Improve CNN description

7_deep_learning/1_CNN/2-vgg.ipynb

@@ -8,7 +8,7 @@
     "\n",
     "计算机视觉是一直深度学习的主战场，从这里将学习近几年非常流行的卷积网络结构，网络结构由浅变深，参数越来越多，网络有着更多的跨层链接。\n",
     "\n",
-    "VGG卷积神经网络是牛津大学在2014年提出来的模型。当这个模型被提出时，由于它的简洁性和实用性，马上成为了当时最流行的卷积神经网络模型。它在图像分类和目标检测任务中都表现出非常好的结果。在2014年的ILSVRC比赛中，VGG 在Top-5中取得了92.3%的正确率。"
+    "VGG是Oxford的Visual Geometry Group的组提出的。该网络是在ILSVRC 2014上的相关工作，主要工作是证明了增加网络的深度能够在一定程度上影响网络最终的性能。VGG有两种结构，分别是VGG16和VGG19，两者并没有本质上的区别，只是网络深度不一样。当这个模型被提出时，由于它的简洁性和实用性，马上成为了当时最流行的卷积神经网络模型。它在图像分类和目标检测任务中都表现出非常好的结果。在2014年的ILSVRC比赛中，VGG 在Top-5中取得了92.3%的正确率。"
    ]
   },
   {
@@ -38,8 +38,7 @@
     "\n",
     "![](images/VGG_network.png)\n",
     "\n",
-    "VGG整个结构只有3×3的卷积层，连续的卷积层后使用池化层隔开。虽然层数很多，但是很简洁。几乎全部使用 3 x 3 的卷积核以及 2 x 2 的池化层，使用小的卷积核进行多层的堆叠和一个大的卷积核的感受野是相同的，同时小的卷积核还能减少参数，同时可以有更深的结构。\n",
-    "\n"
+    "VGG整个结构只有3×3的卷积层，连续的卷积层后使用池化层隔开。虽然层数很多，但是很简洁。几乎全部使用 3 x 3 的卷积核以及 2 x 2 的池化层，使用小的卷积核进行多层的堆叠和一个大的卷积核的感受野是相同的，同时小的卷积核还能减少参数，同时可以有更深的结构。\n"
    ]
   },
   {
@@ -436,6 +435,15 @@
    "source": [
     "可以看到，跑完 20 次，VGG 能在 CIFAR10 上取得 76% 左右的测试准确率"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 参考\n",
+    "\n",
+    "* [一文读懂VGG网络](https://zhuanlan.zhihu.com/p/41423739)"
+   ]
   }
  ],
  "metadata": {
@@ -454,7 +462,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.4"
+   "version": "3.7.9"
   }
  },
  "nbformat": 4,

