985 lines
57 KiB
Plaintext
985 lines
57 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"'1.0.0'"
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]
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},
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"execution_count": 1,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"import torch\n",
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"torch.__version__"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# 2.4 卷积神经网络简介\n",
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"卷积神经网络由一个或多个卷积层和顶端的全连通层（也可以使用1x1的卷积层作为最终的输出）组成一种前馈神经网络。一般的认为，卷积神经网络是由Yann LeCun大神在1989年提出的LeNet中首先被使用，但是由于当时的计算能力不够，并没有得到广泛的应用，到了1998年Yann LeCun及其合作者构建了更加完备的卷积神经网络LeNet-5并在手写数字的识别问题中取得成功，LeNet-5的成功使卷积神经网络的应用得到关注。LeNet-5沿用了LeCun (1989) 的学习策略并在原有设计中加入了池化层对输入特征进行筛选 。LeNet-5基本上定义了现代卷积神经网络的基本结构，其构筑中交替出现的卷积层-池化层被认为有效提取了输入图像的平移不变特征，使得对于特征的提取前进了一大步，所以我们一般的认为，Yann LeCun是卷积神经网络的创始人。\n",
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"\n",
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"2006年后，随着深度学习理论的完善，尤其是计算能力的提升和参数微调（fine-tuning）等技术的出现，卷积神经网络开始快速发展，在结构上不断加深，各类学习和优化理论得到引入，2012年的AlexNet、2014年的VGGNet、GoogLeNet 和2015年的ResNet,使得卷积神经网络几乎成为了深度学习中图像处理方面的标配。\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 2.4.1 为什么要用卷积神经网络\n",
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"对于计算机视觉来说，每一个图像是由一个个像素点构成，每个像素点有三个通道，分别代表RGB三种颜色(不计算透明度)，我们以手写识别的数据集MNIST举例，每个图像的是一个长宽均为28，channel为1的单色图像，如果使用全连接的网络结构，即，网络中的神经与相邻层上的每个神经元均连接，那就意味着我们的网络有28 * 28 =784个神经元（RGB3色的话还要*3），hidden层如果使用了15个神经元，需要的参数个数(w和b)就有：28 * 28 * 15 * 10 + 15 + 10=117625个，这个数量级到现在为止也是一个很恐怖的数量级，一次反向传播计算量都是巨大的，这还展示一个单色的28像素大小的图片，如果我们使用更大的像素，计算量可想而知。"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 2.4.2结构组成\n",
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"上面说到传统的网络需要大量的参数，但是这些参数是否重复了呢，例如，我们识别一个人，只要看到他的眼睛，鼻子，嘴，还有脸基本上就知道这个人是谁了，只是用这些局部的特征就能做做判断了，并不需要所有的特征。\n",
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"另外一点就是我们上面说的可以有效提取了输入图像的平移不变特征，就好像我们看到了这是个眼睛，这个眼镜在左边还是在右边他都是眼睛，这就是平移不变性。\n",
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"我们通过卷积的计算操作来提取图像局部的特征，每一层都会计算出一些局部特征，这些局部特征再汇总到下一层，这样一层一层的传递下去，特征由小变大，最后在通过这些局部的特征对图片进行处理，这样大大提高了计算效率，也提高了准确度。\n",
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"### 卷积层\n",
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"#### 卷积计算\n",
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"在介绍卷积层之前要先介绍一下卷积的计算，这里使用[知乎](https://www.zhihu.com/question/39022858)上的一张图片\n",
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"\n",
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"我们会定义一个权重矩阵，也就是我们说的W（一般对于卷积来说，称作卷积的核kernel也有有人称做过滤器filter），这个权重矩阵的大小一般为`3 * 3` 或者`5 * 5`，但是在LeNet里面还用到了比较大的`7 * 7`，现在已经很少见了，因为根据经验的验证，3和5是最佳的大小。\n",
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"我们以图上所示的方式，我们在输入矩阵上使用我们的权重矩阵进行滑动，每滑动一步，将所覆盖的值与矩阵对应的值相乘，并将结果求和并作为输出矩阵的一项，依次类推直到全部计算完成。\n",
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"\n",
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"上图所示，我们输入是一个 `5 * 5`的矩阵，通过使用一次`3 * 3`的卷积核计算得到的计算结果是一个`3 * 3`的新矩阵。\n",
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"那么新矩阵的大小是如何计算的呢？\n",
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"#### 卷积核大小 f\n",
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"刚才已经说到了一个重要的参数，就是核的大小，我们这里用f来表示\n",
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"\n",
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"#### 边界填充 (p)adding\n",
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"我们看到上图，经过计算后矩阵的大小改变了，如果要使矩阵大小不改变呢，我们可以先对矩阵做一个填充，将矩阵的周围全部再包围一层，这个矩阵就变成了`7*7`,上下左右各加1，相当于 `5+1+1=7` 这时，计算的结果还是 `5 * 5`的矩阵，保证了大小不变，这里的p=1\n",
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"\n",
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"#### 步长 (s)tride\n",
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"从动图上我们能够看到，每次滑动只是滑动了一个距离，如果每次滑动两个距离呢？那就需要使用步长这个参数。\n",
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"\n",
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"#### 计算公式\n",
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"\n",
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"n为我们输入的矩阵的大小，$ \\frac{n-f+2p}{s} +1 $ 向下取整\n",
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"\n",
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"这个公式非常重要一定要记住\n",
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"\n",
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"#### 卷积层\n",
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"在每一个卷积层中我们都会设置多个核，每个核代表着不同的特征，这些特征就是我们需要传递到下一层的输出，而我们训练的过程就是训练这些不同的核。\n",
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"\n",
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"### 激活函数\n",
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||
"由于卷积的操作也是线性的，所以也需要进行激活，一般情况下，都会使用relu。\n",
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"\n",
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||
"### 池化层（pooling）\n",
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"池化层是CNN的重要组成部分，通过减少卷积层之间的连接，降低运算复杂程度，池化层的操作很简单，就想相当于是合并，我们输入一个过滤器的大小，与卷积的操作一样，也是一步一步滑动，但是过滤器覆盖的区域进行合并，只保留一个值。\n",
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||
"合并的方式也有很多种，例如我们常用的两种取最大值maxpooling，取平均值avgpooling\n",
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||
"\n",
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||
"池化层的输出大小公式也与卷积层一样，由于没有进行填充，所以p=0，可以简化为\n",
|
||
"$ \\frac{n-f}{s} +1 $\n",
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"\n",
|
||
"### dropout层\n",
|
||
"dropout是2014年 Hinton 提出防止过拟合而采用的trick，增强了模型的泛化能力\n",
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"Dropout（随机失活）是指在深度学习网络的训练过程中，按照一定的概率将一部分神经网络单元暂时从网络中丢弃，相当于从原始的网络中找到一个更瘦的网络，说的通俗一点，就是随机将一部分网络的传播掐断，听起来好像不靠谱，但是通过实际测试效果非常好。\n",
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||
"有兴趣的可以去看一下原文[Dropout: A Simple Way to Prevent Neural Networks from Overfitting](http://jmlr.org/papers/v15/srivastava14a.html)这里就不详细介绍了。\n",
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"\n",
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||
"### 全连接层\n",
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||
