VGG.ipynb

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   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "from torch import nn\n",
    "from d2l import torch as d2l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [],
   "source": [
    "def vgg_blocks(num_covs, in_channels, out_channels):\n",
    "    layers = []\n",
    "    for _ in range(num_covs):\n",
    "        layers.append(nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1))\n",
    "        layers.append(nn.ReLU())\n",
    "        in_channels = out_channels\n",
    "    layers.append(nn.MaxPool2d(kernel_size=2, stride=2))\n",
    "    return nn.Sequential(*layers)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [],
   "source": [
    "conv_arch = ((1, 64), (1, 128), (2, 256), (2, 512), (2, 512))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [],
   "source": [
    "def vgg(conv_arch):\n",
    "    conv_blks = []\n",
    "    in_channels = 1\n",
    "    for (num_covs, out_channels) in conv_arch:\n",
    "        conv_blks.append(vgg_blocks(num_covs, in_channels, out_channels))\n",
    "        in_channels = out_channels\n",
    "    return nn.Sequential(*conv_blks, nn.Flatten(), nn.Linear(out_channels * 7 * 7, 4096), nn.ReLU(),\n",
    "                         nn.Dropout(0.5), nn.Linear(4096, 4096), nn.ReLU(), nn.Dropout(0.5), nn.Linear(4096, 10))\n",
    "net = vgg(conv_arch)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sequential  output shape:  torch.Size([1, 64, 112, 112])\n",
      "Sequential  output shape:  torch.Size([1, 128, 56, 56])\n",
      "Sequential  output shape:  torch.Size([1, 256, 28, 28])\n",
      "Sequential  output shape:  torch.Size([1, 512, 14, 14])\n",
      "Sequential  output shape:  torch.Size([1, 512, 7, 7])\n",
      "Flatten  output shape:  torch.Size([1, 25088])\n",
      "Linear  output shape:  torch.Size([1, 4096])\n",
      "ReLU  output shape:  torch.Size([1, 4096])\n",
      "Dropout  output shape:  torch.Size([1, 4096])\n",
      "Linear  output shape:  torch.Size([1, 4096])\n",
      "ReLU  output shape:  torch.Size([1, 4096])\n",
      "Dropout  output shape:  torch.Size([1, 4096])\n",
      "Linear  output shape:  torch.Size([1, 10])\n"
     ]
    }
   ],
   "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: \", X.shape)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [],
   "source": [
    "ratio = 4\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",
    "small_conv_arch = [(pair[0], pair[1] // ratio) for pair in conv_arch]\n",
    "net = vgg(small_conv_arch)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [
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   ],
   "source": [
    "lr, num_epochs, batch_size = 0.05, 10, 128\n",
    "train_iter, test_iter = d2l.load_data_fashion_mnist(batch_size, resize=224)\n",
    "d2l.train_ch6(net, train_iter, test_iter, num_epochs, lr, d2l.try_gpu())"
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