7_deep_learning/1_CNN/4-resnet.ipynb

@@ -5,26 +5,37 @@
    "metadata": {},
    "source": [
     "# ResNet\n",
-    "当大家还在惊叹 GoogLeNet 的 inception 结构的时候，微软亚洲研究院的研究员已经在设计更深但结构更加简单的网络 ResNet，并且凭借这个网络子在 2015 年 ImageNet 比赛上大获全胜。\n",
     "\n",
-    "ResNet 有效地解决了深度神经网络难以训练的问题，可以训练高达 1000 层的卷积网络。网络之所以难以训练，是因为存在着梯度消失的问题，离 loss 函数越远的层，在反向传播的时候，梯度越小，就越难以更新，随着层数的增加，这个现象越严重。之前有两种常见的方案来解决这个问题：\n",
+    "当大家还在惊叹 GoogLeNet 的 Inception 结构的时候，微软亚洲研究院的研究员已经在设计更深但结构更加简单的网络 ResNet，并且凭借这个网络子在 2015 年 ImageNet 比赛上大获全胜。\n",
     "\n",
-    "1.按层训练，先训练比较浅的层，然后在不断增加层数，但是这种方法效果不是特别好，而且比较麻烦\n",
+    "ResNet 有效地解决了深度神经网络难以训练的问题，可以训练高达 1000 层的卷积网络。网络之所以难以训练，是因为存在着梯度消失的问题，离 loss 函数越远的层，在反向传播的时候，梯度越小，就越难以更新，随着层数的增加，这个现象越严重。之前有两种常见的方案来解决这个问题：\n",
     "\n",
-    "2.使用更宽的层，或者增加输出通道，而不加深网络的层数，这种结构往往得到的效果又不好\n",
+    "1. 按层训练，先训练比较浅的层，然后在不断增加层数，但是这种方法效果不是特别好，而且比较麻烦\n",
+    "2. 使用更宽的层，或者增加输出通道，而不加深网络的层数，这种结构往往得到的效果又不好\n",
     "\n",
     "ResNet 通过引入了跨层链接解决了梯度回传消失的问题。\n",
     "\n",
-    "![](https://ws1.sinaimg.cn/large/006tNc79ly1fmptq2snv9j30j808t74a.jpg)"
+    "![](images/ResNet_PlainNet.png)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "这就普通的网络连接跟跨层残差连接的对比图，使用普通的连接，上层的梯度必须要一层一层传回来，而是用残差连接，相当于中间有了一条更短的路，梯度能够从这条更短的路传回来，避免了梯度过小的情况。\n",
+    "这就普通的网络连接跟跨层残差连接的对比图，使用普通的连接（左图），上层的梯度必须要一层一层传回来；而是用残差连接（右图），相当于中间有了一条更短的路，梯度能够从这条更短的路传回来，避免了梯度过小的情况。\n",
     "\n",
-    "假设某层的输入是 x，期望输出是 H(x)， 如果我们直接把输入 x 传到输出作为初始结果，这就是一个更浅层的网络，更容易训练，而这个网络没有学会的部分，我们可以使用更深的网络 F(x) 去训练它，使得训练更加容易，最后希望拟合的结果就是 F(x) = H(x) - x，这就是一个残差的结构\n",
+    "假设某层的输入是 $x$，期望输出是 $H(x)$\n",
+    "* 如果我们直接把输入 $x$ 传到输出作为初始结果，这就是一个更浅层的网络，更容易训练\n",
+    "* 而这个网络没有学习的部分，我们可以使用更深的网络 $F(x)$ 去训练它，使得训练更加容易\n",
+    "* 最后希望拟合的结果就是 $F(x) = H(x) - x$，这就是一个残差的结构\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1. ResidualBlock\n",
     "\n",
     "残差网络的结构就是上面这种残差块的堆叠，下面让我们来实现一个 residual block"
    ]
@@ -63,7 +74,8 @@
    "outputs": [],
    "source": [
     "def conv3x3(in_channel, out_channel, stride=1):\n",
-    "    return nn.Conv2d(in_channel, out_channel, 3, stride=stride, padding=1, bias=False)"
+    "    return nn.Conv2d(in_channel, out_channel, 3, \n",
+    "                     stride=stride, padding=1, bias=False)"
    ]
   },
   {
@@ -77,7 +89,7 @@
    },
    "outputs": [],
    "source": [
-    "class residual_block(nn.Module):\n",
+    "class Residual_Block(nn.Module):\n",
     "    def __init__(self, in_channel, out_channel, same_shape=True):\n",
     "        super(residual_block, self).__init__()\n",
     "        self.same_shape = same_shape\n",
@@ -89,7 +101,8 @@
     "        self.conv2 = conv3x3(out_channel, out_channel)\n",
     "        self.bn2 = nn.BatchNorm2d(out_channel)\n",
     "        if not self.same_shape:\n",
-    "            self.conv3 = nn.Conv2d(in_channel, out_channel, 1, stride=stride)\n",
+    "            self.conv3 = nn.Conv2d(in_channel, out_channel, 1, \n",
+    "                                   stride=stride)\n",
     "        \n",
     "    def forward(self, x):\n",
     "        out = self.conv1(x)\n",
@@ -130,7 +143,7 @@
    ],
    "source": [
     "# 输入输出形状相同\n",
-    "test_net = residual_block(32, 32)\n",
+    "test_net = Residual_Block(32, 32)\n",
     "test_x = Variable(torch.zeros(1, 32, 96, 96))\n",
     "print('input: {}'.format(test_x.shape))\n",
     "test_y = test_net(test_x)\n",
@@ -158,7 +171,7 @@
    ],
    "source": [
     "# 输入输出形状不同\n",
-    "test_net = residual_block(3, 32, False)\n",
