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"# Adam算法\n",
":label:`sec_adam`\n",
"\n",
"本章我们已经学习了许多有效优化的技术。\n",
"在本节讨论之前，我们先详细回顾一下这些技术：\n",
"\n",
"* 在 :numref:`sec_sgd`中，我们学习了：随机梯度下降在解决优化问题时比梯度下降更有效。\n",
"* 在 :numref:`sec_minibatch_sgd`中，我们学习了：在一个小批量中使用更大的观测值集，可以通过向量化提供额外效率。这是高效的多机、多GPU和整体并行处理的关键。\n",
"* 在 :numref:`sec_momentum`中我们添加了一种机制，用于汇总过去梯度的历史以加速收敛。\n",
"* 在 :numref:`sec_adagrad`中，我们通过对每个坐标缩放来实现高效计算的预处理器。\n",
"* 在 :numref:`sec_rmsprop`中，我们通过学习率的调整来分离每个坐标的缩放。\n",
"\n",
"Adam算法 :cite:`Kingma.Ba.2014`将所有这些技术汇总到一个高效的学习算法中。\n",
"不出预料，作为深度学习中使用的更强大和有效的优化算法之一，它非常受欢迎。\n",
"但是它并非没有问题，尤其是 :cite:`Reddi.Kale.Kumar.2019`表明，有时Adam算法可能由于方差控制不良而发散。\n",
"在完善工作中， :cite:`Zaheer.Reddi.Sachan.ea.2018`给Adam算法提供了一个称为Yogi的热补丁来解决这些问题。\n",
"下面我们了解一下Adam算法。\n",
"\n",
"## 算法\n",
"\n",
"Adam算法的关键组成部分之一是：它使用指数加权移动平均值来估算梯度的动量和二次矩，即它使用状态变量\n",
"\n",
"$$\\begin{aligned}\n",
" \\mathbf{v}_t & \\leftarrow \\beta_1 \\mathbf{v}_{t-1} + (1 - \\beta_1) \\mathbf{g}_t, \\\\\n",
" \\mathbf{s}_t & \\leftarrow \\beta_2 \\mathbf{s}_{t-1} + (1 - \\beta_2) \\mathbf{g}_t^2.\n",
"\\end{aligned}$$\n",
"\n",
"这里$\\beta_1$和$\\beta_2$是非负加权参数。\n",
"常将它们设置为$\\beta_1 = 0.9$和$\\beta_2 = 0.999$。\n",
"也就是说，方差估计的移动远远慢于动量估计的移动。\n",
"注意，如果我们初始化$\\mathbf{v}_0 = \\mathbf{s}_0 = 0$，就会获得一个相当大的初始偏差。\n",
"我们可以通过使用$\\sum_{i=0}^t \\beta^i = \\frac{1 - \\beta^t}{1 - \\beta}$来解决这个问题。\n",
"相应地，标准化状态变量由下式获得\n",
"\n",
"$$\\hat{\\mathbf{v}}_t = \\frac{\\mathbf{v}_t}{1 - \\beta_1^t} \\text{ and } \\hat{\\mathbf{s}}_t = \\frac{\\mathbf{s}_t}{1 - \\beta_2^t}.$$\n",
"\n",
"有了正确的估计，我们现在可以写出更新方程。\n",
"首先，我们以非常类似于RMSProp算法的方式重新缩放梯度以获得\n",
"\n",
"$$\\mathbf{g}_t' = \\frac{\\eta \\hat{\\mathbf{v}}_t}{\\sqrt{\\hat{\\mathbf{s}}_t} + \\epsilon}.$$\n",
"\n",
"与RMSProp不同，我们的更新使用动量$\\hat{\\mathbf{v}}_t$而不是梯度本身。\n",
"此外，由于使用$\\frac{1}{\\sqrt{\\hat{\\mathbf{s}}_t} + \\epsilon}$而不是$\\frac{1}{\\sqrt{\\hat{\\mathbf{s}}_t + \\epsilon}}$进行缩放，两者会略有差异。\n",
"前者在实践中效果略好一些，因此与RMSProp算法有所区分。\n",
"通常，我们选择$\\epsilon = 10^{-6}$，这是为了在数值稳定性和逼真度之间取得良好的平衡。\n",
"\n",
"最后，我们简单更新：\n",
"\n",
"$$\\mathbf{x}_t \\leftarrow \\mathbf{x}_{t-1} - \\mathbf{g}_t'.$$\n",
"\n",
"回顾Adam算法，它的设计灵感很清楚：\n",
"首先，动量和规模在状态变量中清晰可见，\n",
"它们相当独特的定义使我们移除偏项（这可以通过稍微不同的初始化和更新条件来修正）。\n",
"其次，RMSProp算法中两项的组合都非常简单。\n",
"最后，明确的学习率$\\eta$使我们能够控制步长来解决收敛问题。\n",
"\n",
"## 实现\n",
"\n",
"从头开始实现Adam算法并不难。\n",
"为方便起见，我们将时间步$t$存储在`hyperparams`字典中。\n",
"除此之外，一切都很简单。\n"
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"%matplotlib inline\n",
"import torch\n",
"from d2l import torch as d2l\n",
"\n",
"\n",
"def init_adam_states(feature_dim):\n",
" v_w, v_b = torch.zeros((feature_dim, 1)), torch.zeros(1)\n",
" s_w, s_b = torch.zeros((feature_dim, 1)), torch.zeros(1)\n",
" return ((v_w, s_w), (v_b, s_b))\n",
"\n",
"def adam(params, states, hyperparams):\n",
" beta1, beta2, eps = 0.9, 0.999, 1e-6\n",
" for p, (v, s) in zip(params, states):\n",
" with torch.no_grad():\n",
" v[:] = beta1 * v + (1 - beta1) * p.grad\n",
" s[:] = beta2 * s + (1 - beta2) * torch.square(p.grad)\n",
" v_bias_corr = v / (1 - beta1 ** hyperparams['t'])\n",
" s_bias_corr = s / (1 - beta2 ** hyperparams['t'])\n",
" p[:] -= hyperparams['lr'] * v_bias_corr / (torch.sqrt(s_bias_corr)\n",