"全链接层一般是作为最后的输出层使用，卷积的作用是提取图像的特征，最后的全连接层就是要通过这些特征来进行计算，输出我们所要的结果了，无论是分类，还是回归。\n",
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||
"\n",
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||
"我们的特征都是使用矩阵表示的，所以再传入全连接层之前还需要对特征进行压扁，将他这些特征变成一维的向量，如果要进行分类的话，就是用sofmax作为输出，如果要是回归的话就直接使用linear即可。"
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||
]
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||
},
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||
{
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||
"cell_type": "markdown",
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||
"metadata": {},
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||
"source": [
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||
"以上就是卷积神经网络几个主要的组成部分，下面我们介绍一些经典的网络模型\n",
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||
"## 2.4.3 经典模型"
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||
]
|
||
},
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||
{
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||
"cell_type": "markdown",
|
||
"metadata": {},
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||
"source": [
|
||
"### LeNet-5\n",
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||
"1998， Yann LeCun 的 LeNet5 [官网](http://yann.lecun.com/exdb/lenet/index.html)\n",
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"\n",
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||
"卷积神经网路的开山之作，麻雀虽小，但五脏俱全，卷积层、pooling层、全连接层，这些都是现代CNN网络的基本组件\n",
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||
" - 用卷积提取空间特征；\n",
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||
" - 由空间平均得到子样本；\n",
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||
" - 用 tanh 或 sigmoid 得到非线性；\n",
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||
" - 用 multi-layer neural network（MLP）作为最终分类器；\n",
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||
" - 层层之间用稀疏的连接矩阵，以避免大的计算成本。\n",
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||
"\n",
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||
"\n",
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||
"输入：图像Size为32*32。这要比mnist数据库中最大的字母(28*28)还大。这样做的目的是希望潜在的明显特征，如笔画断续、角点能够出现在最高层特征监测子感受野的中心。\n",
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||
"\n",
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||
"输出：10个类别，分别为0-9数字的概率\n",
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||
"\n",
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||
"1. C1层是一个卷积层，有6个卷积核（提取6种局部特征），核大小为5 * 5\n",
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"2. S2层是pooling层，下采样（区域:2 * 2 ）降低网络训练参数及模型的过拟合程度。\n",
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||
"3. C3层是第二个卷积层，使用16个卷积核，核大小:5 * 5 提取特征\n",
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"4. S4层也是一个pooling层，区域:2*2\n",
|
||
"5. C5层是最后一个卷积层，卷积核大小:5 * 5 卷积核种类:120\n",
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||
"6. 最后使用全连接层，将C5的120个特征进行分类，最后输出0-9的概率\n",
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"\n",
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||
"以下代码来自[官方教程](https://pytorch.org/tutorials/beginner/blitz/neural_networks_tutorial.html)"
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]
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||
},
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{
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||
"cell_type": "code",
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||
"execution_count": 2,
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||
"metadata": {},
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||
"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"LeNet5(\n",
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" (conv1): Conv2d(1, 6, kernel_size=(5, 5), stride=(1, 1))\n",
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" (conv2): Conv2d(6, 16, kernel_size=(5, 5), stride=(1, 1))\n",
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" (fc1): Linear(in_features=400, out_features=120, bias=True)\n",
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" (fc2): Linear(in_features=120, out_features=84, bias=True)\n",
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" (fc3): Linear(in_features=84, out_features=10, bias=True)\n",
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")\n"
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]
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||
}
|
||
],
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||
"source": [
|
||
"import torch.nn as nn\n",
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"class LeNet5(nn.Module):\n",
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"\n",
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" def __init__(self):\n",
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" super(LeNet5, self).__init__()\n",
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" # 1 input image channel, 6 output channels, 5x5 square convolution\n",
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" # kernel\n",
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" self.conv1 = nn.Conv2d(1, 6, 5)\n",
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" self.conv2 = nn.Conv2d(6, 16, 5)\n",
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" # an affine operation: y = Wx + b\n",
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" self.fc1 = nn.Linear(16 * 5 * 5, 120) # 这里论文上写的是conv,官方教程用了线性层\n",
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" self.fc2 = nn.Linear(120, 84)\n",
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" self.fc3 = nn.Linear(84, 10)\n",
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"\n",
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" def forward(self, x):\n",
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" # Max pooling over a (2, 2) window\n",
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" x = F.max_pool2d(F.relu(self.conv1(x)), (2, 2))\n",
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" # If the size is a square you can only specify a single number\n",
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" x = F.max_pool2d(F.relu(self.conv2(x)), 2)\n",
