Ch09_Modern_Convolutional_Networks/Network_in_Network(NiN).ipynb

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    "# Network in Network (NiN)\n",
    "\n",
    ":label:`chapter_nin`\n",
    "\n",
    "\n",
    "LeNet, AlexNet, and VGG all share a common design pattern:\n",
    "extract features exploiting *spatial* structure\n",
    "via a sequence of convolutions and pooling layers\n",
    "and then post-process the representations via fully-connected layers.\n",
    "The improvements upon LeNet by AlexNet and VGG mainly lie\n",
    "in how these later networks widen and deepen these two modules.\n",
    "Alternatively, one could imagine using fully-connected layers\n",
    "earlier in the process.\n",
    "However, a careless use of dense layers might give up the\n",
    "spatial structure of the representation entirely,\n",
    "Network in Network (NiN) blocks offer an alternative.\n",
    "They were proposed by [Lin, Chen and Yan, 2013](https://arxiv.org/pdf/1312.4400.pdf) based on a very simple insight---to\n",
    "use an MLP on the channels for each pixel separately.\n",
    "\n",
    "## NiN Blocks\n",
    "\n",
    "Recall that the inputs and outputs of convolutional layers\n",
    "consist of four-dimensional arrays with axes\n",
    "corresponding to the batch, channel, height, and width.\n",
    "Also recall that the inputs and outputs of fully-connected layers\n",
    "are typically two-dimensional arrays corresponding to the batch, and features.\n",
    "The idea behind NiN is to apply a fully-connected layer\n",
    "at each pixel location (for each height and  width).\n",
    "If we tie the weights across each spatial location,\n",
    "we could think of this as a $1\\times 1$ convolutional layer\n",
    "(as described in :numref:`chapter_channels`)\n",
    "or as a fully-connected layer acting independently on each pixel location.\n",
    "Another way to view this is to think of each element in the spatial dimension\n",
    "(height and width) as equivalent to an example\n",
    "and the channel as equivalent to a feature.\n",
    "The figure below illustrates the main structural differences\n",
    "between NiN and AlexNet, VGG, and other networks.\n",
    "\n"
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      "text/plain": [
       "<IPython.core.display.SVG object>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import SVG\n",
    "SVG(filename='../img/nin-compare.svg')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "kmYHFzRQi_pq"
   },
   "source": [
    "![The figure on the left shows the network structure of AlexNet and VGG, and the figure on the right shows the network structure of NiN. ]\n",
    "\n",
    "The NiN block consists of one convolutional layer\n",
    "followed by two $1\\times 1$ convolutional layers that act as\n",
    "per-pixel fully-connected layers with ReLU activations.\n",
    "The convolution width of the first layer is typically set by the user.\n",
    "The subsequent widths are fixed to $1 \\times 1$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "attributes": {
     "classes": [],
     "id": "",
     "n": "2"
    },
    "colab": {},
    "colab_type": "code",
    "id": "nYgYLWHQi_pr"
   },
   "outputs": [],
   "source": [
    "import sys\n",
    "sys.path.insert(0, '..')\n",
    "\n",
    "import d2l\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "\n",
    "def nin_block(in_channels,out_channels,kernel_size,strides,padding):\n",
    "    blk = nn.Sequential(\n",
    "            nn.Conv2d(in_channels,out_channels,kernel_size,strides,padding),\n",
    "            nn.ReLU(),\n",
    "            nn.Conv2d(out_channels,out_channels,kernel_size=1),\n",
    "            nn.ReLU(),\n",
    "            nn.Conv2d(out_channels,out_channels,kernel_size=1),\n",
    "            nn.ReLU())\n",
    "    return blk"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "aCPHtMOPi_pu"
   },
   "source": [
    "## NiN Model\n",
    "\n",
    "The original NiN network was proposed shortly after AlexNet\n",
    "and clearly draws some inspiration.\n",
    "NiN uses convolutional layers with window shapes\n",
    "of $11\\times 11$, $5\\times 5$, and $3\\times 3$,\n",
    "and the corresponding numbers of output channels are the same as in AlexNet. Each NiN block is followed by a maximum pooling layer\n",
    "with a stride of 2 and a window shape of $3\\times 3$.\n",
    "\n",
    "Once significant difference between NiN and AlexNet\n",
    "is that NiN avoids dense connections altogether.\n",
    "Instead, NiN uses an NiN block with a number of output channels equal to the number of label classes, followed by a *global* average pooling layer,\n",
    "yielding a vector of logits.\n",
    "One advantage of NiN's design is that it significantly\n",
    "reduces the number of required model parameters.\n",
    "However, in practice, this design sometimes requires\n",
    "increased model training time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Thqgr1b6i_pv"
   },
   "outputs": [],
   "source": [
    "class Net(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(Net, self).__init__()\n",
    "        self.n1 = nin_block(1,out_channels=96, kernel_size=11, strides=4, padding=0)\n",
    "        self.m1 = nn.MaxPool2d(3,stride=2)\n",
    "        self.n2 = nin_block(96,out_channels=256, kernel_size=5, strides=1, padding=2)\n",
