1288 lines
46 KiB
Plaintext
1288 lines
46 KiB
Plaintext
{
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"source": [
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"# 使用块的网络（VGG）\n",
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":label:`sec_vgg`\n",
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"\n",
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"虽然AlexNet证明深层神经网络卓有成效，但它没有提供一个通用的模板来指导后续的研究人员设计新的网络。\n",
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"在下面的几个章节中，我们将介绍一些常用于设计深层神经网络的启发式概念。\n",
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"\n",
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"与芯片设计中工程师从放置晶体管到逻辑元件再到逻辑块的过程类似，神经网络架构的设计也逐渐变得更加抽象。研究人员开始从单个神经元的角度思考问题，发展到整个层，现在又转向块，重复层的模式。\n",
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"\n",
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"使用块的想法首先出现在牛津大学的[视觉几何组（visual geometry group）](http://www.robots.ox.ac.uk/~vgg/)的*VGG网络*中。通过使用循环和子程序，可以很容易地在任何现代深度学习框架的代码中实现这些重复的架构。\n",
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"\n",
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"## (**VGG块**)\n",
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"\n",
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"经典卷积神经网络的基本组成部分是下面的这个序列：\n",
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"\n",
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"1. 带填充以保持分辨率的卷积层；\n",
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"1. 非线性激活函数，如ReLU；\n",
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"1. 汇聚层，如最大汇聚层。\n",
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"\n",
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"而一个VGG块与之类似，由一系列卷积层组成，后面再加上用于空间下采样的最大汇聚层。在最初的VGG论文中 :cite:`Simonyan.Zisserman.2014`，作者使用了带有$3\\times3$卷积核、填充为1（保持高度和宽度）的卷积层，和带有$2 \\times 2$汇聚窗口、步幅为2（每个块后的分辨率减半）的最大汇聚层。在下面的代码中，我们定义了一个名为`vgg_block`的函数来实现一个VGG块。\n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "93fe0e7e",
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"metadata": {
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"origin_pos": 2,
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"tab": [
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"pytorch"
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]
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},
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"source": [
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"该函数有三个参数，分别对应于卷积层的数量`num_convs`、输入通道的数量`in_channels`\n",
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"和输出通道的数量`out_channels`.\n"
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]
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"cell_type": "code",
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"execution_count": 1,
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"id": "328fda20",
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"metadata": {
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"execution": {
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"iopub.execute_input": "2023-08-18T07:17:59.402403Z",
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"iopub.status.busy": "2023-08-18T07:17:59.401674Z",
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"shell.execute_reply": "2023-08-18T07:18:04.619845Z"
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},
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"origin_pos": 4,
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"tab": [
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"pytorch"
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]
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},
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"outputs": [],
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"source": [
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"import torch\n",
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"from torch import nn\n",
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"from d2l import torch as d2l\n",
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"\n",
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"\n",
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"def vgg_block(num_convs, in_channels, out_channels):\n",
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" layers = []\n",
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" for _ in range(num_convs):\n",
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" layers.append(nn.Conv2d(in_channels, out_channels,\n",
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" kernel_size=3, padding=1))\n",
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" layers.append(nn.ReLU())\n",
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" in_channels = out_channels\n",
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" layers.append(nn.MaxPool2d(kernel_size=2,stride=2))\n",
|
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" return nn.Sequential(*layers)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "e136ba7b",
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"metadata": {
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"origin_pos": 7
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},
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"source": [
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"## [**VGG网络**]\n",
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"\n",
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"与AlexNet、LeNet一样，VGG网络可以分为两部分：第一部分主要由卷积层和汇聚层组成，第二部分由全连接层组成。如 :numref:`fig_vgg`中所示。\n",
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"\n",
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"\n",
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":width:`400px`\n",
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":label:`fig_vgg`\n",