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" x = x.view(-1, self.num_flat_features(x))\n",
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" x = F.relu(self.fc1(x))\n",
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" x = F.relu(self.fc2(x))\n",
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" x = self.fc3(x)\n",
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" return x\n",
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"\n",
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" def num_flat_features(self, x):\n",
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" size = x.size()[1:] # all dimensions except the batch dimension\n",
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" num_features = 1\n",
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" for s in size:\n",
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" num_features *= s\n",
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" return num_features\n",
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"\n",
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"\n",
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"net = LeNet5()\n",
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"print(net)"
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||
]
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||
},
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||
{
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||
"cell_type": "markdown",
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||
"metadata": {},
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||
"source": [
|
||
"### AlexNet\n",
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"2012，Alex Krizhevsky\n",
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"可以算作LeNet的一个更深和更广的版本，可以用来学习更复杂的对象 [论文](https://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf)\n",
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||
" - 用rectified linear units（ReLU）得到非线性；\n",
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||
" - 使用 dropout 技巧在训练期间有选择性地忽略单个神经元，来减缓模型的过拟合；\n",
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||
" - 重叠最大池，避免平均池的平均效果；\n",
|
||
" - 使用 GPU NVIDIA GTX 580 可以减少训练时间，这比用CPU处理快了 10 倍，所以可以被用于更大的数据集和图像上。\n",
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"\n",
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||
"虽然 AlexNet只有8层，但是它有60M以上的参数总量，Alexnet有一个特殊的计算层，LRN层，做的事是对当前层的输出结果做平滑处理，这里就不做详细介绍了，\n",
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||
"Alexnet的每一阶段（含一次卷积主要计算的算作一层）可以分为8层：\n",
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||
"1. con - relu - pooling - LRN ：\n",
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||
"要注意的是input层是227*227，而不是paper里面的224，这里可以算一下，主要是227可以整除后面的conv1计算，224不整除。如果一定要用224可以通过自动补边实现，不过在input就补边感觉没有意义，补得也是0，这就是我们上面说的公式的重要性。\n",
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"\n",
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"2. conv - relu - pool - LRN ：\n",
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"group=2，这个属性强行把前面结果的feature map分开，卷积部分分成两部分做\n",
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"\n",
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"3. conv - relu\n",
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"\n",
|
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"4. conv - relu\n",
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||
"\n",
|
||
"5. conv - relu - pool\n",
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||
"\n",
|
||
"6. fc - relu - dropout ：\n",
|
||
"dropout层，在alexnet中是说在训练的以1/2概率使得隐藏层的某些neuron的输出为0，这样就丢到了一半节点的输出，BP的时候也不更新这些节点，防止过拟合。\n",
|
||
"\n",
|
||
"7. fc - relu - dropout \n",
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||
"\n",
|
||
"8. fc - softmax \n",
|
||
"\n",
|
||
"在Pytorch的vision包中是包含Alexnet的官方实现的，我们直接使用官方版本看下网络"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 3,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"AlexNet(\n",
|
||
" (features): Sequential(\n",
|
||
" (0): Conv2d(3, 64, kernel_size=(11, 11), stride=(4, 4), padding=(2, 2))\n",
|
||
" (1): ReLU(inplace)\n",
|
||
" (2): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
|
||
" (3): Conv2d(64, 192, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))\n",
|
||
" (4): ReLU(inplace)\n",
|
||
" (5): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
|
||
" (6): Conv2d(192, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
|
||
" (7): ReLU(inplace)\n",
|
||
" (8): Conv2d(384, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
|
||
" (9): ReLU(inplace)\n",
|
||
" (10): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
|
||
" (11): ReLU(inplace)\n",
|
||
" (12): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
|
||
" )\n",
|
||
" (classifier): Sequential(\n",
|
||
" (0): Dropout(p=0.5)\n",
|
||
" (1): Linear(in_features=9216, out_features=4096, bias=True)\n",
|
||
" (2): ReLU(inplace)\n",
|
||
" (3): Dropout(p=0.5)\n",
|
||
" (4): Linear(in_features=4096, out_features=4096, bias=True)\n",
|
||
" (5): ReLU(inplace)\n",
|
||
" (6): Linear(in_features=4096, out_features=1000, bias=True)\n",
|
||
" )\n",
|
||
")\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"import torchvision\n",
|
||
"model = torchvision.models.alexnet(pretrained=False) #我们不下载预训练权重\n",
|
||
"print(model)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### VGG\n",
|
||
"2015，牛津的 VGG。[论文](https://arxiv.org/pdf/1409.1556.pdf)\n",
|
||
"\n",
|
||
" - 每个卷积层中使用更小的 3×3 filters，并将它们组合成卷积序列\n",
|
||
" - 多个3×3卷积序列可以模拟更大的接收场的效果\n",
|
||
" - 每次的图像像素缩小一倍，卷积核的数量增加一倍\n",
|
||
" \n",
|
||
"VGG有很多个版本，也算是比较稳定和经典的model。它的特点也是连续conv多计算量巨大，这里我们以VGG16为例.[图片来源](https://www.cs.toronto.edu/~frossard/post/vgg16/)\n",
|
||
" \n",
|
||
"VGG清一色用小卷积核，结合作者和自己的观点，这里整理出小卷积核比用大卷积核的优势：\n",
|
||
"\n",
|
||
"根据作者的观点，input8 -> 3层conv3x3后，output=2，等同于1层conv7x7的结果； input=8 -> 2层conv3x3后，output=2，等同于2层conv5x5的结果\n",
|
||
"\n",
|
||
"卷积层的参数减少。相比5x5、7x7和11x11的大卷积核，3x3明显地减少了参数量\n",
|
||
"\n",
|
||
"通过卷积和池化层后，图像的分辨率降低为原来的一半，但是图像的特征增加一倍，这是一个十分规整的操作:\n",
|
||
"分辨率由输入的224->112->56->28->14->7，\n",
|
||
