wehub-resource-sync
233 lines
6.6 KiB
Plaintext
233 lines
6.6 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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"# 5.9 含并行连结的网络（GoogLeNet）"
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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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"import torch.nn.functional as F\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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"## 5.9.1 Inception 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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"class Inception(nn.Module):\n",
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" # c1 - c4为每条线路里的层的输出通道数\n",
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" def __init__(self, in_c, c1, c2, c3, c4):\n",
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" super(Inception, self).__init__()\n",
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" # 线路1，单1 x 1卷积层\n",
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" self.p1_1 = nn.Conv2d(in_c, c1, kernel_size=1)\n",
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" # 线路2，1 x 1卷积层后接3 x 3卷积层\n",
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" self.p2_1 = nn.Conv2d(in_c, c2[0], kernel_size=1)\n",
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" self.p2_2 = nn.Conv2d(c2[0], c2[1], kernel_size=3, padding=1)\n",
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" # 线路3，1 x 1卷积层后接5 x 5卷积层\n",
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" self.p3_1 = nn.Conv2d(in_c, c3[0], kernel_size=1)\n",
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" self.p3_2 = nn.Conv2d(c3[0], c3[1], kernel_size=5, padding=2)\n",
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" # 线路4，3 x 3最大池化层后接1 x 1卷积层\n",
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" self.p4_1 = nn.MaxPool2d(kernel_size=3, stride=1, padding=1)\n",
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" self.p4_2 = nn.Conv2d(in_c, c4, kernel_size=1)\n",
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"\n",
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" def forward(self, x):\n",
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" p1 = F.relu(self.p1_1(x))\n",
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" p2 = F.relu(self.p2_2(F.relu(self.p2_1(x))))\n",
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" p3 = F.relu(self.p3_2(F.relu(self.p3_1(x))))\n",
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" p4 = F.relu(self.p4_2(self.p4_1(x)))\n",
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" return torch.cat((p1, p2, p3, p4), dim=1) # 在通道维上连结输出"
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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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"## 5.9.2 GoogLeNet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": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"b1 = nn.Sequential(nn.Conv2d(1, 64, kernel_size=7, stride=2, padding=3),\n",
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" nn.ReLU(),\n",
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" nn.MaxPool2d(kernel_size=3, stride=2, padding=1))"
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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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"b2 = nn.Sequential(nn.Conv2d(64, 64, kernel_size=1),\n",
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" nn.Conv2d(64, 192, kernel_size=3, padding=1),\n",
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" nn.MaxPool2d(kernel_size=3, stride=2, padding=1))"
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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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"source": [
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"b3 = nn.Sequential(Inception(192, 64, (96, 128), (16, 32), 32),\n",
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" Inception(256, 128, (128, 192), (32, 96), 64),\n",
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" nn.MaxPool2d(kernel_size=3, stride=2, padding=1))"
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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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"source": [
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"b4 = nn.Sequential(Inception(480, 192, (96, 208), (16, 48), 64),\n",
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" Inception(512, 160, (112, 224), (24, 64), 64),\n",
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" Inception(512, 128, (128, 256), (24, 64), 64),\n",
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" Inception(512, 112, (144, 288), (32, 64), 64),\n",
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" Inception(528, 256, (160, 320), (32, 128), 128),\n",
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" nn.MaxPool2d(kernel_size=3, stride=2, padding=1))"
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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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"source": [
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"b5 = nn.Sequential(Inception(832, 256, (160, 320), (32, 128), 128),\n",
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" Inception(832, 384, (192, 384), (48, 128), 128),\n",
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" d2l.GlobalAvgPool2d())"
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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": 8,
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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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"output shape: torch.Size([1, 64, 24, 24])\n",
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"output shape: torch.Size([1, 192, 12, 12])\n",
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"output shape: torch.Size([1, 480, 6, 6])\n",
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"output shape: torch.Size([1, 832, 3, 3])\n",
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"output shape: torch.Size([1, 1024, 1, 1])\n",
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"output shape: torch.Size([1, 1024])\n",
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"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 = nn.Sequential(b1, b2, b3, b4, b5, d2l.FlattenLayer(), nn.Linear(1024, 10))\n",
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"X = torch.rand(1, 1, 96, 96)\n",
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"for blk in net.children(): \n",
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" X = blk(X)\n",
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" print('output shape: ', X.shape)"
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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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"## 5.9.3 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": 9,
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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 0.0087, train acc 0.570, test acc 0.831, time 45.5 sec\n",
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"epoch 2, loss 0.0032, train acc 0.851, test acc 0.853, time 48.5 sec\n",
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"epoch 3, loss 0.0026, train acc 0.880, test acc 0.883, time 45.4 sec\n",
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"epoch 4, loss 0.0022, train acc 0.895, test acc 0.887, time 46.6 sec\n",
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"epoch 5, loss 0.0020, train acc 0.906, test acc 0.896, time 43.5 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 = 128\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=96)\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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