+    "test_net = Residual_Block(3, 32, False)\n",
     "test_x = Variable(torch.zeros(1, 3, 96, 96))\n",
     "print('input: {}'.format(test_x.shape))\n",
     "test_y = test_net(test_x)\n",
@@ -169,7 +182,18 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "下面我们尝试实现一个 ResNet，它就是 residual block 模块的堆叠"
+    "一个Residual_Block的结构如下图所示\n",
+    "\n",
+    "![resnet-block.png](images/resnet-block.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. ResNet的网络实现\n",
+    "\n",
+    "下面实现一个 ResNet，它就是 residual block 模块的堆叠"
    ]
   },
   {
@@ -183,7 +207,7 @@
    },
    "outputs": [],
    "source": [
-    "class resnet(nn.Module):\n",
+    "class ResNet(nn.Module):\n",
     "    def __init__(self, in_channel, num_classes, verbose=False):\n",
     "        super(resnet, self).__init__()\n",
     "        self.verbose = verbose\n",
@@ -192,23 +216,23 @@
     "        \n",
     "        self.block2 = nn.Sequential(\n",
     "            nn.MaxPool2d(3, 2),\n",
-    "            residual_block(64, 64),\n",
-    "            residual_block(64, 64)\n",
+    "            Residual_Block(64, 64),\n",
+    "            Residual_Block(64, 64)\n",
     "        )\n",
     "        \n",
     "        self.block3 = nn.Sequential(\n",
-    "            residual_block(64, 128, False),\n",
-    "            residual_block(128, 128)\n",
+    "            Residual_Block(64, 128, False),\n",
+    "            Residual_Block(128, 128)\n",
     "        )\n",
     "        \n",
     "        self.block4 = nn.Sequential(\n",
-    "            residual_block(128, 256, False),\n",
-    "            residual_block(256, 256)\n",
+    "            Residual_Block(128, 256, False),\n",
+    "            Residual_Block(256, 256)\n",
     "        )\n",
     "        \n",
     "        self.block5 = nn.Sequential(\n",
-    "            residual_block(256, 512, False),\n",
-    "            residual_block(512, 512),\n",
+    "            Residual_Block(256, 512, False),\n",
+    "            Residual_Block(512, 512),\n",
     "            nn.AvgPool2d(3)\n",
     "        )\n",
     "        \n",
@@ -299,7 +323,7 @@
     "test_set = CIFAR10('../../data', train=False, transform=data_tf)\n",
     "test_data = torch.utils.data.DataLoader(test_set, batch_size=128, shuffle=False)\n",
     "\n",
-    "net = resnet(3, 10)\n",
+    "net = ResNet(3, 10)\n",
     "optimizer = torch.optim.SGD(net.parameters(), lr=0.01)\n",
     "criterion = nn.CrossEntropyLoss()"
    ]
@@ -350,16 +374,33 @@
    "metadata": {},
    "source": [
     "ResNet 使用跨层通道使得训练非常深的卷积神经网络成为可能。同样它使用很简单的卷积层配置，使得其拓展更加简单。\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 练习\n",
     "\n",
-    "**小练习：  \n",
-    "1.尝试一下论文中提出的 bottleneck 的结构   \n",
-    "2.尝试改变 conv -> bn -> relu 的顺序为 bn -> relu -> conv，看看精度会不会提高**"
+    "* 尝试一下论文中提出的 bottleneck 的结构   \n",
+    "* 尝试改变 conv -> bn -> relu 的顺序为 bn -> relu -> conv，看看精度会不会提高\n",
+    "* 在Residual_Block加入1x1卷积，并尝试结果的差别"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 参考资料\n",
+    "* [Residual Networks (ResNet)](https://d2l.ai/chapter_convolutional-modern/resnet.html)\n",
+    "* [An Overview of ResNet and its Variants](https://towardsdatascience.com/an-overview-of-resnet-and-its-variants-5281e2f56035)"
    ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },
@@ -373,7 +414,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.7"
+   "version": "3.7.9"
   }
  },
  "nbformat": 4,

