精华内容
下载资源
问答
  • 使用cmd在pytorch环境下使用命令 pip install git+https://github.com/ildoonet/pytorch-gradual-warmup-lr.git 会报错ERROR: Command errored out with exit status 128: git clone -q ...‘C:\Users\DELL\AppData\...

    使用cmd在pytorch环境下使用命令 pip install git+https://github.com/ildoonet/pytorch-gradual-warmup-lr.git 会报错ERROR: Command errored out with exit status 128: git clone -q https://github.com/ildoonet/pytorch-gradual-warmup-lr.git ‘C:\Users\DELL\AppData\Local\Temp\pip-req-build-w3xw5wgo’ Check the logs for full command output.
    解决办法:在git cmd中执行这个命令。
    在这里插入图片描述

    展开全文
  • Pytorch lr_scheduler 各个函数的用法及可视化参考博客说几个我之前不这么了解的吧,也不怎么用的 参考博客 https://www.pythonheidong.com/blog/article/511529/e49e08939f608d9736fe/ ... 说

    Pytorch lr_scheduler 各个函数的用法及可视化

    参考博客
    1. https://www.pythonheidong.com/blog/article/511529/e49e08939f608d9736fe/
    2. https://blog.csdn.net/xiaotuzigaga/article/details/87879198
    3. https://blog.csdn.net/baoxin1100/article/details/107446538
    说几个我之前不这么了解的吧,也不怎么用的
    • CyclicLR

    在这里插入图片描述
    在这里插入图片描述
    在这里插入图片描述

    这个函数没用过,其实估计效果应该不错,回头试试,这个mode有三种模式。“triangular2"是一个周期就减半,即缩放是0.5;“exp_range”是可以自定义这个每个周期后的缩放比例,通过设置"gamma"参数实现。
    在这里插入图片描述

    import torch
    import matplotlib.pyplot as plt
    lr = 0.001
    epochs = 10
    iters = 4
    cyc_epoch = 1
    # triangular, triangular2, exp_range
    # 三角的形式,0.0001代表最小的学习率, 0.001代表最大的学习率, cyc_epoch*iters代表一个升降周期
    scheduler_cyc0 = torch.optim.lr_scheduler.CyclicLR(torch.optim.SGD([torch.ones(1)], lr), 0.0001, 0.001, cyc_epoch*iters, mode='triangular')
    scheduler_cyc1 = torch.optim.lr_scheduler.CyclicLR(torch.optim.SGD([torch.ones(1)], lr), 0.0001, 0.001, cyc_epoch*iters, mode='triangular2')
    scheduler_cyc2 = torch.optim.lr_scheduler.CyclicLR(torch.optim.SGD([torch.ones(1)], lr), 0.0001, 0.001, cyc_epoch*iters, mode='exp_range', gamma=0.8)                         
    triangular  = []
    triangular2 = []
    exp_range  = []
    for epoch in range(epochs):
        for i in range(iters):
            # print (scheduler_cyc0.get_lr()) #[0.000325]
            triangular += (scheduler_cyc0.get_lr()) # list之间, + 和 append有区别
            triangular2 += scheduler_cyc1.get_lr()
            exp_range  += scheduler_cyc2.get_lr()
            scheduler_cyc0.step()
            scheduler_cyc1.step()
            scheduler_cyc2.step()
    print (triangular)
    print (triangular2)
    print (exp_range)
    x = list(range(len(triangular)))
    plt.figure(figsize=(12,7))
    plt.plot(x, triangular, "r")
    plt.plot(x, triangular2, "g--")
    plt.plot(x, exp_range, "b-.")
    # plt.plot(triangular2)
    plt.legend(['triangular','triangular2','exp_range'], fontsize=20)
    plt.xlabel('iters', size=15)
    plt.ylabel('lr', size=15)
    # plt.savefig("CyclicLR.png")
    plt.show()
    

    这边讲个小细节,以前注意过。

    a ,b = [1], [2]
    a.append(b) # [1, [2]]
    a += [b] # [1, 2]
    
