3517 lines
138 KiB
Plaintext
3517 lines
138 KiB
Plaintext
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"# AdaGrad算法\n",
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":label:`sec_adagrad`\n",
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"\n",
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"我们从有关特征学习中并不常见的问题入手。\n",
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"\n",
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"## 稀疏特征和学习率\n",
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"\n",
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"假设我们正在训练一个语言模型。\n",
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"为了获得良好的准确性，我们大多希望在训练的过程中降低学习率，速度通常为$\\mathcal{O}(t^{-\\frac{1}{2}})$或更低。\n",
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"现在讨论关于稀疏特征（即只在偶尔出现的特征）的模型训练，这对自然语言来说很常见。\n",
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"例如，我们看到“预先条件”这个词比“学习”这个词的可能性要小得多。\n",
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"但是，它在计算广告学和个性化协同过滤等其他领域也很常见。\n",
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"\n",
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"只有在这些不常见的特征出现时，与其相关的参数才会得到有意义的更新。\n",
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"鉴于学习率下降，我们可能最终会面临这样的情况：常见特征的参数相当迅速地收敛到最佳值，而对于不常见的特征，我们仍缺乏足够的观测以确定其最佳值。\n",
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"换句话说，学习率要么对于常见特征而言降低太慢，要么对于不常见特征而言降低太快。\n",
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"\n",
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"解决此问题的一个方法是记录我们看到特定特征的次数，然后将其用作调整学习率。\n",
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"即我们可以使用大小为$\\eta_i = \\frac{\\eta_0}{\\sqrt{s(i, t) + c}}$的学习率，而不是$\\eta = \\frac{\\eta_0}{\\sqrt{t + c}}$。\n",
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"在这里$s(i, t)$计下了我们截至$t$时观察到功能$i$的次数。\n",
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"这其实很容易实施且不产生额外损耗。\n",
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"\n",
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"AdaGrad算法 :cite:`Duchi.Hazan.Singer.2011`通过将粗略的计数器$s(i, t)$替换为先前观察所得梯度的平方之和来解决这个问题。\n",
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"它使用$s(i, t+1) = s(i, t) + \\left(\\partial_i f(\\mathbf{x})\\right)^2$来调整学习率。\n",
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"这有两个好处：首先，我们不再需要决定梯度何时算足够大。\n",
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"其次，它会随梯度的大小自动变化。通常对应于较大梯度的坐标会显著缩小，而其他梯度较小的坐标则会得到更平滑的处理。\n",
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"在实际应用中，它促成了计算广告学及其相关问题中非常有效的优化程序。\n",
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"但是，它遮盖了AdaGrad固有的一些额外优势，这些优势在预处理环境中很容易被理解。\n",
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"\n",
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"## 预处理\n",
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"\n",
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"凸优化问题有助于分析算法的特点。\n",
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"毕竟对大多数非凸问题来说，获得有意义的理论保证很难，但是直觉和洞察往往会延续。\n",
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"让我们来看看最小化$f(\\mathbf{x}) = \\frac{1}{2} \\mathbf{x}^\\top \\mathbf{Q} \\mathbf{x} + \\mathbf{c}^\\top \\mathbf{x} + b$这一问题。\n",
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"\n",
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"正如在 :numref:`sec_momentum`中那样，我们可以根据其特征分解$\\mathbf{Q} = \\mathbf{U}^\\top \\boldsymbol{\\Lambda} \\mathbf{U}$重写这个问题，来得到一个简化得多的问题，使每个坐标都可以单独解出：\n",
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"\n",
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"$$f(\\mathbf{x}) = \\bar{f}(\\bar{\\mathbf{x}}) = \\frac{1}{2} \\bar{\\mathbf{x}}^\\top \\boldsymbol{\\Lambda} \\bar{\\mathbf{x}} + \\bar{\\mathbf{c}}^\\top \\bar{\\mathbf{x}} + b.$$\n",
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"\n",
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"在这里我们使用了$\\mathbf{x} = \\mathbf{U} \\mathbf{x}$，且因此$\\mathbf{c} = \\mathbf{U} \\mathbf{c}$。\n",
