notebooks/6.4-6.6 CNN_2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# [6.4. Multiple Input and Multiple Output Channels](https://d2l.ai/chapter_convolutional-neural-networks/channels.html)\n",
    "\n",
    "\n",
    "<img src='./images/conv_operation.png' width='800'/>\n",
    "\n",
    "&emsp; <sub>  * Image Credit : https://www.kaggle.com/manmohan291/57-cnn-basics\n",
    "\n",
    "In this example, the input array/image has 3 channels. We want the output to have 2 channels. <br> \n",
    "With a convolution kernel of size **3 x 3**, **how many parameters does this convolutional layer has ?** <br>\n",
    "- **weight** parameters : **input_channels x output_channels x 3 x 3**\n",
    "- **bias** parameters : **output_channels**\n",
    "    \n",
    "Why multiple output channels?\n",
    "- So that we can capture multiple aspects of an image at each spatial location. <br> E.g. The 1st filter can learn to detect horizontal edge, and maybe the 2nd filter is learned to detect 45$^\\circ$ edge."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Weight parameter dimension :  torch.Size([2, 3, 3, 3])\n",
      "Bias parameter dimension :  torch.Size([2])\n"
     ]
    }
   ],
   "source": [
    "# Let's count the number of parameters in a Conv2D layer in PyTorch\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "\n",
    "conv2d = nn.Conv2d(in_channels=3, out_channels=2, kernel_size=(3, 3), bias=True)\n",
    "\n",
    "print('Weight parameter dimension : ', conv2d.weight.data.shape)\n",
    "print('Bias parameter dimension : '  , conv2d.bias.data.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1 x 1 Convolutional Layer\n",
    "\n",
    "<img src='./images/fig_6.4.2.png' width='800'/>\n",
    "\n",
    "- Not like a typical convolutional layer, a 1×1 filter doesn't have a spatial extent to correlate adjacent pixels of the inputs. \n",
    "\n",
    "\n",
    "- The  1×1 convolutional layer is equivalent to the fully-connected layer, when applied on a per spatial pixel basis. Therefore, we can view it this way : the 1x1 convolution allows the model to **summarize the input feature maps** at each spatial location; the coefficients used in the weighted sum operation is stored in each of the 1x1 filter. \n",
    "\n",
    "\n",
    "- The **1x1 convolution** can be used to adjust the number of channels to control model model complexity. (Similar to **pooling** being used to reduce the width and height of an image.) \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# [6.5 Pooling](https://d2l.ai/chapter_convolutional-neural-networks/pooling.html)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Properties \n",
    "\n",
    "- Two types of Pooling : **maximum pooling** and **average pooling**\n",
    "\n",
    "- The pooling layer’s number of output channels is the same as the number of input channels.\n",
    "\n",
    "- Pooling layer contains no parameters.\n",
    "\n",
    "- Pooling layer combined with a stride > 1 can be used to reduce the spatial dimensions of the hidden representations. \n",
    "\n",
    "\n",
    "### Max v.s. Avg Pooling\n",
    "\n",
    "- Max-Pooling : Maximized presence of the features from a feature map.\n",
    "\n",
    "Max-pooling helps to extract the sharpest features on the image and the sharpest features are a best lower-level representation of the image.\n",
    "\n",
    "- Avg-Pooling : Summarizes the average presence of features from a feature map. \n",
    "\n",
    "Avg-Pooling helps to extract the smooth features. \n",
    "\n",
    "- In practice, maximum pooling has been found to work better than average pooling for computer vision tasks.\n",
    "\n",
    "\n",
    "### Purpose of Pooling\n",
    "\n",
    "- Downsampling the feature maps / hidden representations.\n",