    • CosineAnnealingWarmRestarts && CosineAnnealingWarmRestarts
      在这里插入图片描述
    lr = 0.001
    epochs = 100
    iters = 32
    # T_0代表第一个余弦退火的周期,T_mult代表周期的成长因子,例如:T_0=5, T_mult=5. [5, 5+25, 5+25+50]
    scheduler_cosW = torch.optim.lr_scheduler.CosineAnnealingWarmRestarts(torch.optim.SGD([torch.ones(1)], lr), T_0=500, T_mult=5)
    # lr从initial_lr = max_lr/div_factor在pct_start*total_steps(=epochs*steps_per_epoch)步数里
    # 采用cos(可选还有线性)升到max_lr,
    # 然后用cos(可选还有线性)退火降至min_lr = initial_lr/final_div_factor
    scheduler_onecyc = torch.optim.lr_scheduler.OneCycleLR(torch.optim.SGD([torch.ones(1)], lr), max_lr=lr, steps_per_epoch=iters, epochs=epochs, anneal_strategy='cos')
    scheduler_onecyc_linear = torch.optim.lr_scheduler.OneCycleLR(torch.optim.SGD([torch.ones(1)], lr), max_lr=lr, steps_per_epoch=iters, epochs=epochs, anneal_strategy='linear')
    

    在这里插入图片描述
    从图中可以看出,OneCycleLR才是我们口中常说的warmup吧,而且anneal_strategy这个参数居然没用,都是cos退火。我觉得以后可以直接用OneCycleLR。当然也有一些其他scheduler带上warmup的,自己改下即可。

    import math
    from torch.optim.lr_scheduler import MultiStepLR, _LRScheduler
    
    class WarmupMultiStepLR(MultiStepLR):
        r"""
        # max_iter = epochs * steps_per_epoch
        Args:
            optimizer (Optimizer): Wrapped optimizer.
            max_iter (int): The total number of steps.
            milestones (list) – List of iter indices. Must be increasing.
            gamma (float): Multiplicative factor of learning rate decay. Default: 0.1.
            pct_start (float): The percentage of the cycle (in number of steps) spent
                        increasing the learning rate.
                        Default: 0.3
            warmup_factor (float):         
            last_epoch (int): The index of last epoch. Default: -1.
        """
        def __init__(self, optimizer, max_iter, milestones, gamma=0.1, pct_start=0.3, warmup_factor=1.0 / 2,
                      last_epoch=-1):
            self.warmup_factor = warmup_factor
            self.warmup_iters = int(pct_start * max_iter)
            super().__init__(optimizer, milestones, gamma, last_epoch)
    
        def get_lr(self):
            if self.last_epoch <= self.warmup_iters:
                alpha = self.last_epoch / self.warmup_iters
                warmup_factor = self.warmup_factor * (1 - alpha) + alpha
                return [lr * warmup_factor for lr in self.base_lrs]
            else:
                lr = super().get_lr()
            return lr
    
    class WarmupCosineLR(_LRScheduler):
        def __init__(self, optimizer, max_iter, pct_start=0.3, warmup_factor=1.0 / 3, 
                     eta_min=0, last_epoch=-1):
            self.warmup_factor = warmup_factor
            self.warmup_iters = int(pct_start * max_iter)
            self.max_iter, self.eta_min = max_iter, eta_min
            super().__init__(optimizer)
    
        def get_lr(self):
            if self.last_epoch <= self.warmup_iters:
                alpha = self.last_epoch / self.warmup_iters
                warmup_factor = self.warmup_factor * (1 - alpha) + alpha
                return [lr * warmup_factor for lr in self.base_lrs]
            else:
                # print ("after warmup")
                return [self.eta_min + (base_lr - self.eta_min) *
                        (1 + math.cos(
                            math.pi * (self.last_epoch - self.warmup_iters) / (self.max_iter - self.warmup_iters))) / 2
                        for base_lr in self.base_lrs]
    