" + eps)\n",
" p.grad.data.zero_()\n",
" hyperparams['t'] += 1"
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"现在，我们用以上Adam算法来训练模型，这里我们使用$\\eta = 0.01$的学习率。\n"
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"loss: 0.244, 0.015 sec/epoch\n"
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"source": [
"此外，我们可以用深度学习框架自带算法应用Adam算法，这里我们只需要传递配置参数。\n"
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{
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"## Yogi\n",
"\n",
"Adam算法也存在一些问题：\n",
"即使在凸环境下，当$\\mathbf{s}_t$的二次矩估计值爆炸时，它可能无法收敛。\n",
" :cite:`Zaheer.Reddi.Sachan.ea.2018`为$\\mathbf{s}_t$提出了的改进更新和参数初始化。\n",
"论文中建议我们重写Adam算法更新如下：\n",
"\n",
"$$\\mathbf{s}_t \\leftarrow \\mathbf{s}_{t-1} + (1 - \\beta_2) \\left(\\mathbf{g}_t^2 - \\mathbf{s}_{t-1}\\right).$$\n",
"\n",
"每当$\\mathbf{g}_t^2$具有值很大的变量或更新很稀疏时，$\\mathbf{s}_t$可能会太快地“忘记”过去的值。\n",
"一个有效的解决方法是将$\\mathbf{g}_t^2 - \\mathbf{s}_{t-1}$替换为$\\mathbf{g}_t^2 \\odot \\mathop{\\mathrm{sgn}}(\\mathbf{g}_t^2 - \\mathbf{s}_{t-1})$。\n",
"这就是Yogi更新，现在更新的规模不再取决于偏差的量。\n",
"\n",
"$$\\mathbf{s}_t \\leftarrow \\mathbf{s}_{t-1} + (1 - \\beta_2) \\mathbf{g}_t^2 \\odot \\mathop{\\mathrm{sgn}}(\\mathbf{g}_t^2 - \\mathbf{s}_{t-1}).$$\n",
"\n",
"论文中，作者还进一步建议用更大的初始批量来初始化动量，而不仅仅是初始的逐点估计。\n"
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"text/plain": [
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},
"metadata": {
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"output_type": "display_data"
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],
"source": [
"def yogi(params, states, hyperparams):\n",
" beta1, beta2, eps = 0.9, 0.999, 1e-3\n",
" for p, (v, s) in zip(params, states):\n",
" with torch.no_grad():\n",
" v[:] = beta1 * v + (1 - beta1) * p.grad\n",
" s[:] = s + (1 - beta2) * torch.sign(\n",
" torch.square(p.grad) - s) * torch.square(p.grad)\n",
" v_bias_corr = v / (1 - beta1 ** hyperparams['t'])\n",
" s_bias_corr = s / (1 - beta2 ** hyperparams['t'])\n",
" p[:] -= hyperparams['lr'] * v_bias_corr / (torch.sqrt(s_bias_corr)\n",
" + eps)\n",
" p.grad.data.zero_()\n",
" hyperparams['t'] += 1\n",
"\n",
"data_iter, feature_dim = d2l.get_data_ch11(batch_size=10)\n",
"d2l.train_ch11(yogi, init_adam_states(feature_dim),\n",
" {'lr': 0.01, 't': 1}, data_iter, feature_dim);"
]
},
{
"cell_type": "markdown",
"id": "4feb3739",
"metadata": {
"origin_pos": 17
},
"source": [
"## 小结\n",
"\n",
"* Adam算法将许多优化算法的功能结合到了相当强大的更新规则中。\n",
"* Adam算法在RMSProp算法基础上创建的，还在小批量的随机梯度上使用EWMA。\n",
"* 在估计动量和二次矩时，Adam算法使用偏差校正来调整缓慢的启动速度。\n",
"* 对于具有显著差异的梯度，我们可能会遇到收敛性问题。我们可以通过使用更大的小批量或者切换到改进的估计值$\\mathbf{s}_t$来修正它们。Yogi提供了这样的替代方案。\n",
"\n",
"## 练习\n",
"\n",
"1. 调节学习率，观察并分析实验结果。\n",
"1. 试着重写动量和二次矩更新，从而使其不需要偏差校正。\n",
"1. 收敛时为什么需要降低学习率$\\eta$？\n",
"1. 尝试构造一个使用Adam算法会发散而Yogi会收敛的例子。\n"
]
},
{
"cell_type": "markdown",
"id": "475fc1bc",
"metadata": {
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"tab": [
"pytorch"
]
},
"source": [
"[Discussions](https://discuss.d2l.ai/t/4331)\n"
]
}
],
"metadata": {
"language_info": {
"name": "python"
},
"required_libs": []
},
"nbformat": 4,
"nbformat_minor": 5
}
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