"特征从原始的RGB3个通道-> 64 ->128 -> 256 -> 512\n",
|
||
"\n",
|
||
"这为后面的网络提供了一个标准，我们依旧使用Pytorch官方实现版本来查看"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 4,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"VGG(\n",
|
||
" (features): Sequential(\n",
|
||
" (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
|
||
" (1): ReLU(inplace)\n",
|
||
" (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
|
||
" (3): ReLU(inplace)\n",
|
||
" (4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
|
||
" (5): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
|
||
" (6): ReLU(inplace)\n",
|
||
" (7): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
|
||
" (8): ReLU(inplace)\n",
|
||
" (9): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
|
||
" (10): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
|
||
" (11): ReLU(inplace)\n",
|
||
" (12): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
|
||
" (13): ReLU(inplace)\n",
|
||
" (14): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
|
||
" (15): ReLU(inplace)\n",
|
||
" (16): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
|
||
" (17): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
|
||
" (18): ReLU(inplace)\n",
|
||
" (19): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
|
||
" (20): ReLU(inplace)\n",
|
||
" (21): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
|
||
" (22): ReLU(inplace)\n",
|
||
" (23): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
|
||
" (24): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
|
||
" (25): ReLU(inplace)\n",
|
||
" (26): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
|
||
" (27): ReLU(inplace)\n",
|
||
" (28): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
|
||
" (29): ReLU(inplace)\n",
|
||
" (30): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
|
||
" )\n",
|
||
" (classifier): Sequential(\n",
|
||
" (0): Linear(in_features=25088, out_features=4096, bias=True)\n",
|
||
" (1): ReLU(inplace)\n",
|
||
" (2): Dropout(p=0.5)\n",
|
||
" (3): Linear(in_features=4096, out_features=4096, bias=True)\n",
|
||
" (4): ReLU(inplace)\n",
|
||
" (5): Dropout(p=0.5)\n",
|
||
" (6): Linear(in_features=4096, out_features=1000, bias=True)\n",
|
||
" )\n",
|
||
")\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"import torchvision\n",
|
||
"model = torchvision.models.vgg16(pretrained=False) #我们不下载预训练权重\n",
|
||
"print(model)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### GoogLeNet (Inception)\n",
|
||
"2014，Google Christian Szegedy [论文](https://arxiv.org/abs/1512.00567)\n",
|
||
"- 使用1×1卷积块（NiN）来减少特征数量，这通常被称为“瓶颈”，可以减少深层神经网络的计算负担。\n",
|
||
"- 每个池化层之前，增加 feature maps，增加每一层的宽度来增多特征的组合性\n",
|
||
"\n",
|
||
"googlenet最大的特点就是包含若干个inception模块，所以有时候也称作 inception net。\n",
|
||
"googlenet虽然层数要比VGG多很多，但是由于inception的设计，计算速度方面要快很多。\n",
|
||
"\n",
|
||
"\n",
|
||
"\n",
|
||
"不要被这个图吓到，其实原理很简单\n",
|
||
"\n",
|
||
"Inception架构的主要思想是找出如何让已有的稠密组件接近与覆盖卷积视觉网络中的最佳局部稀疏结构。现在需要找出最优的局部构造，并且重复几次。之前的一篇文献提出一个层与层的结构，在最后一层进行相关性统计，将高相关性的聚集到一起。这些聚类构成下一层的单元，且与上一层单元连接。假设前面层的每个单元对应于输入图像的某些区域，这些单元被分为滤波器组。在接近输入层的低层中，相关单元集中在某些局部区域，最终得到在单个区域中的大量聚类，在最后一层通过1x1的卷积覆盖。\n",
|
||
"\n",
|
||
"上面的话听起来很生硬，其实解释起来很简单：每一模块我们都是用若干个不同的特征提取方式，例如 3x3卷积，5x5卷积，1x1的卷积，pooling等，都计算一下，最后再把这些结果通过Filter Concat来进行连接，找到这里面作用最大的。而网络里面包含了许多这样的模块，这样不用我们人为去判断哪个特征提取方式好，网络会自己解决（是不是有点像AUTO ML），在Pytorch中实现了InceptionA-E，还有InceptionAUX 模块。\n",
|
||
"\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 5,
|
||
"metadata": {
|
||
"scrolled": true
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Inception3(\n",
|
||
" (Conv2d_1a_3x3): BasicConv2d(\n",
|
||
" (conv): Conv2d(3, 32, kernel_size=(3, 3), stride=(2, 2), bias=False)\n",
|
||
" (bn): BatchNorm2d(32, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||
" )\n",
|
||
" (Conv2d_2a_3x3): BasicConv2d(\n",
|
||
" (conv): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), bias=False)\n",
|
||
" (bn): BatchNorm2d(32, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||
" )\n",
|
||
" (Conv2d_2b_3x3): BasicConv2d(\n",
|
||
" (conv): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
|
||
" (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||
" )\n",
|
||
" (Conv2d_3b_1x1): BasicConv2d(\n",
|
||
" (conv): Conv2d(64, 80, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
||
" (bn): BatchNorm2d(80, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||
" )\n",
|
||
" (Conv2d_4a_3x3): BasicConv2d(\n",
|
||
" (conv): Conv2d(80, 192, kernel_size=(3, 3), stride=(1, 1), bias=False)\n",
|
||
" (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||
" )\n",
|
||
" (Mixed_5b): InceptionA(\n",
|
||
" (branch1x1): BasicConv2d(\n",
|
||
" (conv): Conv2d(192, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
||
" (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||
" )\n",
|
||
" (branch5x5_1): BasicConv2d(\n",
|
||
" (conv): Conv2d(192, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
||
" (bn): BatchNorm2d(48, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||
" )\n",
|
||
" (branch5x5_2): BasicConv2d(\n",
|
||
" (conv): Conv2d(48, 64, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), bias=False)\n",
|
||
" (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||
" )\n",
|
||
" (branch3x3dbl_1): BasicConv2d(\n",
|
||
" (conv): Conv2d(192, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
||
" (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||
" )\n",
|
||
" (branch3x3dbl_2): BasicConv2d(\n",
|
||
" (conv): Conv2d(64, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
|
||
" (bn): BatchNorm2d(96, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||
" )\n",
|
||
" (branch3x3dbl_3): BasicConv2d(\n",
|
||
" (conv): Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
|
||
" (bn): BatchNorm2d(96, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||
" )\n",
|
||
" (branch_pool): BasicConv2d(\n",
|
||
" (conv): Conv2d(192, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
||
" (bn): BatchNorm2d(32, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||
" )\n",
|
||
" )\n",
|
||
" (Mixed_5c): InceptionA(\n",
|
||
" (branch1x1): BasicConv2d(\n",
|
||