    class WarmupPolyLR(_LRScheduler):
        def __init__(self, optimizer, T_max, pct_start=0.3, warmup_factor=1.0 / 4, 
                     eta_min=0, power=0.9):
            self.warmup_factor = warmup_factor
            self.warmup_iters = int(pct_start * T_max)
            self.power = power
            self.T_max, self.eta_min = T_max, eta_min
            super().__init__(optimizer)
    
        def get_lr(self):
            if self.last_epoch <= self.warmup_iters:
                alpha = self.last_epoch / self.warmup_iters
                warmup_factor = self.warmup_factor * (1 - alpha) + alpha
                return [lr * warmup_factor for lr in self.base_lrs]
            else:
                return [self.eta_min + (base_lr - self.eta_min) *
                        math.pow(1 - (self.last_epoch - self.warmup_iters) / (self.T_max - self.warmup_iters),
                                 self.power) for base_lr in self.base_lrs]
    
    if __name__ == '__main__':
    
        import matplotlib.pyplot as plt
        import torch
        import sys
        # sys.setrecursionlimit(12000)
        max_iter = 10000
        lr=5e-4
        optimizer = torch.optim.SGD([torch.ones(1)], lr)
    
        scheduler_WP = WarmupPolyLR(optimizer, T_max=max_iter)
        scheduler_WS = WarmupCosineLR(optimizer, max_iter)
        scheduler_WM = WarmupMultiStepLR(optimizer, max_iter, [5000, 7000, 9000])
    
        lrs_wp = []
        lrs_ws = []
        lrs_wm = []
    
        for cur_iter in range(max_iter):
    
            # lr = optimizer.param_groups[0]['lr']
            # lrs_wp.append(scheduler_WP.get_lr()[0])
    
            lrs_wp += scheduler_WP.get_lr()
            lrs_ws += scheduler_WS.get_lr()
            lrs_wm += scheduler_WM.get_lr()
    
            optimizer.step()
            scheduler_WP.step()
            scheduler_WS.step()
            scheduler_WM.step()
    
        x = list(range(len(lrs_wm)))
        plt.figure(figsize=(12,7))
        plt.plot(x, lrs_wp, "r",
                x, lrs_ws, "g--",
                x, lrs_wm, "b-.")
        # plt.plot(triangular2)
        plt.legend(['WarmupPolyLR','WarmupCosineLR','WarmupMultiStepLR'], fontsize=20)
        plt.xlabel('iters', size=15)
        plt.ylabel('lr', size=15)
        # plt.savefig("CyclicLR.png")
        plt.show()
    

    在这里插入图片描述

    • other
    class MyLRScheduler(object):
        '''
        CLass that defines cyclic learning rate that decays the learning rate linearly till the end of cycle and then restarts
        at the maximum value.
        '''
        def __init__(self, initial=0.1, cycle_len=5, ep_cycle=50, ep_max=100):
            super(MyLRScheduler, self).__init__()
    
            self.min_lr = initial# minimum learning rate
            self.m = cycle_len
            self.ep_cycle = ep_cycle
            self.ep_max = ep_max
            self.poly_start = initial
            self.step = initial/ self.ep_cycle
            print('Using Cyclic LR Scheduler with warm restarts and poly step '
                  + str(self.step))
    
        def get_lr(self, epoch):
            if epoch==0:
                current_lr = self.min_lr
            elif 0< epoch and epoch <= self.ep_cycle:
                counter = (epoch-1) % self.m
                current_lr = round((self.min_lr * self.m) - (counter * self.min_lr), 5)
            else:
    
                current_lr = round(self.poly_start - (epoch-self.ep_cycle )*self.step, 8)
    
