machinelearning_notebook/7_deep_learning/1_CNN/6-batch-normalization.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 批标准化\n",
    "在正式进入模型的构建和训练之前，先讲一下数据预处理和批标准化。因为模型训练并不容易，特别是一些非常复杂的模型，并不能非常好的训练得到收敛的结果，所以对数据增加一些预处理，同时使用批标准化能够得到非常好的收敛结果，这也是卷积网络能够训练到非常深的层的一个重要原因。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. 数据预处理\n",
    "目前数据预处理最常见的方法就是 `中心化` 和 `标准化`\n",
    "* **中心化** 相当于修正数据的中心位置，实现方法非常简单，就是在每个特征维度上减去对应的均值，最后得到 0 均值的特征。\n",
    "* **标准化** 也非常简单，在数据变成 0 均值之后，为了使得不同的特征维度有着相同的规模，可以除以标准差近似为一个标准正态分布，也可以依据最大值和最小值将其转化为 -1 ~ 1 之间\n",
    "\n",
    "下图是处理的的示例：\n",
    "\n",
    "![](images/data_normalize.png)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Batch Normalization\n",
    "前面在数据预处理的时候，我们尽量输入特征不相关且满足一个标准的正态分布，这样模型的表现一般也较好。但是对于很深的网路结构，网路的非线性层会使得输出的结果变得相关，且不再满足一个标准的 N(0, 1) 的分布，甚至输出的中心已经发生了偏移，这对于模型的训练，特别是深层的模型训练非常的困难。\n",
    "\n",
    "所以在 2015 年一篇论文提出了这个方法，批标准化，简而言之，就是对于每一层网络的输出，对其做一个归一化，使其服从标准的正态分布，这样后一层网络的输入也是一个标准的正态分布，所以能够比较好的进行训练，加快收敛速度。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "batch normalization 的实现非常简单，对于给定的一个 batch 的数据 $B = \\{x_1, x_2, \\cdots, x_m\\}$算法的公式如下\n",
    "\n",
    "$$\n",
    "\\mu_B = \\frac{1}{m} \\sum_{i=1}^m x_i\n",
    "$$\n",
    "$$\n",
    "\\sigma^2_B = \\frac{1}{m} \\sum_{i=1}^m (x_i - \\mu_B)^2\n",
    "$$\n",
    "$$\n",
    "\\hat{x}_i = \\frac{x_i - \\mu_B}{\\sqrt{\\sigma^2_B + \\epsilon}}\n",
    "$$\n",
    "$$\n",
    "y_i = \\gamma \\hat{x}_i + \\beta\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "第一行和第二行是计算出一个 batch 中数据的均值和方差，接着使用第三个公式对 batch 中的每个数据点做标准化，$\\epsilon$ 是为了计算稳定引入的一个小的常数，通常取 $10^{-5}$，最后利用权重修正得到最后的输出结果，非常的简单，下面我们可以实现一下简单的一维的情况，也就是神经网络中的情况"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-23T06:50:51.579067Z",
     "start_time": "2017-12-23T06:50:51.575693Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import sys\n",
    "sys.path.append('..')\n",
    "\n",
    "import torch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-23T07:14:11.077807Z",
     "start_time": "2017-12-23T07:14:11.060849Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def simple_batch_norm_1d(x, gamma, beta):\n",
    "    eps = 1e-5\n",
    "    x_mean = torch.mean(x, dim=0, keepdim=True) # 保留维度进行 broadcast\n",
    "    x_var = torch.mean((x - x_mean) ** 2, dim=0, keepdim=True)\n",
    "    x_hat = (x - x_mean) / torch.sqrt(x_var + eps)\n",
    "    return gamma.view_as(x_mean) * x_hat + beta.view_as(x_mean)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们来验证一下是否对于任意的输入，输出会被标准化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-23T07:14:20.610603Z",
     "start_time": "2017-12-23T07:14:20.597682Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "before bn: \n",
      "\n",
      "  0   1   2\n",
      "  3   4   5\n",
      "  6   7   8\n",
      "  9  10  11\n",
      " 12  13  14\n",
      "[torch.FloatTensor of size 5x3]\n",
      "\n",
      "after bn: \n",
      "\n",
      "-1.4142 -1.4142 -1.4142\n",
      "-0.7071 -0.7071 -0.7071\n",
      " 0.0000  0.0000  0.0000\n",
      " 0.7071  0.7071  0.7071\n",
      " 1.4142  1.4142  1.4142\n",