    "- Pooling gives us some amount of translation invariance by mitigating the sensitivity of convolutional layers to location.\n",
    "- To aggregate information -> Larger receptive fields for deeper layers in a network.\n",
    "- Better robustness to noise.\n",
    "\n",
    "Pooling layers summarizes the featured map so that the model would be more reliable and robust to irrelevant details, and be less sensitive to precisely positioned features.\n",
    "\n",
    "\n",
    "\n",
    "#### Reference\n",
    "\n",
    "- [Comprehensive Guide to Different Pooling Layers in Deep Learning](https://analyticsindiamag.com/comprehensive-guide-to-different-pooling-layers-in-deep-learning/)\n",
    "\n",
    "- [A Theoretical Analysis of Feature Pooling in Visual Recognition (Boureau+10)](https://www.di.ens.fr/willow/pdfs/icml2010b.pdf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# [6.6. Convolutional Neural Networks (LeNet)](https://d2l.ai/chapter_convolutional-neural-networks/lenet.html)\n",
    "\n",
    "- **LeNet** was one of the earliest convolutional neural networks proposed by Yann LeCun at 1989 to identify handwritten digits. <br>\n",
    "  Paper : [Backpropagation Applied to Handwritten Zip Code Recognition -- LeCun et al. 1989](https://ieeexplore.ieee.org/document/6795724)\n",
    "  \n",
    "  \n",
    "- LeNet Architecture : <br>\n",
    "  ConV -> Pool -> ConV -> Pool -> FC -> FC -> FC (-> Softmax)\n",
    "\n",
    "<img src='./images/slide_LeNet.png' width='860'/>\n",
    "\n",
    "- The basic units in **each convolutional block** are a **convolutional layer**, a **sigmoid activation function**, and a subsequent **average pooling** operation.\n",
    "\n",
    "\n",
    "- **Flatten** the feature maps before passing into to the **fully connected layer**.\n",
    "\n",
    "\n",
    "- In a CNN, convolutional layers are typically arranged so that they **gradually decrease the spatial resolution** of the representations, while **increasing the number of channels**.\n",
    "\n",
    "\n",
    "- Total network parameters ~ 62k. Most of the parameters are coming from the fully connected layers. \n",
    "\n",
    "\n",
    "- The activation functions used back in 1990s is **sigmoid**, while modern CNNs uses **ReLU** in most of the time. Also **max-pooling** is a more common choice in modern CNNs, instead of the **avg-pooling** used here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "device(type='cpu')"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import torch\n",
    "from torch import nn\n",
    "from torchvision import datasets, transforms\n",
    "from torch.utils.data import DataLoader\n",
    "import torch.optim as optim\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rc('font', size=16)\n",
    "\n",
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = 'retina'\n",
    "\n",
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
    "device"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define LeNet-5 in PyTorch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total network parameters : 61706\n"
     ]
    }
   ],
   "source": [
    "net = nn.Sequential(\n",
    "    nn.Conv2d(1, 6, kernel_size=5, padding=2), nn.Sigmoid(),\n",
    "    nn.AvgPool2d(kernel_size=2, stride=2),\n",
    "    nn.Conv2d(6, 16, kernel_size=5), nn.Sigmoid(),\n",
    "    nn.AvgPool2d(kernel_size=2, stride=2),\n",
    "    nn.Flatten(),\n",
    "    nn.Linear(16 * 5 * 5, 120), nn.Sigmoid(),\n",
    "    nn.Linear(120, 84), nn.Sigmoid(),\n",
    "    nn.Linear(84, 10))\n",
    "\n",
    "net.to(device)\n",
    "\n",
    "# count total network parameters\n",
    "print('Total network parameters :', sum(param.numel() for param in net.parameters() if param.requires_grad))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train LeNet-5 on fashionMNIST\n",
    "\n",
    "### Data : prepare dataset and dataloader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "fmnist_train = datasets.FashionMNIST(\n",