" (conv): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
||
" (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||
" )\n",
|
||
" (branch5x5_1): BasicConv2d(\n",
|
||
" (conv): Conv2d(256, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
||
" (bn): BatchNorm2d(48, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||
" )\n",
|
||
" (branch5x5_2): BasicConv2d(\n",
|
||
" (conv): Conv2d(48, 64, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), bias=False)\n",
|
||
" (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||
" )\n",
|
||
" (branch3x3dbl_1): BasicConv2d(\n",
|
||
" (conv): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
||
" (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||
" )\n",
|
||
" (branch3x3dbl_2): BasicConv2d(\n",
|
||
" (conv): Conv2d(64, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
|
||
" (bn): BatchNorm2d(96, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||
" )\n",
|
||
" (branch3x3dbl_3): BasicConv2d(\n",
|
||
" (conv): Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
|
||
" (bn): BatchNorm2d(96, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||
" )\n",
|
||
" (branch_pool): BasicConv2d(\n",
|
||
" (conv): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
||
" (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||
" )\n",
|
||
" )\n",
|
||
" (Mixed_5d): InceptionA(\n",
|
||
" (branch1x1): BasicConv2d(\n",
|
||
" (conv): Conv2d(288, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
||
" (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||
" )\n",
|
||
" (branch5x5_1): BasicConv2d(\n",
|
||
" (conv): Conv2d(288, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
||
" (bn): BatchNorm2d(48, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||
" )\n",
|
||
" (branch5x5_2): BasicConv2d(\n",
|
||
" (conv): Conv2d(48, 64, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), bias=False)\n",
|
||
" (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||
" )\n",
|
||
" (branch3x3dbl_1): BasicConv2d(\n",
|
||
" (conv): Conv2d(288, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
||
" (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||
" )\n",
|
||
" (branch3x3dbl_2): BasicConv2d(\n",
|
||
" (conv): Conv2d(64, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
|
||
" (bn): BatchNorm2d(96, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||
" )\n",
|
||
" (branch3x3dbl_3): BasicConv2d(\n",
|
||
" (conv): Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
|
||
" (bn): BatchNorm2d(96, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||
" )\n",
|
||
" (branch_pool): BasicConv2d(\n",
|
||
" (conv): Conv2d(288, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
||
" (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||
" )\n",
|
||
" )\n",
|
||
" (Mixed_6a): InceptionB(\n",
|
||
" (branch3x3): BasicConv2d(\n",
|
||
" (conv): Conv2d(288, 384, kernel_size=(3, 3), stride=(2, 2), bias=False)\n",
|
||
" (bn): BatchNorm2d(384, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||
" )\n",
|
||
" (branch3x3dbl_1): BasicConv2d(\n",
|
||
" (conv): Conv2d(288, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
||
" (bn): BatchNorm2d(64, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||
" )\n",
|
||
" (branch3x3dbl_2): BasicConv2d(\n",
|
||
" (conv): Conv2d(64, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
|
||
" (bn): BatchNorm2d(96, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||
" )\n",
|
||
" (branch3x3dbl_3): BasicConv2d(\n",
|
||
" (conv): Conv2d(96, 96, kernel_size=(3, 3), stride=(2, 2), bias=False)\n",
|
||
" (bn): BatchNorm2d(96, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||
" )\n",
|
||
" )\n",
|
||
" (Mixed_6b): InceptionC(\n",
|
||
" (branch1x1): BasicConv2d(\n",
|
||
" (conv): Conv2d(768, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
||
" (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||
" )\n",
|
||
" (branch7x7_1): BasicConv2d(\n",
|
||
" (conv): Conv2d(768, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
||
" (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||
" )\n",
|
||
" (branch7x7_2): BasicConv2d(\n",
|
||
" (conv): Conv2d(128, 128, kernel_size=(1, 7), stride=(1, 1), padding=(0, 3), bias=False)\n",
|
||
" (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||
" )\n",
|
||
" (branch7x7_3): BasicConv2d(\n",
|
||
" (conv): Conv2d(128, 192, kernel_size=(7, 1), stride=(1, 1), padding=(3, 0), bias=False)\n",
|
||
" (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||
" )\n",
|
||
" (branch7x7dbl_1): BasicConv2d(\n",
|
||
" (conv): Conv2d(768, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
||
" (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||
" )\n",
|
||
" (branch7x7dbl_2): BasicConv2d(\n",
|
||
" (conv): Conv2d(128, 128, kernel_size=(7, 1), stride=(1, 1), padding=(3, 0), bias=False)\n",
|
||
" (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||
" )\n",
|
||
" (branch7x7dbl_3): BasicConv2d(\n",
|
||
" (conv): Conv2d(128, 128, kernel_size=(1, 7), stride=(1, 1), padding=(0, 3), bias=False)\n",
|
||
" (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||
" )\n",
|
||
" (branch7x7dbl_4): BasicConv2d(\n",
|
||
" (conv): Conv2d(128, 128, kernel_size=(7, 1), stride=(1, 1), padding=(3, 0), bias=False)\n",
|
||
" (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||
" )\n",
|
||
" (branch7x7dbl_5): BasicConv2d(\n",
|
||
" (conv): Conv2d(128, 192, kernel_size=(1, 7), stride=(1, 1), padding=(0, 3), bias=False)\n",
|
||
" (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||
" )\n",
|
||
" (branch_pool): BasicConv2d(\n",
|
||
" (conv): Conv2d(768, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
||
" (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||
" )\n",
|
||
" )\n",
|
||
" (Mixed_6c): InceptionC(\n",
|
||
" (branch1x1): BasicConv2d(\n",
|
||
" (conv): Conv2d(768, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
||
" (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||
" )\n",
|
||
" (branch7x7_1): BasicConv2d(\n",
|
||
" (conv): Conv2d(768, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
||