      "[torch.FloatTensor of size 5x3]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "x = torch.arange(15).view(5, 3)\n",
    "gamma = torch.ones(x.shape[1])\n",
    "beta = torch.zeros(x.shape[1])\n",
    "print('before bn: ')\n",
    "print(x)\n",
    "y = simple_batch_norm_1d(x, gamma, beta)\n",
    "print('after bn: ')\n",
    "print(y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以看到这里一共是 5 个数据点，三个特征，每一列表示一个特征的不同数据点，使用批标准化之后，每一列都变成了标准的正态分布\n",
    "\n",
    "这个时候会出现一个问题，就是测试的时候该使用批标准化吗？\n",
    "\n",
    "答案是肯定的，因为训练的时候使用了，而测试的时候不使用肯定会导致结果出现偏差，但是测试的时候如果只有一个数据集，那么均值不就是这个值，方差为 0 吗？这显然是随机的，所以测试的时候不能用测试的数据集去算均值和方差，而是用训练的时候算出的移动平均均值和方差去代替\n",
    "\n",
    "下面我们实现以下能够区分训练状态和测试状态的批标准化方法"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-23T07:32:48.025709Z",
     "start_time": "2017-12-23T07:32:48.005892Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def batch_norm_1d(x, gamma, beta, is_training, moving_mean, moving_var, moving_momentum=0.1):\n",
    "    eps = 1e-5\n",
    "    x_mean = torch.mean(x, dim=0, keepdim=True) # 保留维度进行 broadcast\n",
    "    x_var = torch.mean((x - x_mean) ** 2, dim=0, keepdim=True)\n",
    "    if is_training:\n",
    "        x_hat = (x - x_mean) / torch.sqrt(x_var + eps)\n",
    "        moving_mean[:] = moving_momentum * moving_mean + (1. - moving_momentum) * x_mean\n",
    "        moving_var[:] = moving_momentum * moving_var + (1. - moving_momentum) * x_var\n",
    "    else:\n",
    "        x_hat = (x - moving_mean) / torch.sqrt(moving_var + eps)\n",
    "    return gamma.view_as(x_mean) * x_hat + beta.view_as(x_mean)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "下面我们使用上一节课将的深度神经网络分类 mnist 数据集的例子来试验一下批标准化是否有用"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from torchvision.datasets import mnist # 导入 pytorch 内置的 mnist 数据\n",
    "from torch.utils.data import DataLoader\n",
    "from torch import nn\n",
    "from torch.autograd import Variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 使用内置函数下载 mnist 数据集\n",
    "train_set = mnist.MNIST('../../data/mnist', train=True)\n",
    "test_set  = mnist.MNIST('../../data/mnist', train=False)\n",
    "\n",
    "def data_tf(x):\n",
    "    x = np.array(x, dtype='float32') / 255\n",
    "    x = (x - 0.5) / 0.5 # 数据预处理，标准化\n",
    "    x = x.reshape((-1,)) # 拉平\n",
    "    x = torch.from_numpy(x)\n",
    "    return x\n",
    "\n",
    "train_set = mnist.MNIST('../../data/mnist', train=True,  transform=data_tf, download=True) # 重新载入数据集，申明定义的数据变换\n",
    "test_set  = mnist.MNIST('../../data/mnist', train=False, transform=data_tf, download=True)\n",
    "train_data = DataLoader(train_set, batch_size=64, shuffle=True)\n",
    "test_data = DataLoader(test_set, batch_size=128, shuffle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class multi_network(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(multi_network, self).__init__()\n",
    "        self.layer1 = nn.Linear(784, 100)\n",
    "        self.relu = nn.ReLU(True)\n",
    "        self.layer2 = nn.Linear(100, 10)\n",
    "        \n",
    "        self.gamma = nn.Parameter(torch.randn(100))\n",