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"修改后优化器为$\\bar{\\mathbf{x}} = -\\boldsymbol{\\Lambda}^{-1} \\bar{\\mathbf{c}}$且最小值为$-\\frac{1}{2} \\bar{\\mathbf{c}}^\\top \\boldsymbol{\\Lambda}^{-1} \\bar{\\mathbf{c}} + b$。\n",
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"这样更容易计算，因为$\\boldsymbol{\\Lambda}$是一个包含$\\mathbf{Q}$特征值的对角矩阵。\n",
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"\n",
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"如果稍微扰动$\\mathbf{c}$，我们会期望在$f$的最小化器中只产生微小的变化。\n",
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"遗憾的是，情况并非如此。\n",
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"虽然$\\mathbf{c}$的微小变化导致了$\\bar{\\mathbf{c}}$同样的微小变化，但$f$的（以及$\\bar{f}$的）最小化器并非如此。\n",
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"每当特征值$\\boldsymbol{\\Lambda}_i$很大时，我们只会看到$\\bar{x}_i$和$\\bar{f}$的最小值发声微小变化。\n",
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"相反，对小的$\\boldsymbol{\\Lambda}_i$来说，$\\bar{x}_i$的变化可能是剧烈的。\n",
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"最大和最小的特征值之比称为优化问题的*条件数*（condition number）。\n",
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"\n",
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"$$\\kappa = \\frac{\\boldsymbol{\\Lambda}_1}{\\boldsymbol{\\Lambda}_d}.$$\n",
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"\n",
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"如果条件编号$\\kappa$很大，准确解决优化问题就会很难。\n",
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"我们需要确保在获取大量动态的特征值范围时足够谨慎：难道我们不能简单地通过扭曲空间来“修复”这个问题，从而使所有特征值都是$1$？\n",
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"理论上这很容易：我们只需要$\\mathbf{Q}$的特征值和特征向量即可将问题从$\\mathbf{x}$整理到$\\mathbf{z} := \\boldsymbol{\\Lambda}^{\\frac{1}{2}} \\mathbf{U} \\mathbf{x}$中的一个。\n",
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"在新的坐标系中，$\\mathbf{x}^\\top \\mathbf{Q} \\mathbf{x}$可以被简化为$\\|\\mathbf{z}\\|^2$。\n",
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"可惜，这是一个相当不切实际的想法。\n",
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"一般而言，计算特征值和特征向量要比解决实际问题“贵”得多。\n",
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"\n",
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"虽然准确计算特征值可能会很昂贵，但即便只是大致猜测并计算它们，也可能已经比不做任何事情好得多。\n",
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"特别是，我们可以使用$\\mathbf{Q}$的对角线条目并相应地重新缩放它。\n",
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"这比计算特征值开销小的多。\n",
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"\n",
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"$$\\tilde{\\mathbf{Q}} = \\mathrm{diag}^{-\\frac{1}{2}}(\\mathbf{Q}) \\mathbf{Q} \\mathrm{diag}^{-\\frac{1}{2}}(\\mathbf{Q}).$$\n",
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"\n",
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"在这种情况下，我们得到了$\\tilde{\\mathbf{Q}}_{ij} = \\mathbf{Q}_{ij} / \\sqrt{\\mathbf{Q}_{ii} \\mathbf{Q}_{jj}}$，特别注意对于所有$i$，$\\tilde{\\mathbf{Q}}_{ii} = 1$。\n",
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"在大多数情况下，这大大简化了条件数。\n",
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"例如我们之前讨论的案例，它将完全消除眼下的问题，因为问题是轴对齐的。\n",
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"\n",
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"遗憾的是，我们还面临另一个问题：在深度学习中，我们通常情况甚至无法计算目标函数的二阶导数：对于$\\mathbf{x} \\in \\mathbb{R}^d$，即使只在小批量上，二阶导数可能也需要$\\mathcal{O}(d^2)$空间来计算，导致几乎不可行。\n",
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"AdaGrad算法巧妙的思路是，使用一个代理来表示黑塞矩阵（Hessian）的对角线，既相对易于计算又高效。\n",
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"\n",
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"为了了解它是如何生效的，让我们来看看$\\bar{f}(\\bar{\\mathbf{x}})$。\n",
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"我们有\n",
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"\n",
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"$$\\partial_{\\bar{\\mathbf{x}}} \\bar{f}(\\bar{\\mathbf{x}}) = \\boldsymbol{\\Lambda} \\bar{\\mathbf{x}} + \\bar{\\mathbf{c}} = \\boldsymbol{\\Lambda} \\left(\\bar{\\mathbf{x}} - \\bar{\\mathbf{x}}_0\\right),$$\n",
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"\n",