                # current_lr = round(self.poly_start * (1 - (epoch-self.ep_cycle) / (self.ep_max-self.ep_cycle)) ** 0.9, 8)
    
            return current_lr
    
    
    class WarmupPoly(object):
        '''
        CLass that defines cyclic learning rate that decays the learning rate linearly till the end of cycle and then restarts
        at the maximum value.
        '''
        def __init__(self, init_lr, total_ep, warmup_ratio=0.05, poly_pow = 0.98):
            super(WarmupPoly, self).__init__()
            self.init_lr = init_lr
            self.total_ep = total_ep
            self.warmup_ep = int(warmup_ratio*total_ep)
            print("warup unitl " + str(self.warmup_ep))
            self.poly_pow = poly_pow
    
        def get_lr(self, epoch):
            #
            if epoch < self.warmup_ep:
                curr_lr =  self.init_lr*pow((((epoch+1) / self.warmup_ep)), self.poly_pow)
    
            else:
                curr_lr = self.init_lr*pow((1 - ((epoch- self.warmup_ep)  / (self.total_ep-self.warmup_ep))), self.poly_pow)
    
            return curr_lr
            
    if __name__ == '__main__':
        import matplotlib.pyplot as plt
        max_epochs = 300
        lrSched = MyLRScheduler(initial=0.0001, cycle_len=10, ep_cycle=150, ep_max=300)
        lrSched1 = WarmupPoly(1e-3, max_epochs , poly_pow=0.95)
    
        x = []
        y = []
        y1 = []
        for i in range(max_epochs):
            x.append(i)
            y.append(lrSched.get_lr(i))
            y1.append(lrSched1.get_lr(i))
        print (y[0], y[-1]) # 0.0001 6.7e-07
        print (y1[0], y1[-1]) # 7.633317097623927e-05 4.654760957913004e-06
        plt.figure(figsize=(12,7))
        plt.plot(x, y, "r",
                x, y1, "b-.")
        plt.legend(['cyclic','WarmupPoly',], fontsize=20)
        plt.xlabel('iters', size=15)
        plt.ylabel('lr', size=15)
        plt.savefig("MyLRScheduler.png")
        plt.show()
    
    

    在这里插入图片描述

    • 还发现Pytorch doc optim部分最后出现了SWA,以后实际任务跑一下,试试。
    展开全文
  • pytorch中Schedule与warmup_steps的用法

    千次阅读 2020-02-27 16:38:27
    lr_scheduler = WarmupLinearSchedule(optimizer, warmup_steps=args.warmup_steps, t_total=num_train_optimization_steps) 其中args.warmup_steps可以认为是耐心系数 num_train_optimization_steps为模型参数的总...

    1. lr_scheduler相关

    lr_scheduler = WarmupLinearSchedule(optimizer, warmup_steps=args.warmup_steps, t_total=num_train_optimization_steps)
    

    其中args.warmup_steps可以认为是耐心系数
    num_train_optimization_steps为模型参数的总更新次数
    一般来说:

        num_train_optimization_steps = int(total_train_examples / args.train_batch_size / args.gradient_accumulation_steps)
    
    

    Schedule用来调节学习率,拿线性变换调整来说,下面代码中,step是当前迭代次数。

        def lr_lambda(self, step):
            # 线性变换,返回的是某个数值x,然后返回到类LambdaLR中,最终返回old_lr*x
            if step < self.warmup_steps: # 增大学习率
                return float(step) / float(max(1, self.warmup_steps))
            # 减小学习率
            return max(0.0, float(self.t_total - step) / float(max(1.0, self.t_total - self.warmup_steps)))
    
    

    在实际运行中,lr_scheduler.step()先将lr初始化为0. 在第一次参数更新时,此时step=1,lr由0变为初始值initial_lr;在第二次更新时,step=2,上面代码中生成某个实数alpha,新的lr=initial_lr *alpha;在第三次更新时,新的lr是在initial_lr基础上生成,即新的lr=initial_lr *alpha。其中warmup_steps可以认为是lr调整的耐心系数。
    由于有warmup_steps存在,lr先慢慢增加,超过warmup_steps时,lr再慢慢减小。在实际中,由于训练刚开始时,训练数据计算出的grad可能与期望方向相反,所以此时采用较小的lr,随着迭代次数增加,lr线性增大,增长率为1/warmup_steps;迭代次数等于warmup_steps时,学习率为初始设定的学习率;迭代次数超过warmup_steps时,学习率逐步衰减,衰减率为1/(total-warmup_steps),再进行微调。