    "    root=\"../../../data\", train=True, transform=transforms.ToTensor(), download=True)\n",
    "fmnist_test = datasets.FashionMNIST(\n",
    "    root=\"../../../data\", train=False, transform=transforms.ToTensor(), download=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "<Figure size 936x288 with 5 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 174,
       "width": 739
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "labels_map = {\n",
    "    0: \"T-Shirt\",\n",
    "    1: \"Trouser\",\n",
    "    2: \"Pullover\",\n",
    "    3: \"Dress\",\n",
    "    4: \"Coat\",\n",
    "    5: \"Sandal\",\n",
    "    6: \"Shirt\",\n",
    "    7: \"Sneaker\",\n",
    "    8: \"Bag\",\n",
    "    9: \"Ankle Boot\",\n",
    "}\n",
    "\n",
    "fig, axes = plt.subplots(1, 5, figsize=(13, 4))\n",
    "plt.subplots_adjust(hspace=0.001, wspace=0.001)\n",
    "\n",
    "for i in range(5):\n",
    "    image, label = fmnist_train.__getitem__(i)\n",
    "    axes[i].imshow(image.squeeze().numpy())\n",
    "    axes[i].set_title(f'{label} : {labels_map[label]}')\n",
    "    axes[i].axis('off')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 100\n",
    "\n",
    "train_loader = DataLoader(fmnist_train, batch_size=batch_size, shuffle=True)\n",
    "test_loader = DataLoader(fmnist_test, batch_size=batch_size, shuffle=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loss function\n",
    "\n",
    "**Careful!** <br>\n",
    "`nn.CrossEntropyLoss` applies: ``nn.LogSoftmax`` and ``nn.NLLLoss`` (negative log likelihood loss). <br>\n",
    "So don't apply softmax in the forward loop. <br>\n",
    "``Y`` is class (digit) labels, not one-hot!   <br>\n",
    "``Y_pred`` has raw scores (logits), no Softmax!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "criterion = nn.CrossEntropyLoss()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Optimization Function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "learning_rate = 0.5\n",
    "\n",
    "optimizer = torch.optim.SGD(net.parameters(), lr=learning_rate)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training Loop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_1_epoch(model, optimizer, train_loss, train_acc):\n",
    "    \n",
    "    model.train()             # set model to training mode\n",
    "\n",
    "    running_loss = 0.\n",
    "    running_corrects = 0\n",
    "    for i, (images, labels) in enumerate(train_loader):\n",
    "        images = images.to(device) \n",
    "        labels = labels.to(device)\n",
    "        \n",
    "        # forward\n",
    "        outputs = model(images)\n",
    "        \n",
    "        # loss\n",
    "        loss = criterion(outputs, labels) # predicted outputs v.s. actual labels\n",
    "        \n",
    "        # backwards\n",
    "        optimizer.zero_grad()  # zero gradient\n",
    "        loss.backward()        # loss.backward would calculate dloss/dw_i  \n",
    "        optimizer.step()       # update network parameters (w_i)\n",
    "        \n",
    "        # compute statistics (avg. loss, accuracy)\n",
    "        running_loss += loss.item() * labels.size(0)\n",
    "        _, predictions = torch.max(outputs, 1)\n",
    "        running_corrects += torch.sum(predictions == labels.data)\n",
    "        \n",
    "        #if (i+1)%300 == 0: # every 300 steps, print something\n",
    "        #    print(f'epoch {epoch+1}/{num_epochs}, step {i+1}/{len(train_loader)}, loss={loss.item():.4f}')\n",
    "            \n",
    "    epoch_loss = running_loss / len(fmnist_train)            ; train_loss.append(epoch_loss)\n",
    "    epoch_acc  = running_corrects / len(fmnist_train) * 100. ; train_acc.append(epoch_acc)\n",
    "    print(f'>>> End epoch {epoch+1}, train accuracy = {epoch_acc:.2f}%, train avgLoss = {epoch_loss:.4f}')\n",