    "        self.beta = nn.Parameter(torch.randn(100))\n",
    "        \n",
    "        self.moving_mean = Variable(torch.zeros(100))\n",
    "        self.moving_var = Variable(torch.zeros(100))\n",
    "        \n",
    "    def forward(self, x, is_train=True):\n",
    "        x = self.layer1(x)\n",
    "        x = batch_norm_1d(x, self.gamma, self.beta, is_train, self.moving_mean, self.moving_var)\n",
    "        x = self.relu(x)\n",
    "        x = self.layer2(x)\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "net = multi_network()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 定义 loss 函数\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "optimizer = torch.optim.SGD(net.parameters(), 1e-1) # 使用随机梯度下降，学习率 0.1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "为了方便，训练函数已经定义在外面的 utils.py 中，跟前面训练网络的操作是一样的，感兴趣的同学可以去看看"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 0. Train Loss: 0.308139, Train Acc: 0.912797, Valid Loss: 0.181375, Valid Acc: 0.948279, Time 00:00:07\n",
      "Epoch 1. Train Loss: 0.174049, Train Acc: 0.949910, Valid Loss: 0.143940, Valid Acc: 0.958267, Time 00:00:09\n",
      "Epoch 2. Train Loss: 0.134983, Train Acc: 0.961587, Valid Loss: 0.122489, Valid Acc: 0.963904, Time 00:00:08\n",
      "Epoch 3. Train Loss: 0.111758, Train Acc: 0.968317, Valid Loss: 0.106595, Valid Acc: 0.966278, Time 00:00:09\n",
      "Epoch 4. Train Loss: 0.096425, Train Acc: 0.971915, Valid Loss: 0.108423, Valid Acc: 0.967563, Time 00:00:10\n",
      "Epoch 5. Train Loss: 0.084424, Train Acc: 0.974464, Valid Loss: 0.107135, Valid Acc: 0.969838, Time 00:00:09\n",
      "Epoch 6. Train Loss: 0.076206, Train Acc: 0.977645, Valid Loss: 0.092725, Valid Acc: 0.971420, Time 00:00:09\n",
      "Epoch 7. Train Loss: 0.069438, Train Acc: 0.979661, Valid Loss: 0.091497, Valid Acc: 0.971519, Time 00:00:09\n",
      "Epoch 8. Train Loss: 0.062908, Train Acc: 0.980810, Valid Loss: 0.088797, Valid Acc: 0.972903, Time 00:00:08\n",
      "Epoch 9. Train Loss: 0.058186, Train Acc: 0.982309, Valid Loss: 0.090830, Valid Acc: 0.972310, Time 00:00:08\n"
     ]
    }
   ],
   "source": [
    "from utils import train\n",
    "train(net, train_data, test_data, 10, optimizer, criterion)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "这里的 $\\gamma$ 和 $\\beta$ 都作为参数进行训练，初始化为随机的高斯分布，`moving_mean` 和 `moving_var` 都初始化为 0，并不是更新的参数，训练完 10 次之后，我们可以看看移动平均和移动方差被修改为了多少"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Variable containing:\n",
      " 0.5505\n",
      " 2.0835\n",
      " 0.0794\n",
      "-0.1991\n",
      "-0.9822\n",
      "-0.5820\n",
      " 0.6991\n",
      "-0.1292\n",
      " 2.9608\n",
      " 1.0826\n",
      "[torch.FloatTensor of size 10]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# 打出 moving_mean 的前 10 项\n",
    "print(net.moving_mean[:10])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以看到，这些值已经在训练的过程中进行了修改，在测试过程中，我们不需要再计算均值和方差，直接使用移动平均和移动方差即可"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "作为对比，我们看看不使用批标准化的结果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 0. Train Loss: 0.402263, Train Acc: 0.873817, Valid Loss: 0.220468, Valid Acc: 0.932852, Time 00:00:07\n",