" (bn): BatchNorm2d(160, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||
" )\n",
|
||
" (branch7x7_2): BasicConv2d(\n",
|
||
" (conv): Conv2d(160, 160, kernel_size=(1, 7), stride=(1, 1), padding=(0, 3), bias=False)\n",
|
||
" (bn): BatchNorm2d(160, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||
" )\n",
|
||
" (branch7x7_3): BasicConv2d(\n",
|
||
" (conv): Conv2d(160, 192, kernel_size=(7, 1), stride=(1, 1), padding=(3, 0), bias=False)\n",
|
||
" (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||
" )\n",
|
||
" (branch7x7dbl_1): BasicConv2d(\n",
|
||
" (conv): Conv2d(768, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
||
" (bn): BatchNorm2d(160, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||
" )\n",
|
||
" (branch7x7dbl_2): BasicConv2d(\n",
|
||
" (conv): Conv2d(160, 160, kernel_size=(7, 1), stride=(1, 1), padding=(3, 0), bias=False)\n",
|
||
" (bn): BatchNorm2d(160, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||
" )\n",
|
||
" (branch7x7dbl_3): BasicConv2d(\n",
|
||
" (conv): Conv2d(160, 160, kernel_size=(1, 7), stride=(1, 1), padding=(0, 3), bias=False)\n",
|
||
" (bn): BatchNorm2d(160, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||
" )\n",
|
||
" (branch7x7dbl_4): BasicConv2d(\n",
|
||
" (conv): Conv2d(160, 160, kernel_size=(7, 1), stride=(1, 1), padding=(3, 0), bias=False)\n",
|
||
" (bn): BatchNorm2d(160, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||
" )\n",
|
||
" (branch7x7dbl_5): BasicConv2d(\n",
|
||
" (conv): Conv2d(160, 192, kernel_size=(1, 7), stride=(1, 1), padding=(0, 3), bias=False)\n",
|
||
" (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||
" )\n",
|
||
" (branch_pool): BasicConv2d(\n",
|
||
" (conv): Conv2d(768, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
||
" (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||
" )\n",
|
||
" )\n",
|
||
" (Mixed_6d): InceptionC(\n",
|
||
" (branch1x1): BasicConv2d(\n",
|
||
" (conv): Conv2d(768, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
||
" (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||
" )\n",
|
||
" (branch7x7_1): BasicConv2d(\n",
|
||
" (conv): Conv2d(768, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
||
" (bn): BatchNorm2d(160, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||
" )\n",
|
||
" (branch7x7_2): BasicConv2d(\n",
|
||
" (conv): Conv2d(160, 160, kernel_size=(1, 7), stride=(1, 1), padding=(0, 3), bias=False)\n",
|
||
" (bn): BatchNorm2d(160, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||
" )\n",
|
||
" (branch7x7_3): BasicConv2d(\n",
|
||
" (conv): Conv2d(160, 192, kernel_size=(7, 1), stride=(1, 1), padding=(3, 0), bias=False)\n",
|
||
" (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||
" )\n",
|
||
" (branch7x7dbl_1): BasicConv2d(\n",
|
||
" (conv): Conv2d(768, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
||
" (bn): BatchNorm2d(160, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||
" )\n",
|
||
" (branch7x7dbl_2): BasicConv2d(\n",
|
||
" (conv): Conv2d(160, 160, kernel_size=(7, 1), stride=(1, 1), padding=(3, 0), bias=False)\n",
|
||
" (bn): BatchNorm2d(160, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||
" )\n",
|
||
" (branch7x7dbl_3): BasicConv2d(\n",
|
||
" (conv): Conv2d(160, 160, kernel_size=(1, 7), stride=(1, 1), padding=(0, 3), bias=False)\n",
|
||
" (bn): BatchNorm2d(160, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||
" )\n",
|
||
" (branch7x7dbl_4): BasicConv2d(\n",
|
||
" (conv): Conv2d(160, 160, kernel_size=(7, 1), stride=(1, 1), padding=(3, 0), bias=False)\n",
|
||
" (bn): BatchNorm2d(160, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||
" )\n",
|
||
" (branch7x7dbl_5): BasicConv2d(\n",
|
||
" (conv): Conv2d(160, 192, kernel_size=(1, 7), stride=(1, 1), padding=(0, 3), bias=False)\n",
|
||
" (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||
" )\n",
|
||
" (branch_pool): BasicConv2d(\n",
|
||
" (conv): Conv2d(768, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
||
" (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||
" )\n",
|
||
" )\n",
|
||
" (Mixed_6e): InceptionC(\n",
|
||
" (branch1x1): BasicConv2d(\n",
|
||
" (conv): Conv2d(768, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
||
" (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||
" )\n",
|
||
" (branch7x7_1): BasicConv2d(\n",
|
||
" (conv): Conv2d(768, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
||
" (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||
" )\n",
|
||
" (branch7x7_2): BasicConv2d(\n",
|
||
" (conv): Conv2d(192, 192, kernel_size=(1, 7), stride=(1, 1), padding=(0, 3), bias=False)\n",
|
||
" (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||
" )\n",
|
||
" (branch7x7_3): BasicConv2d(\n",
|
||
" (conv): Conv2d(192, 192, kernel_size=(7, 1), stride=(1, 1), padding=(3, 0), bias=False)\n",
|
||
" (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||
" )\n",
|
||
" (branch7x7dbl_1): BasicConv2d(\n",
|
||
" (conv): Conv2d(768, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
||
" (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||
" )\n",
|
||
" (branch7x7dbl_2): BasicConv2d(\n",
|
||
" (conv): Conv2d(192, 192, kernel_size=(7, 1), stride=(1, 1), padding=(3, 0), bias=False)\n",
|
||
" (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||
" )\n",
|
||
" (branch7x7dbl_3): BasicConv2d(\n",
|
||
" (conv): Conv2d(192, 192, kernel_size=(1, 7), stride=(1, 1), padding=(0, 3), bias=False)\n",
|
||
" (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||
" )\n",
|
||
" (branch7x7dbl_4): BasicConv2d(\n",
|
||
" (conv): Conv2d(192, 192, kernel_size=(7, 1), stride=(1, 1), padding=(3, 0), bias=False)\n",
|
||
" (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||
" )\n",
|
||
" (branch7x7dbl_5): BasicConv2d(\n",
|
||
" (conv): Conv2d(192, 192, kernel_size=(1, 7), stride=(1, 1), padding=(0, 3), bias=False)\n",
|
||
" (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||
" )\n",
|
||
" (branch_pool): BasicConv2d(\n",
|
||
" (conv): Conv2d(768, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
||
" (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||
" )\n",
|
||
" )\n",
|
||