    2. gradient_accumulation_steps相关

    gradient_accumulation_steps通过累计梯度来解决本地显存不足问题。
    假设原来的batch_size=6,样本总量为24,gradient_accumulation_steps=2
    那么参数更新次数=24/6=4
    现在,减小batch_size=6/2=3,参数更新次数不变=24/3/2=4
    在梯度反传时,每gradient_accumulation_steps次进行一次梯度更新,之前照常利用loss.backward()计算梯度。

    展开全文
  • Pytorch 实现学习率控制(WarmUp)

    千次阅读 2021-01-02 18:41:54
    from torch.optim.lr_scheduler import StepLR, ExponentialLR from torch.optim.sgd import SGD from torch.optim.lr_scheduler import _LRScheduler from torch.optim.lr_scheduler import ReduceLROnPlateau ...
    import torch
    from torch.optim.lr_scheduler import StepLR, ExponentialLR
    from torch.optim.sgd import SGD
    from torch.optim.lr_scheduler import _LRScheduler
    from torch.optim.lr_scheduler import ReduceLROnPlateau
    
    
    class GradualWarmupScheduler(_LRScheduler):
        """ Gradually warm-up(increasing) learning rate in optimizer.
        Proposed in 'Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour'.
    
        Args:
            optimizer (Optimizer): Wrapped optimizer.
            multiplier: target learning rate = base lr * multiplier if multiplier > 1.0. if multiplier = 1.0, lr starts from 0 and ends up with the base_lr.
            total_epoch: target learning rate is reached at total_epoch, gradually
            after_scheduler: after target_epoch, use this scheduler(eg. ReduceLROnPlateau)
        """
    
        def __init__(self, optimizer, multiplier, total_epoch, after_scheduler=None):
            self.multiplier = multiplier
            if self.multiplier < 1.:
                raise ValueError('multiplier should be greater thant or equal to 1.')
            self.total_epoch = total_epoch
            self.after_scheduler = after_scheduler
            self.finished = False
            super(GradualWarmupScheduler, self).__init__(optimizer)
    
        def get_lr(self):
            if self.last_epoch > self.total_epoch:
                if self.after_scheduler:
                    if not self.finished:
                        self.after_scheduler.base_lrs = [base_lr * self.multiplier for base_lr in self.base_lrs]
                        self.finished = True
                    return self.after_scheduler.get_last_lr()
                return [base_lr * self.multiplier for base_lr in self.base_lrs]
    
            if self.multiplier == 1.0:
                return [base_lr * (float(self.last_epoch) / self.total_epoch) for base_lr in self.base_lrs]
            else:
                return [base_lr * ((self.multiplier - 1.) * self.last_epoch / self.total_epoch + 1.) for base_lr in self.base_lrs]
    
        def step_ReduceLROnPlateau(self, metrics, epoch=None):
            if epoch is None:
                epoch = self.last_epoch + 1
            self.last_epoch = epoch if epoch != 0 else 1  # ReduceLROnPlateau is called at the end of epoch, whereas others are called at beginning
            if self.last_epoch <= self.total_epoch:
                warmup_lr = [base_lr * ((self.multiplier - 1.) * self.last_epoch / self.total_epoch + 1.) for base_lr in self.base_lrs]
                for param_group, lr in zip(self.optimizer.param_groups, warmup_lr):
                    param_group['lr'] = lr
            else:
                if epoch is None:
                    self.after_scheduler.step(metrics, None)
                else:
                    self.after_scheduler.step(metrics, epoch - self.total_epoch)
    
        def step(self, epoch=None, metrics=None):
            if type(self.after_scheduler) != ReduceLROnPlateau:
                if self.finished and self.after_scheduler:
                    if epoch is None:
                        self.after_scheduler.step(None)
                    else:
                        self.after_scheduler.step(epoch - self.total_epoch)
                    self._last_lr = self.after_scheduler.get_last_lr()
                else:
                    return super(GradualWarmupScheduler, self).step(epoch)
            else:
                self.step_ReduceLROnPlateau(metrics, epoch)
    
    
    if __name__ == '__main__':
        model = [torch.nn.Parameter(torch.randn(2, 2, requires_grad=True))]
        optim = SGD(model, 0.1)
    