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"其中$\\bar{\\mathbf{x}}_0$是$\\bar{f}$的优化器。\n",
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"因此，梯度的大小取决于$\\boldsymbol{\\Lambda}$和与最佳值的差值。\n",
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"如果$\\bar{\\mathbf{x}} - \\bar{\\mathbf{x}}_0$没有改变，那这就是我们所求的。\n",
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"毕竟在这种情况下，梯度$\\partial_{\\bar{\\mathbf{x}}} \\bar{f}(\\bar{\\mathbf{x}})$的大小就足够了。\n",
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"由于AdaGrad算法是一种随机梯度下降算法，所以即使是在最佳值中，我们也会看到具有非零方差的梯度。\n",
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"因此，我们可以放心地使用梯度的方差作为黑塞矩阵比例的廉价替代。\n",
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"详尽的分析（要花几页解释）超出了本节的范围，请读者参考 :cite:`Duchi.Hazan.Singer.2011`。\n",
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"\n",
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"## 算法\n",
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"\n",
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"让我们接着上面正式开始讨论。\n",
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"我们使用变量$\\mathbf{s}_t$来累加过去的梯度方差，如下所示：\n",
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"\n",
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"$$\\begin{aligned}\n",
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" \\mathbf{g}_t & = \\partial_{\\mathbf{w}} l(y_t, f(\\mathbf{x}_t, \\mathbf{w})), \\\\\n",
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" \\mathbf{s}_t & = \\mathbf{s}_{t-1} + \\mathbf{g}_t^2, \\\\\n",
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" \\mathbf{w}_t & = \\mathbf{w}_{t-1} - \\frac{\\eta}{\\sqrt{\\mathbf{s}_t + \\epsilon}} \\cdot \\mathbf{g}_t.\n",
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"\\end{aligned}$$\n",
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"\n",
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"在这里，操作是按照坐标顺序应用。\n",
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"也就是说，$\\mathbf{v}^2$有条目$v_i^2$。\n",
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"同样，$\\frac{1}{\\sqrt{v}}$有条目$\\frac{1}{\\sqrt{v_i}}$，\n",
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"并且$\\mathbf{u} \\cdot \\mathbf{v}$有条目$u_i v_i$。\n",
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"与之前一样，$\\eta$是学习率，$\\epsilon$是一个为维持数值稳定性而添加的常数，用来确保我们不会除以$0$。\n",
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"最后，我们初始化$\\mathbf{s}_0 = \\mathbf{0}$。\n",
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"\n",
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"就像在动量法中我们需要跟踪一个辅助变量一样，在AdaGrad算法中，我们允许每个坐标有单独的学习率。\n",
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"与SGD算法相比，这并没有明显增加AdaGrad的计算代价，因为主要计算用在$l(y_t, f(\\mathbf{x}_t, \\mathbf{w}))$及其导数。\n",
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"\n",
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"请注意，在$\\mathbf{s}_t$中累加平方梯度意味着$\\mathbf{s}_t$基本上以线性速率增长（由于梯度从最初开始衰减，实际上比线性慢一些）。\n",
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"这产生了一个学习率$\\mathcal{O}(t^{-\\frac{1}{2}})$，但是在单个坐标的层面上进行了调整。\n",
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"对于凸问题，这完全足够了。\n",
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"然而，在深度学习中，我们可能希望更慢地降低学习率。\n",
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"这引出了许多AdaGrad算法的变体，我们将在后续章节中讨论它们。\n",
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"眼下让我们先看看它在二次凸问题中的表现如何。\n",
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"我们仍然以同一函数为例：\n",
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"\n",
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"$$f(\\mathbf{x}) = 0.1 x_1^2 + 2 x_2^2.$$\n",
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"\n",
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"我们将使用与之前相同的学习率来实现AdaGrad算法，即$\\eta = 0.4$。\n",
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"可以看到，自变量的迭代轨迹较平滑。\n",
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"但由于$\\boldsymbol{s}_t$的累加效果使学习率不断衰减，自变量在迭代后期的移动幅度较小。\n"