      "Epoch 1. Train Loss: 0.181916, Train Acc: 0.945379, Valid Loss: 0.162440, Valid Acc: 0.953817, Time 00:00:08\n",
      "Epoch 2. Train Loss: 0.136073, Train Acc: 0.958522, Valid Loss: 0.264888, Valid Acc: 0.918216, Time 00:00:08\n",
      "Epoch 3. Train Loss: 0.111658, Train Acc: 0.966551, Valid Loss: 0.149704, Valid Acc: 0.950752, Time 00:00:08\n",
      "Epoch 4. Train Loss: 0.096433, Train Acc: 0.970732, Valid Loss: 0.116364, Valid Acc: 0.963311, Time 00:00:07\n",
      "Epoch 5. Train Loss: 0.083800, Train Acc: 0.973914, Valid Loss: 0.105775, Valid Acc: 0.968058, Time 00:00:08\n",
      "Epoch 6. Train Loss: 0.074534, Train Acc: 0.977129, Valid Loss: 0.094511, Valid Acc: 0.970728, Time 00:00:08\n",
      "Epoch 7. Train Loss: 0.067365, Train Acc: 0.979311, Valid Loss: 0.130495, Valid Acc: 0.960146, Time 00:00:09\n",
      "Epoch 8. Train Loss: 0.061585, Train Acc: 0.980894, Valid Loss: 0.089632, Valid Acc: 0.974090, Time 00:00:08\n",
      "Epoch 9. Train Loss: 0.055352, Train Acc: 0.982892, Valid Loss: 0.091508, Valid Acc: 0.970431, Time 00:00:08\n"
     ]
    }
   ],
   "source": [
    "no_bn_net = nn.Sequential(\n",
    "    nn.Linear(784, 100),\n",
    "    nn.ReLU(True),\n",
    "    nn.Linear(100, 10)\n",
    ")\n",
    "\n",
    "optimizer = torch.optim.SGD(no_bn_net.parameters(), 1e-1) # 使用随机梯度下降，学习率 0.1\n",
    "train(no_bn_net, train_data, test_data, 10, optimizer, criterion)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以看到虽然最后的结果两种情况一样，但是如果我们看前几次的情况，可以看到使用批标准化的情况能够更快的收敛，因为这只是一个小网络，所以用不用批标准化都能够收敛，但是对于更加深的网络，使用批标准化在训练的时候能够很快地收敛"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从上面可以看到，我们自己实现了 2 维情况的批标准化，对应于卷积的 4 维情况的标准化是类似的，只需要沿着通道的维度进行均值和方差的计算，但是我们自己实现批标准化是很累的，pytorch 当然也为我们内置了批标准化的函数，一维和二维分别是 `torch.nn.BatchNorm1d()` 和 `torch.nn.BatchNorm2d()`，不同于我们的实现，pytorch 不仅将 $\\gamma$ 和 $\\beta$ 作为训练的参数，也将 `moving_mean` 和 `moving_var` 也作为参数进行训练"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "下面我们在卷积网络下试用一下批标准化看看效果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def data_tf(x):\n",
    "    x = np.array(x, dtype='float32') / 255\n",
    "    x = (x - 0.5) / 0.5 # 数据预处理，标准化\n",
    "    x = torch.from_numpy(x)\n",
    "    x = x.unsqueeze(0)\n",
    "    return x\n",
    "\n",
    "train_set = mnist.MNIST('../../data/mnist', train=True,  transform=data_tf, download=True) # 重新载入数据集，申明定义的数据变换\n",
    "test_set  = mnist.MNIST('../../data/mnist', train=False, transform=data_tf, download=True)\n",
    "train_data = DataLoader(train_set, batch_size=64, shuffle=True)\n",
    "test_data = DataLoader(test_set, batch_size=128, shuffle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 使用批标准化\n",
    "class conv_bn_net(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(conv_bn_net, self).__init__()\n",
    "        self.stage1 = nn.Sequential(\n",
    "            nn.Conv2d(1, 6, 3, padding=1),\n",
    "            nn.BatchNorm2d(6),\n",
    "            nn.ReLU(True),\n",
    "            nn.MaxPool2d(2, 2),\n",
    "            nn.Conv2d(6, 16, 5),\n",
    "            nn.BatchNorm2d(16),\n",