    "\n",
    "    \n",
    "    \n",
    "def validation(model, valid_loss, valid_acc):\n",
    "    \n",
    "    model.eval()              # set model to evaluation mode\n",
    "    \n",
    "    running_loss = 0.\n",
    "    running_corrects = 0\n",
    "    \n",
    "    for i, (images, labels) in enumerate(test_loader):\n",
    "        \n",
    "        images = images.to(device)\n",
    "        labels = labels.to(device)\n",
    "        outputs = model(images)\n",
    "        \n",
    "        loss = criterion(outputs, labels)\n",
    "        \n",
    "        # compute statistics (avg. loss, accuracy)\n",
    "        running_loss += loss.item() * labels.size(0)\n",
    "        _, predictions = torch.max(outputs, 1)\n",
    "        running_corrects += torch.sum(predictions == labels.data)\n",
    "    \n",
    "    valid_loss.append(running_loss/len(test_loader.dataset))\n",
    "    valid_acc.append(running_corrects/len(test_loader.dataset) * 100.)\n",
    "    print(f'>>>              valid accuracy = {valid_acc[-1]:.2f}%, valid avgLoss = {valid_loss[-1]:.4f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">>> End epoch 1, train accuracy = 10.03%, train avgLoss = 2.3122\n",
      ">>>              valid accuracy = 10.00%, valid avgLoss = 2.3062\n",
      ">>> End epoch 2, train accuracy = 9.76%, train avgLoss = 2.3050\n",
      ">>>              valid accuracy = 10.00%, valid avgLoss = 2.3034\n",
      ">>> End epoch 3, train accuracy = 9.82%, train avgLoss = 2.3041\n",
      ">>>              valid accuracy = 10.00%, valid avgLoss = 2.3039\n",
      ">>> End epoch 4, train accuracy = 10.48%, train avgLoss = 2.3026\n",
      ">>>              valid accuracy = 10.00%, valid avgLoss = 2.2984\n",
      ">>> End epoch 5, train accuracy = 29.97%, train avgLoss = 1.8273\n",
      ">>>              valid accuracy = 50.66%, valid avgLoss = 1.1681\n",
      ">>> End epoch 6, train accuracy = 60.36%, train avgLoss = 0.9871\n",
      ">>>              valid accuracy = 57.21%, valid avgLoss = 0.9543\n",
      ">>> End epoch 7, train accuracy = 68.60%, train avgLoss = 0.8046\n",
      ">>>              valid accuracy = 68.34%, valid avgLoss = 0.7836\n",
      ">>> End epoch 8, train accuracy = 73.35%, train avgLoss = 0.6904\n",
      ">>>              valid accuracy = 73.33%, valid avgLoss = 0.6454\n",
      ">>> End epoch 9, train accuracy = 75.83%, train avgLoss = 0.6166\n",
      ">>>              valid accuracy = 76.34%, valid avgLoss = 0.6159\n",
      ">>> End epoch 10, train accuracy = 77.89%, train avgLoss = 0.5665\n",
      ">>>              valid accuracy = 76.87%, valid avgLoss = 0.6071\n"
     ]
    }
   ],
   "source": [
    "train_loss = []\n",
    "train_acc = []\n",
    "valid_loss = []\n",
    "valid_acc = []\n",
    "\n",
    "num_epochs = 10\n",
    "for epoch in range(0, num_epochs):\n",
    "    train_1_epoch(net, optimizer, train_loss, train_acc)\n",
    "    validation(net, valid_loss, valid_acc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 316,
       "width": 894
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 2, figsize=(15, 5))\n",
    "plt.rc('font', size=16)\n",
    "\n",
    "ax[0].plot(range(1,num_epochs+1)[::], train_loss[::], 'o--', label='train', color='b')\n",
    "ax[0].plot(range(1,num_epochs+1)[::], valid_loss[::], 'o--', label='valid', color='dodgerblue')\n",
    "ax[1].plot(range(1,num_epochs+1)[::], train_acc[::] , 'o--', label='train', color='b')\n",
    "ax[1].plot(range(1,num_epochs+1)[::], valid_acc[::], 'o--', label='valid', color='dodgerblue')\n",
    "\n",
    "ax[0].set_ylabel('loss')\n",
    "ax[1].set_ylabel('accuracy')\n",
    "\n",
    "for i in range(2):\n",
    "    ax[i].set_xlabel('epoch')\n",
    "    ax[i].legend(loc='best')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}