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75 lines
2.7 KiB
Python
75 lines
2.7 KiB
Python
import math
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from .optimizer import Optimizer
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class Adam(Optimizer):
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"""Implements Adam algorithm.
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It has been proposed in `Adam: A Method for Stochastic Optimization`_.
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Arguments:
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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-3)
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betas (Tuple[float, float], optional): coefficients used for computing
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running averages of gradient and its square (default: (0.9, 0.999))
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eps (float, optional): term added to the denominator to improve
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numerical stability (default: 1e-8)
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weight_decay (float, optional): weight decay (L2 penalty) (default: 0)
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.. _Adam\: A Method for Stochastic Optimization:
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https://arxiv.org/abs/1412.6980
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"""
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def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8,
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weight_decay=0):
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defaults = dict(lr=lr, betas=betas, eps=eps,
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weight_decay=weight_decay)
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super(Adam, self).__init__(params, defaults)
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def step(self, closure=None):
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"""Performs a single optimization step.
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Arguments:
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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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loss = closure()
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for group in self.param_groups:
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for p in group['params']:
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grad = p.grad.data
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state = self.state[p]
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# State initialization
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if len(state) == 0:
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state['step'] = 0
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# Exponential moving average of gradient values
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state['exp_avg'] = grad.new().resize_as_(grad).zero_()
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# Exponential moving average of squared gradient values
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state['exp_avg_sq'] = grad.new().resize_as_(grad).zero_()
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exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq']
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beta1, beta2 = group['betas']
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state['step'] += 1
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if group['weight_decay'] != 0:
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grad = grad.add(group['weight_decay'], p.data)
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# Decay the first and second moment running average coefficient
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exp_avg.mul_(beta1).add_(1 - beta1, grad)
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exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad)
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denom = exp_avg_sq.sqrt().add_(group['eps'])
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bias_correction1 = 1 - beta1 ** state['step']
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bias_correction2 = 1 - beta2 ** state['step']
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step_size = group['lr'] * math.sqrt(bias_correction2) / bias_correction1
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p.data.addcdiv_(-step_size, exp_avg, denom)
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return loss
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