        # scheduler_warmup is chained with schduler_steplr
        scheduler_steplr = StepLR(optim, step_size=10, gamma=0.1)
        scheduler_warmup = GradualWarmupScheduler(optim, multiplier=1, total_epoch=5, after_scheduler=scheduler_steplr)
    
        # this zero gradient update is needed to avoid a warning message, issue #8.
        optim.zero_grad()
        optim.step()
    
        for epoch in range(1, 20):
            scheduler_warmup.step(epoch)
            print(epoch, optim.param_groups[0]['lr'])
    
            optim.step()    # backward pass (update network)

     

    展开全文
  • 【Trick】调优方法warmup

    千次阅读 2020-07-29 21:08:30
    学习率是模型训练中最重要的超参之一,针对学习率的优化有很多种方法,而warmup是其中重要的一种。 先提供github库链接:https://github.com/ildoonet/pytorch-gradual-warmup-lr 什么是warmup warmup是一种学习率...
  • / 1000 warmup_iters = min(1000, len(train_loader) - 1) lr_scheduler = warmup_lr_scheduler(optimizer, warmup_iters, warmup_factor) for i in range(1,100): lr_scheduler.step() #将学习率迭代次数+1传给f ...
  • Gradual warmup lr schedule--pytorch

    千次阅读 2019-06-21 14:26:16
    Gradually warm-up(increasing) learning rate for pytorch’s optimizer. Proposed in ‘Accurate, Large Minibatch SGD: Training ImageNet in 1 ...# from:https://github.com/ildoonet/pytorch-gradual-warmup...
  • PyTorch 在torch.optim.lr_scheduler中提供了十种调整学习率的类,它们的基类为torch.optim.lr_scheduler._LRScheduler。 这里记常用的一些。详尽的参考PyTorch 官方文档。 1. StepLR StepLR类,按固定间隔调整...
  • 参考链接: class torch.optim.lr_scheduler.CosineAnnealingWarmRestarts(optimizer, T_0, T_mult=1, eta_min=0, last_epoch=-1, verbose=False)
  • 关于warm_up学习率

    千次阅读 2019-07-25 15:21:03
    关于warm-up:tf的models里面提到warm-upw为5epoch,所以上面计算的...warmup: Run a 5 epoch warmup to the initial lr. 1 warm_up核心代码 learning_rate = cfg.learning_rate boundaries = cfg.lr_steps # _...
  • https://blog.csdn.net/baoxin1100/article/details/107446538
  • warmup lr+CosineAnnealingLR策略

    千次阅读 2020-11-19 15:03:39
    warmup lr策略就是在网络训练初期用比较小的学习率,线性增长到初始设定的学习率。 大概就是下面这个趋势,从0上升到0.01,再按照正常的学习率调整... """warmup_training learning rate scheduler Args: optimiz
  • pytorch 训练学习率warmup

    千次阅读 2020-08-25 16:04:36
    class WarmupMultiStepLR(torch.optim.lr_scheduler._LRScheduler): def __init__( self, optimizer, milestones, ... warmup_factor=1/3, warmup_iters=100, warmup_method="linear", last_epo.
  • 1. 什么是warmup warmup是针对学习率learning rate优化的一种策略,主要过程是,在预热期间,学习率从0线性(也可非线性)增加到优化器中的初始预设lr,之后使其学习率从优化器中的初始lr线性降低到0,如下图所示: ...
  • GradualWarmupScheduler 学习率预热策略解读 解读:GradualWarmupScheduler 学习率调整策略(完整代码见最后) 本文为作者原创文章,如果转载请事先征得作者同意并注明引用地址: ... 作者能力有限,如有错误还请多多...
  • pytorch中调整学习率: torch.optim.lr_scheduler

