.append(p)
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if p.grad.is_sparse:
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raise RuntimeError('Adadelta does not support sparse gradients')
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grads.append(p.grad)
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state = self.state[p]
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# Lazy state initialization
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if len(state) == 0:
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state['step'] = 0
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state['square_avg'] = torch.zeros_like(p, memory_format=torch.preserve_format)
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state['acc_delta'] = torch.zeros_like(p, memory_format=torch.preserve_format)
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square_avgs.append(state['square_avg'])
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acc_deltas.append(state['acc_delta'])
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lr, rho, eps, weight_decay = group['lr'], group['rho'], group['eps'], group['weight_decay']
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state['step'] += 1
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F.adadelta(params_with_grad,
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grads,
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square_avgs,
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acc_deltas,
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lr,
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rho,
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eps,
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weight_decay)
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return loss
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