" (AuxLogits): InceptionAux(\n",
|
||
" (conv0): BasicConv2d(\n",
|
||
" (conv): Conv2d(768, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
||
" (bn): BatchNorm2d(128, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||
" )\n",
|
||
" (conv1): BasicConv2d(\n",
|
||
" (conv): Conv2d(128, 768, kernel_size=(5, 5), stride=(1, 1), bias=False)\n",
|
||
" (bn): BatchNorm2d(768, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||
" )\n",
|
||
" (fc): Linear(in_features=768, out_features=1000, bias=True)\n",
|
||
" )\n",
|
||
" (Mixed_7a): InceptionD(\n",
|
||
" (branch3x3_1): BasicConv2d(\n",
|
||
" (conv): Conv2d(768, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
||
" (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||
" )\n",
|
||
" (branch3x3_2): BasicConv2d(\n",
|
||
" (conv): Conv2d(192, 320, kernel_size=(3, 3), stride=(2, 2), bias=False)\n",
|
||
" (bn): BatchNorm2d(320, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||
" )\n",
|
||
" (branch7x7x3_1): BasicConv2d(\n",
|
||
" (conv): Conv2d(768, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
||
" (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||
" )\n",
|
||
" (branch7x7x3_2): BasicConv2d(\n",
|
||
" (conv): Conv2d(192, 192, kernel_size=(1, 7), stride=(1, 1), padding=(0, 3), bias=False)\n",
|
||
" (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||
" )\n",
|
||
" (branch7x7x3_3): BasicConv2d(\n",
|
||
" (conv): Conv2d(192, 192, kernel_size=(7, 1), stride=(1, 1), padding=(3, 0), bias=False)\n",
|
||
" (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||
" )\n",
|
||
" (branch7x7x3_4): BasicConv2d(\n",
|
||
" (conv): Conv2d(192, 192, kernel_size=(3, 3), stride=(2, 2), bias=False)\n",
|
||
" (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||
" )\n",
|
||
" )\n",
|
||
" (Mixed_7b): InceptionE(\n",
|
||
" (branch1x1): BasicConv2d(\n",
|
||
" (conv): Conv2d(1280, 320, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
||
" (bn): BatchNorm2d(320, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||
" )\n",
|
||
" (branch3x3_1): BasicConv2d(\n",
|
||
" (conv): Conv2d(1280, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
||
" (bn): BatchNorm2d(384, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||
" )\n",
|
||
" (branch3x3_2a): BasicConv2d(\n",
|
||
" (conv): Conv2d(384, 384, kernel_size=(1, 3), stride=(1, 1), padding=(0, 1), bias=False)\n",
|
||
" (bn): BatchNorm2d(384, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||
" )\n",
|
||
" (branch3x3_2b): BasicConv2d(\n",
|
||
" (conv): Conv2d(384, 384, kernel_size=(3, 1), stride=(1, 1), padding=(1, 0), bias=False)\n",
|
||
" (bn): BatchNorm2d(384, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||
" )\n",
|
||
" (branch3x3dbl_1): BasicConv2d(\n",
|
||
" (conv): Conv2d(1280, 448, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
||
" (bn): BatchNorm2d(448, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||
" )\n",
|
||
" (branch3x3dbl_2): BasicConv2d(\n",
|
||
" (conv): Conv2d(448, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
|
||
" (bn): BatchNorm2d(384, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||
" )\n",
|
||
" (branch3x3dbl_3a): BasicConv2d(\n",
|
||
" (conv): Conv2d(384, 384, kernel_size=(1, 3), stride=(1, 1), padding=(0, 1), bias=False)\n",
|
||
" (bn): BatchNorm2d(384, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||
" )\n",
|
||
" (branch3x3dbl_3b): BasicConv2d(\n",
|
||
" (conv): Conv2d(384, 384, kernel_size=(3, 1), stride=(1, 1), padding=(1, 0), bias=False)\n",
|
||
" (bn): BatchNorm2d(384, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||
" )\n",
|
||
" (branch_pool): BasicConv2d(\n",
|
||
" (conv): Conv2d(1280, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
||
" (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||
" )\n",
|
||
" )\n",
|
||
" (Mixed_7c): InceptionE(\n",
|
||
" (branch1x1): BasicConv2d(\n",
|
||
" (conv): Conv2d(2048, 320, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
||
" (bn): BatchNorm2d(320, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||
" )\n",
|
||
" (branch3x3_1): BasicConv2d(\n",
|
||
" (conv): Conv2d(2048, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
||
" (bn): BatchNorm2d(384, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||
" )\n",
|
||
" (branch3x3_2a): BasicConv2d(\n",
|
||
" (conv): Conv2d(384, 384, kernel_size=(1, 3), stride=(1, 1), padding=(0, 1), bias=False)\n",
|
||
" (bn): BatchNorm2d(384, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||
" )\n",
|
||
" (branch3x3_2b): BasicConv2d(\n",
|
||
" (conv): Conv2d(384, 384, kernel_size=(3, 1), stride=(1, 1), padding=(1, 0), bias=False)\n",
|
||
" (bn): BatchNorm2d(384, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||
" )\n",
|
||
" (branch3x3dbl_1): BasicConv2d(\n",
|
||
" (conv): Conv2d(2048, 448, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
||
" (bn): BatchNorm2d(448, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||
" )\n",
|
||
" (branch3x3dbl_2): BasicConv2d(\n",
|
||
" (conv): Conv2d(448, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
|
||
" (bn): BatchNorm2d(384, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||
" )\n",
|
||
" (branch3x3dbl_3a): BasicConv2d(\n",
|
||
" (conv): Conv2d(384, 384, kernel_size=(1, 3), stride=(1, 1), padding=(0, 1), bias=False)\n",
|
||
" (bn): BatchNorm2d(384, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||
" )\n",
|
||
" (branch3x3dbl_3b): BasicConv2d(\n",
|
||
" (conv): Conv2d(384, 384, kernel_size=(3, 1), stride=(1, 1), padding=(1, 0), bias=False)\n",
|
||
" (bn): BatchNorm2d(384, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||
" )\n",
|
||
" (branch_pool): BasicConv2d(\n",
|
||
" (conv): Conv2d(2048, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
||
" (bn): BatchNorm2d(192, eps=0.001, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||
" )\n",
|
||
" )\n",
|
||
" (fc): Linear(in_features=2048, out_features=1000, bias=True)\n",
|
||
")\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# inception_v3需要scipy，所以没有安装的话pip install scipy 一下\n",
|
||
"import torchvision\n",
|
||
