1.Alexnet.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. AlexNet realization "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "from torch import nn\n",
    "from d2l import torch as d2l "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "net = nn.Sequential(\n",
    "    # 这里，我们使用一个11*11的更大窗口来捕捉对象。\n",
    "    # 同时，步幅为4，以减少输出的高度和宽度。\n",
    "    # 另外，输出通道的数目远大于LeNet\n",
    "    nn.Conv2d(1, 96, kernel_size=11, stride=4, padding=1), nn.ReLU(),\n",
    "    nn.MaxPool2d(kernel_size=3, stride=2),\n",
    "    # 减小卷积窗口，使用填充为2来使得输入与输出的高和宽一致，且增大输出通道数\n",
    "    nn.Conv2d(96, 256, kernel_size=5, padding=2), nn.ReLU(),\n",
    "    nn.MaxPool2d(kernel_size=3, stride=2),\n",
    "    # 使用三个连续的卷积层和较小的卷积窗口。\n",
    "    # 除了最后的卷积层，输出通道的数量进一步增加。\n",
    "    # 在前两个卷积层之后，汇聚层不用于减少输入的高度和宽度\n",
    "    nn.Conv2d(256, 384, kernel_size=3, padding=1), nn.ReLU(),\n",
    "    nn.Conv2d(384, 384, kernel_size=3, padding=1), nn.ReLU(),\n",
    "    nn.Conv2d(384, 256, kernel_size=3, padding=1), nn.ReLU(),\n",
    "    nn.MaxPool2d(kernel_size=3, stride=2),\n",
    "    nn.Flatten(),\n",
    "    # 这里，全连接层的输出数量是LeNet中的好几倍。使用dropout层来减轻过拟合\n",
    "    nn.Linear(6400, 4096), nn.ReLU(),\n",
    "    nn.Dropout(p=0.5),\n",
    "    nn.Linear(4096, 4096), nn.ReLU(),\n",
    "    nn.Dropout(p=0.5),\n",
    "    # 最后是输出层。由于这里使用Fashion-MNIST，所以用类别数为10，而非论文中的1000\n",
    "    nn.Linear(4096, 10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Conv2d output shape:\t torch.Size([1, 96, 54, 54])\n",
      "ReLU output shape:\t torch.Size([1, 96, 54, 54])\n",
      "MaxPool2d output shape:\t torch.Size([1, 96, 26, 26])\n",
      "Conv2d output shape:\t torch.Size([1, 256, 26, 26])\n",
      "ReLU output shape:\t torch.Size([1, 256, 26, 26])\n",
      "MaxPool2d output shape:\t torch.Size([1, 256, 12, 12])\n",
      "Conv2d output shape:\t torch.Size([1, 384, 12, 12])\n",
      "ReLU output shape:\t torch.Size([1, 384, 12, 12])\n",
      "Conv2d output shape:\t torch.Size([1, 384, 12, 12])\n",
      "ReLU output shape:\t torch.Size([1, 384, 12, 12])\n",
      "Conv2d output shape:\t torch.Size([1, 256, 12, 12])\n",
      "ReLU output shape:\t torch.Size([1, 256, 12, 12])\n",
      "MaxPool2d output shape:\t torch.Size([1, 256, 5, 5])\n",
      "Flatten output shape:\t torch.Size([1, 6400])\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, 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"
     ]
    }
   ],
   "source": [
    "X = torch.randn(1, 1, 224, 224)\n",
    "for layer in net:\n",
    "    X=layer(X)\n",
    "    print(layer.__class__.__name__,'output shape:\\t',X.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss 0.328, train acc 0.880, test acc 0.863\n",
      "1386.0 examples/sec on cuda:0\n"
     ]
    },
    {
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      "text/plain": [
       "<Figure size 252x180 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
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     "output_type": "display_data"
    }
   ],
   "source": [
    "batch_size = 128\n",
    "train_iter, test_iter = d2l.load_data_fashion_mnist(batch_size, resize=224)\n",
    "lr, num_epochs = 0.01, 10\n",
    "d2l.train_ch6(net, train_iter, test_iter, num_epochs, lr, d2l.try_gpu())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "完整版本pytorch实现（不借助d2l）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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=True)\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=True)\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=True)\n",
      "    (8): Conv2d(384, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (9): ReLU(inplace=True)\n",
      "    (10): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (11): ReLU(inplace=True)\n",
      "    (12): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "  )\n",
      "  (classifier): Sequential(\n",
      "    (0): Linear(in_features=9216, out_features=4096, bias=True)\n",
      "    (1): ReLU(inplace=True)\n",
      "    (2): Linear(in_features=4096, out_features=4096, bias=True)\n",
      "    (3): ReLU(inplace=True)\n",
      "    (4): Linear(in_features=4096, out_features=1000, bias=True)\n",
      "  )\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "class AlexNet(nn.Module):\n",
    "    def __init__(self, num_classes=1000):\n",
    "        super(AlexNet, self).__init__()\n",
    "        self.features = nn.Sequential(\n",
    "            nn.Conv2d(3, 64, kernel_size=11, stride=4, padding=2),\n",
    "            nn.ReLU(inplace=True),\n",
    "            nn.MaxPool2d(kernel_size=3, stride=2),\n",
    "            nn.Conv2d(64, 192, kernel_size=5, padding=2),\n",
    "            nn.ReLU(inplace=True),\n",
    "            nn.MaxPool2d(kernel_size=3, stride=2),\n",
    "            nn.Conv2d(192, 384, kernel_size=3, padding=1),\n",
    "            nn.ReLU(inplace=True),\n",
    "            nn.Conv2d(384, 256, kernel_size=3, padding=1),\n",
    "            nn.ReLU(inplace=True),\n",
    "            nn.Conv2d(256, 256, kernel_size=3, padding=1),\n",
    "            nn.ReLU(inplace=True),\n",
    "            nn.MaxPool2d(kernel_size=3, stride=2),\n",
    "        )\n",
    "        self.classifier = nn.Sequential(\n",
    "            nn.Linear(256 * 6 * 6, 4096),\n",
    "            nn.ReLU(inplace=True),\n",
    "            nn.Linear(4096, 4096),\n",
    "            nn.ReLU(inplace=True),\n",
    "            nn.Linear(4096, num_classes),\n",
    "        )\n",
    "    def forward(self, x):\n",
    "        x = self.features(x)\n",
    "        x = x.view(x.size(0), 256 * 6 * 6)\n",
    "        x = self.classifier(x)\n",
    "        return x\n",
    "\n",
    "if __name__ == '__main__':\n",
    "    # Example\n",
    "    net = AlexNet()\n",
    "    print(net)"
   ]
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