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"\n",
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"VGG神经网络连接 :numref:`fig_vgg`的几个VGG块（在`vgg_block`函数中定义）。其中有超参数变量`conv_arch`。该变量指定了每个VGG块里卷积层个数和输出通道数。全连接模块则与AlexNet中的相同。\n",
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"\n",
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"原始VGG网络有5个卷积块，其中前两个块各有一个卷积层，后三个块各包含两个卷积层。\n",
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"第一个模块有64个输出通道，每个后续模块将输出通道数量翻倍，直到该数字达到512。由于该网络使用8个卷积层和3个全连接层，因此它通常被称为VGG-11。\n"
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]
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"cell_type": "code",
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"execution_count": 2,
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"id": "93cf8f6b",
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"metadata": {
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"execution": {
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"iopub.execute_input": "2023-08-18T07:18:04.627044Z",
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"iopub.status.busy": "2023-08-18T07:18:04.626251Z",
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"iopub.status.idle": "2023-08-18T07:18:04.632251Z",
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"shell.execute_reply": "2023-08-18T07:18:04.631189Z"
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"origin_pos": 8,
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"tab": [
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"pytorch"
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]
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},
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"outputs": [],
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||
"source": [
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||
"conv_arch = ((1, 64), (1, 128), (2, 256), (2, 512), (2, 512))"
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]
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},
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{
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"cell_type": "markdown",
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"id": "f9d59caf",
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"metadata": {
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"origin_pos": 9
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},
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"source": [
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"下面的代码实现了VGG-11。可以通过在`conv_arch`上执行for循环来简单实现。\n"
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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": 3,
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"id": "3daef8f3",
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"metadata": {
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"execution": {
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"iopub.execute_input": "2023-08-18T07:18:04.637160Z",
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"iopub.status.busy": "2023-08-18T07:18:04.636507Z",
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"iopub.status.idle": "2023-08-18T07:18:06.122904Z",
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"shell.execute_reply": "2023-08-18T07:18:06.121824Z"
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},
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"origin_pos": 11,
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"tab": [
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"pytorch"
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]
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},
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"outputs": [],
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||
"source": [
|
||
"def vgg(conv_arch):\n",
|
||
" conv_blks = []\n",
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||
" in_channels = 1\n",
|
||
" # 卷积层部分\n",
|
||
" for (num_convs, out_channels) in conv_arch:\n",
|
||
" conv_blks.append(vgg_block(num_convs, in_channels, out_channels))\n",
|
||
" in_channels = out_channels\n",
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||
"\n",
|
||
" return nn.Sequential(\n",
|
||
" *conv_blks, nn.Flatten(),\n",
|
||
" # 全连接层部分\n",
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||
" nn.Linear(out_channels * 7 * 7, 4096), nn.ReLU(), nn.Dropout(0.5),\n",
|
||
" nn.Linear(4096, 4096), nn.ReLU(), nn.Dropout(0.5),\n",
|
||
" nn.Linear(4096, 10))\n",
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||
"\n",
|
||
"net = vgg(conv_arch)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "ecf269a2",
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"metadata": {
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"origin_pos": 14
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},
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"source": [
|
||
"接下来，我们将构建一个高度和宽度为224的单通道数据样本，以[**观察每个层输出的形状**]。\n"
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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": 4,
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"id": "d3f0eba0",
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"metadata": {
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"execution": {
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"iopub.execute_input": "2023-08-18T07:18:06.128720Z",
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"iopub.status.busy": "2023-08-18T07:18:06.128055Z",
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"iopub.status.idle": "2023-08-18T07:18:06.433530Z",
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"shell.execute_reply": "2023-08-18T07:18:06.432421Z"
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},
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"origin_pos": 16,