"model = torchvision.models.inception_v3(pretrained=False) #我们不下载预训练权重\n",
|
||
"print(model)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### ResNet\n",
|
||
"2015，Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun [论文](https://arxiv.org/abs/1512.03385)\n",
|
||
"Kaiming He 何凯明（音译）这个大神大家一定要记住，现在很多论文都有他参与(mask rcnn, focal loss)，Jian Sun孙剑老师就不用说了，现在旷视科技的首席科学家。\n",
|
||
"刚才的GoogLeNet已经很深了，ResNet可以做到更深，通过残差计算，可以训练超过1000层的网络，俗称跳连接\n",
|
||
"\n",
|
||
"#### 退化问题\n",
|
||
"网络层数增加，但是在训练集上的准确率却饱和甚至下降了。这个不能解释为overfitting，因为overfit应该表现为在训练集上表现更好才对。这个就是网络退化的问题，退化问题说明了深度网络不能很简单地被很好地优化\n",
|
||
"\n",
|
||
"#### 残差网络的解决办法\n",
|
||
"深层网络的后面那些层是恒等映射，那么模型就退化为一个浅层网络。那现在要解决的就是学习恒等映射函数了。让一些层去拟合一个潜在的恒等映射函数H(x) = x，比较困难。如果把网络设计为H(x) = F(x) + x。我们可以转换为学习一个残差函数F(x) = H(x) - x。 只要F(x)=0，就构成了一个恒等映射H(x) = x. 而且，拟合残差肯定更加容易。\n",
|
||
"\n",
|
||
"以上又很不好理解，继续解释下，先看图：\n",
|
||
"\n",
|
||
"\n",
|
||
"我们在激活函数前将上一层（或几层）的输出与本层计算的输出相加，将求和的结果输入到激活函数中做为本层的输出，引入残差后的映射对输出的变化更敏感，其实就是看本层相对前几层是否有大的变化，相当于是一个差分放大器的作用。图中的曲线就是残差中的shoutcut，他将前一层的结果直接连接到了本层，也就是俗称的跳连接。\n",
|
||
"\n",
|
||
"我们以经典的resnet18来看一下网络结构 [图片来源](https://www.researchgate.net/figure/Proposed-Modified-ResNet-18-architecture-for-Bangla-HCR-In-the-diagram-conv-stands-for_fig1_323063171)\n",
|
||
""
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 6,
|
||
"metadata": {
|
||
"scrolled": true
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"ResNet(\n",
|
||
" (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
|
||
" (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||
" (relu): ReLU(inplace)\n",
|
||
" (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
|
||
" (layer1): Sequential(\n",
|
||
" (0): BasicBlock(\n",
|
||
" (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
|
||
" (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||
" (relu): ReLU(inplace)\n",
|
||
" (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
|
||
" (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||
" )\n",
|
||
" (1): BasicBlock(\n",
|
||
" (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
|
||
" (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||
" (relu): ReLU(inplace)\n",
|
||
" (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
|
||
" (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||
" )\n",
|
||
" )\n",
|
||
" (layer2): Sequential(\n",
|
||
" (0): BasicBlock(\n",
|
||
" (conv1): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
|
||
" (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||
" (relu): ReLU(inplace)\n",
|
||
" (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
|
||
" (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||
" (downsample): Sequential(\n",
|
||
" (0): Conv2d(64, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
|
||
" (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||
" )\n",
|
||
" )\n",
|
||
" (1): BasicBlock(\n",
|
||
" (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
|
||
" (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||
" (relu): ReLU(inplace)\n",
|
||
" (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
|
||
" (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||
" )\n",
|
||
" )\n",
|
||
" (layer3): Sequential(\n",
|
||
" (0): BasicBlock(\n",
|
||
" (conv1): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
|
||
" (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||
" (relu): ReLU(inplace)\n",
|
||
" (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
|
||
" (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||
" (downsample): Sequential(\n",
|
||
" (0): Conv2d(128, 256, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
|
||
" (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||
" )\n",
|
||
" )\n",
|
||
" (1): BasicBlock(\n",
|
||
" (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
|
||
" (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||
" (relu): ReLU(inplace)\n",
|
||
" (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
|
||
" (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||
" )\n",
|
||
" )\n",
|
||
" (layer4): Sequential(\n",
|
||
" (0): BasicBlock(\n",
|
||
" (conv1): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
|
||
" (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||
" (relu): ReLU(inplace)\n",
|
||
" (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
|
||
" (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||
" (downsample): Sequential(\n",
|
||
" (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
|
||
" (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||
" )\n",
|
||
" )\n",
|
||
" (1): BasicBlock(\n",
|
||
" (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
|
||
" (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||
" (relu): ReLU(inplace)\n",
|
||
" (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
|
||
" (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||
" )\n",
|
||
" )\n",
|
||
" (avgpool): AvgPool2d(kernel_size=7, stride=1, padding=0)\n",
|
||
" (fc): Linear(in_features=512, out_features=1000, bias=True)\n",
|
||
")\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"import torchvision\n",
|
||
"model = torchvision.models.resnet18(pretrained=False) #我们不下载预训练权重\n",
|
||
"print(model)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"那么我们该如何选择网络呢？\n",
|
||
"[来源](https://www.researchgate.net/figure/Comparison-of-popular-CNN-architectures-The-vertical-axis-shows-top-1-accuracy-on_fig2_320084139)\n",
|
||
"\n",
|
||
"以上表格可以清楚的看到准确率和计算量之间的对比。我的建议是，小型图片分类任务，resnet18基本上已经可以了，如果真对准确度要求比较高，再选其他更好的网络架构。\n",
|
||
"\n",
|
||
"**另外有句俗话叫：穷人只能AlexNet，富人才用Res**"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
}
|
||
],
|
||
"metadata": {
|
||
"kernelspec": {
|
||
"display_name": "Python 3",
|
||
"language": "python",
|
||
"name": "python3"
|
||
},
|
||
"language_info": {
|
||
"codemirror_mode": {
|
||
"name": "ipython",
|
||
"version": 3
|
||
},
|
||
"file_extension": ".py",
|
||
"mimetype": "text/x-python",
|
||
"name": "python",
|
||
"nbconvert_exporter": "python",
|
||
"pygments_lexer": "ipython3",
|
||
"version": "3.6.7"
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 2
|
||
}
|