shusentang--dive-into-dl-pytorch
wehub-resource-sync
261 行
8.0 KiB
Plaintext
261 行
8.0 KiB
Plaintext
{
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"cells": [
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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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"# 5.7 使用重复元素的网络（VGG）"
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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": 1,
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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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"0.4.0\n",
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"cuda\n"
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]
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}
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],
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"source": [
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"import time\n",
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"import torch\n",
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"from torch import nn, optim\n",
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"\n",
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"import sys\n",
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"sys.path.append(\"..\") \n",
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"import d2lzh_pytorch as d2l\n",
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"device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
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"\n",
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"print(torch.__version__)\n",
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"print(device)"
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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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"## 5.7.1 VGG块"
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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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"source": [
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"def vgg_block(num_convs, in_channels, out_channels):\n",
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" blk = []\n",
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" for i in range(num_convs):\n",
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" if i == 0:\n",
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" blk.append(nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1))\n",
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" else:\n",
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" blk.append(nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1))\n",
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" blk.append(nn.ReLU())\n",
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" blk.append(nn.MaxPool2d(kernel_size=2, stride=2))\n",
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" return nn.Sequential(*blk)"
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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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"## 5.7.2 VGG网络"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"conv_arch = ((1, 1, 64), (1, 64, 128), (2, 128, 256), (2, 256, 512), (2, 512, 512))\n",
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"fc_features = 512 * 7 * 7 # 根据卷积层的输出算出来的\n",
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"fc_hidden_units = 4096 # 任意"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"def vgg(conv_arch, fc_features, fc_hidden_units=4096):\n",
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" net = nn.Sequential()\n",
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" # 卷积层部分\n",
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" for i, (num_convs, in_channels, out_channels) in enumerate(conv_arch):\n",
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" net.add_module(\"vgg_block_\" + str(i+1), vgg_block(num_convs, in_channels, out_channels))\n",
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" # 全连接层部分\n",
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" net.add_module(\"fc\", nn.Sequential(d2l.FlattenLayer(),\n",
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" nn.Linear(fc_features, fc_hidden_units),\n",
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" nn.ReLU(),\n",
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" nn.Dropout(0.5),\n",
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" nn.Linear(fc_hidden_units, fc_hidden_units),\n",
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" nn.ReLU(),\n",
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" nn.Dropout(0.5),\n",
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" nn.Linear(fc_hidden_units, 10)\n",
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" ))\n",
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" return net"
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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": 5,
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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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"vgg_block_1 output shape: torch.Size([1, 64, 112, 112])\n",
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"vgg_block_2 output shape: torch.Size([1, 128, 56, 56])\n",
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"vgg_block_3 output shape: torch.Size([1, 256, 28, 28])\n",
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"vgg_block_4 output shape: torch.Size([1, 512, 14, 14])\n",
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"vgg_block_5 output shape: torch.Size([1, 512, 7, 7])\n",
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"fc output shape: torch.Size([1, 10])\n"
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]
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}
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],
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"source": [
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"net = vgg(conv_arch, fc_features, fc_hidden_units)\n",
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"X = torch.rand(1, 1, 224, 224)\n",
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"\n",
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"# named_children获取一级子模块及其名字(named_modules会返回所有子模块,包括子模块的子模块)\n",
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"for name, blk in net.named_children(): \n",
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" X = blk(X)\n",
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" print(name, 'output shape: ', X.shape)"
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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": 6,
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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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"Sequential(\n",
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" (vgg_block_1): Sequential(\n",
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" (0): Conv2d(1, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
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" (1): ReLU()\n",
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" (2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
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" )\n",
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" (vgg_block_2): Sequential(\n",
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" (0): Conv2d(8, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
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" (1): ReLU()\n",
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" (2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
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" )\n",
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" (vgg_block_3): Sequential(\n",
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" (0): Conv2d(16, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
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" (1): ReLU()\n",
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" (2): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
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" (3): ReLU()\n",
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" (4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
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" )\n",
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" (vgg_block_4): Sequential(\n",
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" (0): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
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" (1): ReLU()\n",
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" (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
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" (3): ReLU()\n",
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" (4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
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" )\n",
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" (vgg_block_5): Sequential(\n",
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" (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
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" (1): ReLU()\n",
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" (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
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" (3): ReLU()\n",
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" (4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
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" )\n",
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" (fc): Sequential(\n",
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" (0): FlattenLayer()\n",
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" (1): Linear(in_features=3136, out_features=512, bias=True)\n",
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" (2): ReLU()\n",
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" (3): Dropout(p=0.5)\n",
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" (4): Linear(in_features=512, out_features=512, bias=True)\n",
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" (5): ReLU()\n",
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" (6): Dropout(p=0.5)\n",
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" (7): Linear(in_features=512, out_features=10, bias=True)\n",
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" )\n",
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")\n"
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]
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}
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],
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"source": [
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"ratio = 8\n",
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"small_conv_arch = [(1, 1, 64//ratio), (1, 64//ratio, 128//ratio), (2, 128//ratio, 256//ratio), \n",
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" (2, 256//ratio, 512//ratio), (2, 512//ratio, 512//ratio)]\n",
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"net = vgg(small_conv_arch, fc_features // ratio, fc_hidden_units // ratio)\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": [
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"## 5.7.3 获取数据和训练模型"
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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": 7,
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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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"training on cuda\n",
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"epoch 1, loss 0.0101, train acc 0.755, test acc 0.859, time 255.9 sec\n",
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"epoch 2, loss 0.0051, train acc 0.882, test acc 0.902, time 238.1 sec\n",
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"epoch 3, loss 0.0043, train acc 0.900, test acc 0.908, time 225.5 sec\n",
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"epoch 4, loss 0.0038, train acc 0.913, test acc 0.914, time 230.3 sec\n",
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"epoch 5, loss 0.0035, train acc 0.919, test acc 0.918, time 153.9 sec\n"
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]
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}
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],
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"source": [
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"batch_size = 64\n",
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"# 如出现“out of memory”的报错信息，可减小batch_size或resize\n",
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"train_iter, test_iter = d2l.load_data_fashion_mnist(batch_size, resize=224)\n",
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"\n",
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"lr, num_epochs = 0.001, 5\n",
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"optimizer = torch.optim.Adam(net.parameters(), lr=lr)\n",
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"d2l.train_ch5(net, train_iter, test_iter, batch_size, optimizer, device, num_epochs)"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python [default]",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.4"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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