    "            nn.ReLU(True),\n",
    "            nn.MaxPool2d(2, 2)\n",
    "        )\n",
    "        \n",
    "        self.classfy = nn.Linear(400, 10)\n",
    "    def forward(self, x):\n",
    "        x = self.stage1(x)\n",
    "        x = x.view(x.shape[0], -1)\n",
    "        x = self.classfy(x)\n",
    "        return x\n",
    "\n",
    "net = conv_bn_net()\n",
    "optimizer = torch.optim.SGD(net.parameters(), 1e-1) # 使用随机梯度下降，学习率 0.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 0. Train Loss: 0.160329, Train Acc: 0.952842, Valid Loss: 0.063328, Valid Acc: 0.978441, Time 00:00:33\n",
      "Epoch 1. Train Loss: 0.067862, Train Acc: 0.979361, Valid Loss: 0.068229, Valid Acc: 0.979430, Time 00:00:37\n",
      "Epoch 2. Train Loss: 0.051867, Train Acc: 0.984625, Valid Loss: 0.044616, Valid Acc: 0.985265, Time 00:00:37\n",
      "Epoch 3. Train Loss: 0.044797, Train Acc: 0.986141, Valid Loss: 0.042711, Valid Acc: 0.986056, Time 00:00:38\n",
      "Epoch 4. Train Loss: 0.039876, Train Acc: 0.987690, Valid Loss: 0.042499, Valid Acc: 0.985067, Time 00:00:41\n"
     ]
    }
   ],
   "source": [
    "train(net, train_data, test_data, 5, optimizer, criterion)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 不使用批标准化\n",
    "class conv_no_bn_net(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(conv_no_bn_net, self).__init__()\n",
    "        self.stage1 = nn.Sequential(\n",
    "            nn.Conv2d(1, 6, 3, padding=1),\n",
    "            nn.ReLU(True),\n",
    "            nn.MaxPool2d(2, 2),\n",
    "            nn.Conv2d(6, 16, 5),\n",
    "            nn.ReLU(True),\n",
    "            nn.MaxPool2d(2, 2)\n",
    "        )\n",
    "        \n",
    "        self.classfy = nn.Linear(400, 10)\n",
    "    def forward(self, x):\n",
    "        x = self.stage1(x)\n",
    "        x = x.view(x.shape[0], -1)\n",
    "        x = self.classfy(x)\n",
    "        return x\n",
    "\n",
    "net = conv_no_bn_net()\n",
    "optimizer = torch.optim.SGD(net.parameters(), 1e-1) # 使用随机梯度下降，学习率 0.1    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 0. Train Loss: 0.211075, Train Acc: 0.935934, Valid Loss: 0.062950, Valid Acc: 0.980123, Time 00:00:27\n",
      "Epoch 1. Train Loss: 0.066763, Train Acc: 0.978778, Valid Loss: 0.050143, Valid Acc: 0.984375, Time 00:00:29\n",
      "Epoch 2. Train Loss: 0.050870, Train Acc: 0.984292, Valid Loss: 0.039761, Valid Acc: 0.988034, Time 00:00:29\n",
      "Epoch 3. Train Loss: 0.041476, Train Acc: 0.986924, Valid Loss: 0.041925, Valid Acc: 0.986155, Time 00:00:29\n",
      "Epoch 4. Train Loss: 0.036118, Train Acc: 0.988523, Valid Loss: 0.042703, Valid Acc: 0.986452, Time 00:00:29\n"
     ]
    }
   ],
   "source": [
    "train(net, train_data, test_data, 5, optimizer, criterion)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "之后介绍一些著名的网络结构的时候，我们会慢慢认识到批标准化的重要性，使用 pytorch 能够非常方便地添加批标准化层"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## References\n",
    "* [透彻分析批归一化Batch Normalization强大作用](https://m.toutiaocdn.com/i6641764088760238595)"
   ]
  }
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