91-GoogLeNet实现-卷积神经网络.ipynb

{
 "cells": [
  {
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"
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"
    }
   },
   "cell_type": "markdown",
   "id": "d706e2dc-93f2-46d1-b939-480831d398ce",
   "metadata": {
    "tags": []
   },
   "source": [
    "## GoogLeNet\n",
    "![image.png](attachment:4f4e6df9-cab2-4175-9d3a-788bd78db41b.png)\n",
    "***\n",
    "![image.png](attachment:4556b947-c8ca-4ebf-8139-51d8e8ae9757.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ede502a6-6b5c-4323-b959-24a6a9e78c72",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "from torch import nn\n",
    "from torch.nn import functional as F\n",
    "import dltools\n",
    "\n",
    "\n",
    "class Inception(nn.Module):\n",
    "    # c1--c4是每条路径的输出通道数\n",
    "    def __init__(self, in_channels, c1, c2, c3, c4, **kwargs):\n",
    "        super(Inception, self).__init__(**kwargs)\n",
    "        # 线路1，单1x1卷积层\n",
    "        self.p1_1 = nn.Conv2d(in_channels, c1, kernel_size=1)\n",
    "        # 线路2，1x1卷积层后接3x3卷积层\n",
    "        self.p2_1 = nn.Conv2d(in_channels, c2[0], kernel_size=1)\n",
    "        self.p2_2 = nn.Conv2d(c2[0], c2[1], kernel_size=3, padding=1)\n",
    "        # 线路3，1x1卷积层后接5x5卷积层\n",
    "        self.p3_1 = nn.Conv2d(in_channels, c3[0], kernel_size=1)\n",
    "        self.p3_2 = nn.Conv2d(c3[0], c3[1], kernel_size=5, padding=2)\n",
    "        # 线路4，3x3最大汇聚层后接1x1卷积层\n",
    "        self.p4_1 = nn.MaxPool2d(kernel_size=3, stride=1, padding=1)\n",
    "        self.p4_2 = nn.Conv2d(in_channels, c4, kernel_size=1)\n",
    "\n",
    "    def forward(self, x):\n",
    "        p1 = F.relu(self.p1_1(x))\n",
    "        p2 = F.relu(self.p2_2(F.relu(self.p2_1(x))))\n",
    "        p3 = F.relu(self.p3_2(F.relu(self.p3_1(x))))\n",
    "        p4 = F.relu(self.p4_2(self.p4_1(x)))\n",
    "        # 在通道维度上连结输出\n",
    "        return torch.cat((p1, p2, p3, p4), dim=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e3960128-9177-41ca-be98-099888df2b23",
   "metadata": {},
   "outputs": [],
   "source": [
    "b1 = nn.Sequential(nn.Conv2d(1, 64, kernel_size=7, stride=2, padding=3),\n",
    "                   nn.ReLU(),\n",
    "                   nn.MaxPool2d(kernel_size=3, stride=2, padding=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0fe9de24-ee1b-4d46-9869-3b4a1304f876",
   "metadata": {},
   "outputs": [],
   "source": [
    "b2 = nn.Sequential(nn.Conv2d(64, 64, kernel_size=1),\n",
    "                   nn.ReLU(),\n",
    "                   nn.Conv2d(64, 192, kernel_size=3, padding=1),\n",
    "                   nn.ReLU(),\n",
    "                   nn.MaxPool2d(kernel_size=3, stride=2, padding=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "915e86ac-675d-44b5-94db-691f3ef4b948",
   "metadata": {},
   "outputs": [],
   "source": [
    "b3 = nn.Sequential(Inception(192, 64, (96, 128), (16, 32), 32),\n",
    "                   Inception(256, 128, (128, 192), (32, 96), 64),\n",
    "                   nn.MaxPool2d(kernel_size=3, stride=2, padding=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2df730f1-4091-4341-ae83-8fa79a2331bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "b4 = nn.Sequential(Inception(480, 192, (96, 208), (16, 48), 64),\n",
    "                   Inception(512, 160, (112, 224), (24, 64), 64),\n",
    "                   Inception(512, 128, (128, 256), (24, 64), 64),\n",
    "                   Inception(512, 112, (144, 288), (32, 64), 64),\n",
    "                   Inception(528, 256, (160, 320), (32, 128), 128),\n",
    "                   nn.MaxPool2d(kernel_size=3, stride=2, padding=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c3f471bb-48d9-43b0-91f9-0e604a25ce53",
   "metadata": {},
   "outputs": [],
   "source": [
    "b5 = nn.Sequential(Inception(832, 256, (160, 320), (32, 128), 128),\n",
    "                   Inception(832, 384, (192, 384), (48, 128), 128),\n",
    "                   nn.AdaptiveAvgPool2d((1,1)),\n",
    "                   nn.Flatten())\n",
    "\n",
    "net = nn.Sequential(b1, b2, b3, b4, b5, nn.Linear(1024, 10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "793c7e8b-61b0-4904-88bf-58e5cc1e46c6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sequential output shape:\t torch.Size([1, 64, 24, 24])\n",
      "Sequential output shape:\t torch.Size([1, 192, 12, 12])\n",
      "Sequential output shape:\t torch.Size([1, 480, 6, 6])\n",
      "Sequential output shape:\t torch.Size([1, 832, 3, 3])\n",
      "Sequential output shape:\t torch.Size([1, 1024])\n",
      "Linear output shape:\t torch.Size([1, 10])\n"
     ]
    }
   ],
   "source": [
    "X = torch.rand(size=(1, 1, 96, 96))\n",
    "for layer in net:\n",
    "    X = layer(X)\n",
    "    print(layer.__class__.__name__,'output shape:\\t', X.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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      "text/plain": [
       "<Figure size 252x180 with 1 Axes>"
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lr, num_epochs, batch_size = 0.1, 10, 128\n",
    "train_iter, test_iter = dltools.load_data_fashion_mnist(batch_size, resize=96)\n",
    "dltools.train_ch6(net, train_iter, test_iter, num_epochs, lr, dltools.try_gpu())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1c715d37-3627-41c7-af9b-defc2052c561",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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