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"%matplotlib inline\n",
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"import math\n",
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"import torch\n",
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"from d2l import torch as d2l"
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"## 从零开始实现\n",
|
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"\n",
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},
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|
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"def init_adagrad_states(feature_dim):\n",
|
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" s_w = torch.zeros((feature_dim, 1))\n",
|
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" s_b = torch.zeros(1)\n",
|
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" return (s_w, s_b)\n",
|
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"\n",
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"def adagrad(params, states, hyperparams):\n",
|
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" eps = 1e-6\n",
|
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" for p, s in zip(params, states):\n",
|
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" with torch.no_grad():\n",
|
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" s[:] += torch.square(p.grad)\n",
|
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" p[:] -= hyperparams['lr'] * p.grad / torch.sqrt(s + eps)\n",
|
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" p.grad.data.zero_()"
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]
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"source": [
|
||
"与 :numref:`sec_minibatch_sgd`一节中的实验相比，这里使用更大的学习率来训练模型。\n"
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]
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},
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{
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|
||
"## 简洁实现\n",
|
||
"\n",
|
||
"我们可直接使用深度学习框架中提供的AdaGrad算法来训练模型。\n"
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|
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"## 小结\n",
|
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"\n",
|
||
"* AdaGrad算法会在单个坐标层面动态降低学习率。\n",
|
||
"* AdaGrad算法利用梯度的大小作为调整进度速率的手段：用较小的学习率来补偿带有较大梯度的坐标。\n",
|
||
"* 在深度学习问题中，由于内存和计算限制，计算准确的二阶导数通常是不可行的。梯度可以作为一个有效的代理。\n",
|
||
"* 如果优化问题的结构相当不均匀，AdaGrad算法可以帮助缓解扭曲。\n",
|
||
"* AdaGrad算法对于稀疏特征特别有效，在此情况下由于不常出现的问题，学习率需要更慢地降低。\n",
|
||
"* 在深度学习问题上，AdaGrad算法有时在降低学习率方面可能过于剧烈。我们将在 :numref:`sec_adam`一节讨论缓解这种情况的策略。\n",
|
||
"\n",
|
||
"## 练习\n",
|
||
"\n",
|
||
"1. 证明对于正交矩阵$\\mathbf{U}$和向量$\\mathbf{c}$，以下等式成立：$\\|\\mathbf{c} - \\mathbf{\\delta}\\|_2 = \\|\\mathbf{U} \\mathbf{c} - \\mathbf{U} \\mathbf{\\delta}\\|_2$。为什么这意味着在变量的正交变化之后，扰动的程度不会改变？\n",
|
||
"1. 尝试对函数$f(\\mathbf{x}) = 0.1 x_1^2 + 2 x_2^2$、以及它旋转45度后的函数即$f(\\mathbf{x}) = 0.1 (x_1 + x_2)^2 + 2 (x_1 - x_2)^2$使用AdaGrad算法。它的表现会不同吗？\n",
|
||
"1. 证明[格什戈林圆盘定理](https://en.wikipedia.org/wiki/Gershgorin_circle_theorem)，其中提到，矩阵$\\mathbf{M}$的特征值$\\lambda_i$在至少一个$j$的选项中满足$|\\lambda_i - \\mathbf{M}_{jj}| \\leq \\sum_{k \\neq j} |\\mathbf{M}_{jk}|$的要求。\n",
|
||
"1. 关于对角线预处理矩阵$\\mathrm{diag}^{-\\frac{1}{2}}(\\mathbf{M}) \\mathbf{M} \\mathrm{diag}^{-\\frac{1}{2}}(\\mathbf{M})$的特征值，格什戈林的定理告诉了我们什么？\n",
|
||
"1. 尝试对适当的深度网络使用AdaGrad算法，例如，:numref:`sec_lenet`中应用于Fashion-MNIST的深度网络。\n",
|
||
"1. 要如何修改AdaGrad算法，才能使其在学习率方面的衰减不那么激进？\n"
|
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]
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"source": [
|
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"[Discussions](https://discuss.d2l.ai/t/4319)\n"
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]
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}
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],
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},
|
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"nbformat": 4,
|
||
"nbformat_minor": 5
|
||
} |