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"tab": [
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"pytorch"
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]
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},
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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": [
|
||
"Sequential output shape:\t torch.Size([1, 64, 112, 112])\n",
|
||
"Sequential output shape:\t torch.Size([1, 128, 56, 56])\n",
|
||
"Sequential output shape:\t torch.Size([1, 256, 28, 28])\n",
|
||
"Sequential output shape:\t torch.Size([1, 512, 14, 14])\n",
|
||
"Sequential output shape:\t torch.Size([1, 512, 7, 7])\n",
|
||
"Flatten output shape:\t torch.Size([1, 25088])\n"
|
||
]
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||
},
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{
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"name": "stdout",
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||
"output_type": "stream",
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"text": [
|
||
"Linear output shape:\t torch.Size([1, 4096])\n",
|
||
"ReLU output shape:\t torch.Size([1, 4096])\n",
|
||
"Dropout output shape:\t torch.Size([1, 4096])\n",
|
||
"Linear output shape:\t torch.Size([1, 4096])\n",
|
||
"ReLU output shape:\t torch.Size([1, 4096])\n",
|
||
"Dropout output shape:\t torch.Size([1, 4096])\n",
|
||
"Linear output shape:\t torch.Size([1, 10])\n"
|
||
]
|
||
}
|
||
],
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||
"source": [
|
||
"X = torch.randn(size=(1, 1, 224, 224))\n",
|
||
"for blk in net:\n",
|
||
" X = blk(X)\n",
|
||
" print(blk.__class__.__name__,'output shape:\\t',X.shape)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
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||
"id": "a2d53faf",
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"metadata": {
|
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"origin_pos": 19
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},
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||
"source": [
|
||
"正如从代码中所看到的，我们在每个块的高度和宽度减半，最终高度和宽度都为7。最后再展平表示，送入全连接层处理。\n",
|
||
"\n",
|
||
"## 训练模型\n",
|
||
"\n",
|
||
"[**由于VGG-11比AlexNet计算量更大，因此我们构建了一个通道数较少的网络**]，足够用于训练Fashion-MNIST数据集。\n"
|
||
]
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},
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{
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||
"cell_type": "code",
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||
"execution_count": 5,
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||
"id": "f90a296d",
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"metadata": {
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"execution": {
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"iopub.execute_input": "2023-08-18T07:18:06.441259Z",
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"iopub.status.busy": "2023-08-18T07:18:06.439026Z",
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"iopub.status.idle": "2023-08-18T07:18:06.938917Z",
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"shell.execute_reply": "2023-08-18T07:18:06.938010Z"
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},
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"origin_pos": 20,
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"tab": [
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"pytorch"
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||
]
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"ratio = 4\n",
|
||
"small_conv_arch = [(pair[0], pair[1] // ratio) for pair in conv_arch]\n",
|
||
"net = vgg(small_conv_arch)"
|
||
]
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},
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{
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"cell_type": "markdown",
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"id": "cc4a93ee",
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"metadata": {
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"origin_pos": 22
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},
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"source": [
|
||
"除了使用略高的学习率外，[**模型训练**]过程与 :numref:`sec_alexnet`中的AlexNet类似。\n"
|
||
]
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},
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{
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"name": "stdout",
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"text": [
|
||
"loss 0.178, train acc 0.935, test acc 0.920\n",
|
||
"2463.7 examples/sec on cuda:0\n"
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"## 小结\n",
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"\n",
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"* VGG-11使用可复用的卷积块构造网络。不同的VGG模型可通过每个块中卷积层数量和输出通道数量的差异来定义。\n",
|
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"* 块的使用导致网络定义的非常简洁。使用块可以有效地设计复杂的网络。\n",
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"* 在VGG论文中，Simonyan和Ziserman尝试了各种架构。特别是他们发现深层且窄的卷积（即$3 \\times 3$）比较浅层且宽的卷积更有效。\n",
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"\n",
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"\n",
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|
||
"1. 尝试将Fashion-MNIST数据集图像的高度和宽度从224改为96。这对实验有什么影响？\n",
|
||
"1. 请参考VGG论文 :cite:`Simonyan.Zisserman.2014`中的表1构建其他常见模型，如VGG-16或VGG-19。\n"
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]
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},
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"source": [
|
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"[Discussions](https://discuss.d2l.ai/t/1866)\n"
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]
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}
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