    万次阅读 多人点赞 2018-10-20 16:15:35
    torch.optim.lr_scheduler 中提供了基于多种epoch数目调整学习率的方法. torch.optim.lr_scheduler.ReduceLROnPlateau :允许基于一些验证测量对学习率进行动态的下降 -------------------------------...
  • 1. 为什么需要调整学习率 在深度学习训练过程中,最重要的参数就是学习率,通常来说,在整个训练过层中,学习率不会一直保持不变,为了让模型能够在训练初期快速收敛,学习率通常比较大,在训练末期,为了让模型收敛...
  • The mxnet.lr_scheduler package The base class LRScheduler defines the interface, while other classes implement various schemes to change the learning rate during training. LRScheduler Base class of a ...
  • 目录一、warmup定义二、为什么使用warmup2.1、理性分析2.2、感性分析三、常用的warmup3.1、Constant Warmup3.2、Linner Warmup3.3、Cosine Warmup3.4、Warmup的改进:gradual warmup四、PyTorch实现总结Reference ...
  • 模型训练技巧——warm up

    千次阅读 热门讨论 2020-05-09 15:45:42
    在训练模型时,先给模型预热(warm up),会使模型最终收敛得更好,PyTorch学习率调整策略通过torch.optim.lr_scheduler接口实现。本文介绍模型在训练时,学习率的调节技巧。
  • import numpy as np from tensorflow import keras from keras import backend as K # 带有warm-up的cosine学习率 ...def cosine_decay_with_warmup(global_step, learning_rate_base, total_steps, .
  • 深度学习训练策略-学习率预热Warmup

    千次阅读 2020-04-05 17:06:20
    一、什么是Warmup? Warmup是在ResNet论文中提到的一种学习率预热的方法,它在训练开始的时候先选择使用一个较小的学习率,训练了一些steps(15000steps,见代码1)或者epoches(5epoches,见代码2),再修改为预先设置...
  • 学习率是神经网络训练中最重要的超参数之一,针对学习率的优化方式很多,Warmup是其中的一种 (一)、什么是Warmup? Warmup是在ResNet论文中提到的一种学习率预热的方法,它在训练开始的时候先选择使用一个较小的学习率...
  • 图像分类任务中的tricks总结

    千次阅读 2019-10-22 09:39:00
    上述的方法是constant warmup,18年Facebook又针对上面的warmup进行了改进[3],因为从一个很小的学习率一下变为比较大的学习率可能会导致训练误差突然增大。论文[3]提出了gradual warmup来解决这个问题,即从最开始...
  • pytorch之warm-up预热学习策略

    千次阅读 2021-05-27 10:09:58
    学习率是神经网络训练中最重要的超参数之一,针对学习率的优化方式很多,Warmup是其中的一种 1、什么是Warmup Warmup是在ResNet论文中提到的一种学习率预热的方法,它在训练开始的时候先选择使用一个较小的学习率,...
  • classes=2) optimizer = torch.optim.SGD(model.parameters(), lr=0.01, momentum=0.9, nesterov=False) scheduler = optim.lr_scheduler.CyclicLR(optimizer, base_lr=0.01, max_lr=0.0001, step_size_up=2000,...
  • Pytorch:几行代码轻松实现Warm up + Cosine Anneal LR

    千次阅读 热门讨论 2021-02-23 22:40:46
    Warm up 浅谈 warm up是深度学习炼丹时常用的一种手段,由于一开始参数不稳定,梯度较大,如果此时学习率设置过大可能导致数值不稳定。使用warm up有助于减缓模型在初始阶段对mini-batch的提前过拟合现象,保持分布...
  • 什么是warmup 学习率的设置 — 不同阶段不同值:上升 -> 平稳 -> 下降 由于神经网络在刚开始训练的时候是非常不稳定的,因此刚开始的学习率应当设置得很低很低,这样可以保证网络能够具有良好的收敛性。但是较...

空空如也

空空如也

1 2 3 4 5 ... 20
收藏数 1,381
精华内容 552
关键字:

warmup_scheduler