    "        self.m2 = nn.MaxPool2d(3,stride=2)\n",
    "        self.n3 = nin_block(256,out_channels=384, kernel_size=3, strides=1, padding=1)\n",
    "        self.m3 = nn.MaxPool2d(3,stride=2)\n",
    "        self.dropout1 = nn.Dropout2d(0.5)\n",
    "        self.n4 = nin_block(384,out_channels=10, kernel_size=3, strides=1, padding=1)\n",
    "        #Global Average Pooling can be achieved by AdaptiveMaxPool2d with output size = (1,1)\n",
    "        self.avg1 = nn.AdaptiveMaxPool2d((1,1))\n",
    "        self.flat = Flatten()\n",
    "        \n",
    "    def forward(self, x): \n",
    "        x = self.m1(self.n1(x))\n",
    "        x = self.m2(self.n2(x))\n",
    "        x = self.dropout1(self.m3(self.n3(x)))\n",
    "        x = self.n4(x)\n",
    "        x = self.avg1(x)\n",
    "        x = self.flat(x) \n",
    "        return x\n",
    "\n",
    "class Flatten(nn.Module):\n",
    "    def forward(self, input):\n",
    "        return input.view(input.size(0), -1)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "EHtgQ0pNi_pz"
   },
   "source": [
    "We create a data example to see the output shape of each block."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 187
    },
    "colab_type": "code",
    "id": "T2OYmfQhi_p0",
    "outputId": "f3d694a8-bc05-4bed-8f43-a412ffd0d98e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sequential output shape:\t torch.Size([1, 96, 54, 54])\n",
      "MaxPool2d output shape:\t torch.Size([1, 96, 26, 26])\n",
      "Sequential output shape:\t torch.Size([1, 256, 26, 26])\n",
      "MaxPool2d output shape:\t torch.Size([1, 256, 12, 12])\n",
      "Sequential output shape:\t torch.Size([1, 384, 12, 12])\n",
      "MaxPool2d output shape:\t torch.Size([1, 384, 5, 5])\n",
      "Dropout2d output shape:\t torch.Size([1, 384, 5, 5])\n",
      "Sequential output shape:\t torch.Size([1, 10, 5, 5])\n",
      "AdaptiveMaxPool2d output shape:\t torch.Size([1, 10, 1, 1])\n",
      "Flatten output shape:\t torch.Size([1, 10])\n"
     ]
    }
   ],
   "source": [
    "net = Net()\n",
    "X = torch.rand(size=(1,1,224,224))\n",
    "for layer in net.children():\n",
    "    X = layer(X)\n",
    "    print(layer.__class__.__name__,'output shape:\\t', X.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "XPYoonvpi_p4"
   },
   "source": [
    "## Data Acquisition and Training\n",
    "\n",
    "As before we use Fashion-MNIST to train the model.\n",
    "NiN's training is similar to that for AlexNet and VGG,\n",
    "but it often uses a larger learning rate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 119
    },
    "colab_type": "code",
    "id": "3ICRo_vEi_p9",
    "outputId": "b1f91211-2f15-4f09-8bfc-fdff9ca0756d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training on cuda:0\n",
      "epoch 1, loss 0.0179, train acc 0.102, test acc 0.100, time 70.8 sec\n",
      "epoch 2, loss 0.0115, train acc 0.458, test acc 0.653, time 69.7 sec\n",
      "epoch 3, loss 0.0073, train acc 0.669, test acc 0.709, time 69.9 sec\n",
      "epoch 4, loss 0.0062, train acc 0.708, test acc 0.716, time 70.1 sec\n",
      "epoch 5, loss 0.0045, train acc 0.788, test acc 0.792, time 70.2 sec\n"
     ]
    }
   ],
   "source": [
    "lr, num_epochs, batch_size, device = 0.1, 5, 128, d2l.try_gpu()\n",
    "\n",
    "#Xavier initialization of weights\n",
    "def init_weights(m):\n",
    "    if type(m) == nn.Linear or type(m) == nn.Conv2d:\n",
    "        torch.nn.init.xavier_uniform_(m.weight)\n",
    "\n",
    "net.apply(init_weights)\n",
    "\n",
    "#Loading fashion-MNIST data\n",
    "train_iter, test_iter = d2l.load_data_fashion_mnist(batch_size, resize=224)\n",
    "\n",
    "#criterion\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "\n",
    "d2l.train_ch5(net, train_iter, test_iter, criterion, num_epochs, batch_size, device, lr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ya1Uz8WEi_qD"
   },
   "source": [
    "## Summary\n",
    "\n",
    "* NiN uses blocks consisting of a convolutional layer and multiple $1\\times 1$ convolutional layer. This can be used within the convolutional stack to allow for more per-pixel nonlinearity.\n",
    "* NiN removes the fully connected layers and replaces them with global average pooling (i.e. summing over all locations) after reducing the number of channels to the desired number of outputs (e.g. 10 for Fashion-MNIST).\n",
    "* Removing the dense layers reduces overfitting. NiN has dramatically fewer parameters.\n",
    "* The NiN design influenced many subsequent convolutional neural networks designs.\n",
    "\n",
    "## Exercises\n",
    "\n",
    "1. Tune the hyper-parameters to improve the classification accuracy.\n",
    "1. Why are there two $1\\times 1$ convolutional layers in the NiN block? Remove one of them, and then observe and analyze the experimental phenomena.\n",
    "1. Calculate the resource usage for NiN\n",
    "    * What is the number of parameters?\n",
    "    * What is the amount of computation?\n",
    "    * What is the amount of memory needed during training?\n",
    "    * What is the amount of memory needed during inference?\n",
    "1. What are possible problems with reducing the $384 \\times 5 \\times 5$ representation to a $10 \\times 5 \\times 5$ representation in one step?\n",
    "\n"
   ]
  }
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