7_deep_learning/1_CNN/5-densenet.ipynb

@@ -5,27 +5,37 @@
    "metadata": {},
    "source": [
     "# DenseNet\n",
-    "因为 ResNet 提出了跨层链接的思想，这直接影响了随后出现的卷积网络架构，其中最有名的就是 cvpr 2017 的 best paper，DenseNet。\n",
     "\n",
-    "DenseNet 和 ResNet 不同在于 ResNet 是跨层求和，而 DenseNet 是跨层将特征在通道维度进行拼接，下面可以看看他们两者的图示\n",
+    "因为 ResNet 提出了跨层链接的思想，这直接影响了随后出现的卷积网络架构，其中最有名的就是 CVPR 2017 的 Best Paper，DenseNet。DenseNet 和 ResNet 不同在于 ResNet 是跨层求和，而 DenseNet 是跨层将特征在通道维度进行拼接，下面可以看看他们两者的图示：\n",
     "\n",
-    "![](https://ws4.sinaimg.cn/large/006tNc79ly1fmpvj5vkfhj30uw0anq73.jpg)\n",
-    "\n",
-    "![](https://ws1.sinaimg.cn/large/006tNc79ly1fmpvj7fxd1j30vb0eyzqf.jpg)"
+    "![cnn_vs_resnet_vs_densenet.png](images/cnn_vs_resnet_vs_densenet.png)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "第一张图是 ResNet，第二张图是 DenseNet，因为是在通道维度进行特征的拼接，所以底层的输出会保留进入所有后面的层，这能够更好的保证梯度的传播，同时能够使用低维的特征和高维的特征进行联合训练，能够得到更好的结果。"
+    "第2张图是 ResNet，第3张图是 DenseNet，因为是在通道维度进行特征的拼接，所以底层的输出会保留进入所有后面的层，这能够更好的保证梯度的传播，同时能够使用低维的特征和高维的特征进行联合训练，能够得到更好的结果。\n",
+    "\n",
+    "DenseNet主要的优点包括：\n",
+    "1. 减轻了vanishing-gradient（梯度消失）\n",
+    "2. 加强了feature的传递\n",
+    "3. 更有效地利用了feature\n",
+    "4. 一定程度上较少了参数数量\n",
+    "\n",
+    "在深度学习网络中，随着网络深度的加深，梯度消失问题会愈加明显，目前很多论文都针对这个问题提出了解决方案，比如ResNet，Highway Networks，Stochastic depth，FractalNets等，尽管这些算法的网络结构有差别，但是核心都在于：**create short paths from early layers to later layers**。延续这个思路，那就是在保证网络中层与层之间最大程度的信息传输的前提下，直接将所有层连接起来。\n",
+    "\n",
+    "先放一个dense block的结构图。在传统的卷积神经网络中，如果你有L层，那么就会有L个连接，但是在DenseNet中，会有 **L(L+1)/2** 个连接。简单讲，就是每一层的输入来自前面所有层的输出。如下图：x0是input，H1的输入是x0（input），H2的输入是x0和x1（x1是H1的输出）……\n",
+    "\n",
+    "![DesNet_arch.png](images/DesNet_arch.png)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "DenseNet 主要由 dense block 构成，下面我们来实现一个 densen block"
+    "## 1. Dense_Block\n",
+    "DenseNet 主要由 Dense Block 构成，下面我们来实现一个 Densen Block"
    ]
   },
   {
@@ -80,7 +90,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "dense block 将每次的卷积的输出称为 `growth_rate`，因为如果输入是 `in_channel`，有 n 层，那么输出就是 `in_channel + n * growh_rate`"
+    "Dense Block 将每次的卷积的输出称为 `growth_rate`，因为如果输入是 `in_channel`，有 n 层，那么输出就是 `in_channel + n * growh_rate`"
    ]
   },
   {
@@ -212,6 +222,8 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
+    "## 2. DenseNet\n",
+    "\n",
     "最后我们定义 DenseNet"
    ]
   },
@@ -364,11 +376,20 @@
    "source": [
     "DenseNet 将残差连接改为了特征拼接，使得网络有了更稠密的连接"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 参考\n",
+    "* [DenseNet算法详解](https://blog.csdn.net/u014380165/article/details/75142664)\n",
+    "* [DenseNet详解](https://zhuanlan.zhihu.com/p/43057737)"
+   ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },
@@ -382,7 +403,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.7"
+   "version": "3.7.9"
   }
  },
  "nbformat": 4,

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

@@ -28,16 +28,16 @@
    "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",
+    "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。但是对于很深的网络结构，网络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。"
+    "所以在 2015 年一篇论文提出了这个方法，批标准化(batch normalization)，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",
+    "批标准化的实现非常简单，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",
@@ -57,7 +57,11 @@
    "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"
+    "* 第一和第二个公式计算出一个 batch 中数据的均值和方差\n",
+    "* 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 $10^{-5}$\n",
+    "* 最后利用权重修正得到最后的输出结果，其中 $\\gamma$ $\\beta$是权值变换参数，也是网络参数在训练过程一起学习\n",
+    "\n",
+    "下面演示一维的情况，也就是神经网络中的情况"
    ]
   },
   {
@@ -159,7 +163,7 @@
     "\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",
+    "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"
    ]
@@ -176,7 +180,9 @@
    },
    "outputs": [],
    "source": [
-    "def batch_norm_1d(x, gamma, beta, is_training, moving_mean, moving_var, moving_momentum=0.1):\n",
+    "def batch_norm_1d(x, gamma, beta, \n",
+    "                  is_training, \n",
+    "                  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",
@@ -193,7 +199,7 @@
    "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"
+    "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"
    ]
   },
   {
@@ -230,8 +236,11 @@
     "    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",
+    "# 重新载入数据集，申明定义的数据变换\n",
+    "train_set = mnist.MNIST('../../data/mnist', train=True,  \n",
+    "                        transform=data_tf, download=True) \n",
+    "test_set  = mnist.MNIST('../../data/mnist', train=False, \n",
+    "                        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)"
    ]
@@ -259,7 +268,8 @@
     "        \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 = batch_norm_1d(x, self.gamma, self.beta, \n",
+    "                          is_train, self.moving_mean, self.moving_var)\n",
     "        x = self.relu(x)\n",
     "        x = self.layer2(x)\n",
     "        return x"
@@ -293,7 +303,7 @@
    "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"
+    "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。"
    ]
   },
   {
@@ -591,7 +601,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.4"
+   "version": "3.7.9"
   }
  },
  "nbformat": 4,

7_deep_learning/1_CNN/7-lr-decay.ipynb

@@ -12,9 +12,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "这个时候就需要对学习率进行衰减已达到 loss 的充分下降，而是用学习率衰减的办法能够解决这个矛盾，学习率衰减就是随着训练的进行不断的减小学习率。\n",
+    "这个时候就需要对学习率进行衰减已达到 loss 的充分下降，用学习率衰减的办法能够解决这个矛盾，学习率衰减就是随着训练的进行不断的减小学习率。\n",
     "\n",
-    "在 PyTorch 中学习率衰减非常方便，使用 `torch.optim.lr_scheduler`，更多的信息可以直接查看[文档](http://pytorch.org/docs/0.3.0/optim.html#how-to-adjust-learning-rate)\n",
+    "在 PyTorch 中学习率衰减非常方便，使用 `torch.optim.lr_scheduler`，更多的信息可以直接查看[文档](https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate)\n",
     "\n",
     "推荐大家使用下面这种方式来做学习率衰减，更加直观，下面举例子来说明"
    ]
@@ -65,7 +65,11 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "这里定义好了模型和优化器，可以通过 `optimizer.param_groups` 来得到所有的参数组和其对应的属性，参数组是什么意思呢？就是将模型的参数分成几个组，每个组定义一个学习率。这个参数组是一个字典，里面有很多属性，比如学习率，权重衰减等等，可以通过如下方式访问属性值："
+    "这里定义好了模型和优化器，可以通过 `optimizer.param_groups` 来得到所有的参数组和其对应的属性。\n",
+    "* 参数组将模型的参数分成几个组，每个组定义一个学习率。\n",
+    "* 这个参数组是一个字典，里面有很多属性，比如学习率，权重衰减等等\n",
+    "\n",
+    "可以通过如下方式访问属性值："
    ]
   },
   {
@@ -274,9 +278,11 @@
     "        else:\n",
     "            im = Variable(im)\n",
     "            label = Variable(label)\n",
+    "        \n",
     "        # forward\n",
     "        output = net(im)\n",
     "        loss = criterion(output, label)\n",
+    "        \n",
     "        # backward\n",
     "        optimizer.zero_grad()\n",
     "        loss.backward()\n",
@@ -397,7 +403,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.4"
+   "version": "3.7.9"
   }
  },
  "nbformat": 4,

7_deep_learning/1_CNN/README.md

@@ -1,4 +1,12 @@
-## References
+# 卷积神经网络
+
+卷积神经网络的发展历程
+
+![resnet-development.png](images/resnet-development.png)
+
+
+
+## 参考
 
 * [一文搞懂CNN中的卷积和反卷积](https://www.toutiao.com/i6642655643314422275/)
 * [卷积可视化github](https://github.com/vdumoulin/conv_arithmetic)

7_deep_learning/1_CNN/images/residual-block.svg

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