BMEN4460-NB2-image_classification_on_MNIST_data_with_visualization.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Simple Image Classification Using the MNIST Dataset\n",
    "Nanyan \"Rosalie\" Zhu and Chen \"Raphael\" Liu\n",
    "\n",
    "This notebook belongs to this repository [\"RnR-2018/BMEN4460-NB2-image_classification_on_MNIST_data\"](https://github.com/RnR-2018/BMEN4460-NB2-image_classification_on_MNIST_data) and its parent repository [\"RnR-2018/Deep-learning-with-PyTorch-and-GCP\"](https://github.com/RnR-2018/Deep-learning-with-PyTorch-and-GCP).\n",
    "\n",
    "This notebook is inspired by and largely adapted from [this GitHub repository](https://github.com/jkotra/mnist-pytorch)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import the necessary packages."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "from torch.optim import Adam, SGD\n",
    "from torch.utils.data import TensorDataset, DataLoader\n",
    "\n",
    "import tensorflow.keras.datasets.mnist as MNIST\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import confusion_matrix, accuracy_score\n",
    "import warnings"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check GPU availability (should be False if you run the CPU VM and True if you run the GPU VM)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GPU available: True\n"
     ]
    }
   ],
   "source": [
    "cuda = torch.cuda.is_available()\n",
    "print(\"GPU available:\", cuda)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Set the random seed for reproducibility.\n",
    "\n",
    "The random seed helps to make sure that the model parameter initialization, sequence of random shuffling, and most other nondeterministic operations are kept the same each time you run this notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "torch.manual_seed(20204460)\n",
    "np.random.seed(20204460)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Download the MNIST handwritten digits dataset.\n",
    "\n",
    "It is designed to have a 60k-image train set and a 10k-image test set. We are not going to use all of them for this tutorial because that will eat up quite a bit of memory."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "(image_MNIST_train_set, label_MNIST_train_set), (image_MNIST_test_set, label_MNIST_test_set) = MNIST.load_data()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Display some sample images and labels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 400x600 with 24 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "row = 4\n",
    "col = 6\n",
    "fig = plt.figure(figsize = (row, col), dpi = 100)\n",
    "\n",
    "for image_index in range(1, row * col + 1):\n",
    "    image = image_MNIST_test_set[image_index, :, :]\n",
    "    ax = fig.add_subplot(row, col, image_index)\n",
    "    ax.set_xticks([])\n",
    "    ax.set_yticks([])\n",
    "    ax.title.set_text(str(label_MNIST_test_set[image_index]))\n",
    "    plt.imshow(image, cmap = 'gray')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### We have 70k images in total, but we will only use a subset of it for this tutorial.\n",
    "\n",
    "#### Let's just use what MNIST originally allocated as the test set as our entire dataset (N = 10k) and perform a 8:1:1 train:validation:test split."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Images: train 8000, validation 1000, test 1000 \n",
      "Labels: train 8000, validation 1000, test 1000\n"
     ]
    }
   ],
   "source": [
    "# Split the 10000 MNIST test set into our train set (N = 8e3) and validation+test set (N = 2e3) at an 8:2 ratio.\n",
    "indices_train, indices_validation_and_test = train_test_split(range(len(image_MNIST_test_set)), test_size = 0.2)\n",
    "image_train = image_MNIST_test_set[indices_train, :, :]\n",
    "label_train = label_MNIST_test_set[indices_train]\n",
    "image_validation_and_test = image_MNIST_test_set[indices_validation_and_test, :, :]\n",
    "label_validation_and_test = label_MNIST_test_set[indices_validation_and_test]\n",
    "\n",
    "# Split the validation+test set into the validation set (N = 5e3) and the test set (N = 5e3).\n",
    "# Note that we are using the package \"train_test_split\" but what we are actually doing here is validation test split.\n",
    "indices_validation, indices_test = train_test_split(range(len(image_validation_and_test)), test_size = 0.5)\n",
    "image_validation = image_validation_and_test[indices_validation, :, :]\n",
    "label_validation = label_validation_and_test[indices_validation]\n",
    "image_test = image_validation_and_test[indices_test, :, :]\n",
    "label_test = label_validation_and_test[indices_test]\n",
    "\n",
    "print('Images: train %d, validation %d, test %d \\nLabels: train %d, validation %d, test %d' % \\\n",
    "      (len(image_train), len(image_validation), len(image_test), len(label_train), len(label_validation), len(label_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reformat the images and labels so that they can be fed into the data loader.\n",
    "Normally you would construct a validation loader and a test loader similar to how we construct the train loader, but for this case since the validation and test set only consists of 1000 28$\\times$28 images each, we can just get away with not using a data loader at all."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "image_train_torch = torch.from_numpy(image_train).type(torch.FloatTensor).view(-1, 1, 28, 28)\n",
    "label_train_torch = torch.from_numpy(label_train).type(torch.LongTensor)\n",
    "\n",
    "image_validation_torch = torch.from_numpy(image_validation).type(torch.FloatTensor).view(-1, 1, 28, 28)\n",
    "label_validation_torch = torch.from_numpy(label_validation).type(torch.LongTensor)\n",
    "\n",
    "image_test_torch = torch.from_numpy(image_test).type(torch.FloatTensor).view(-1, 1, 28, 28)\n",
    "label_test_torch = torch.from_numpy(label_test).type(torch.LongTensor)\n",
    "\n",
    "train_data = TensorDataset(image_train_torch, label_train_torch)\n",
    "train_loader = DataLoader(train_data, batch_size = 1000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Build our classification network.\n",
    "\n",
    "This architecture comes from [this GitHub repository](https://github.com/floydhub/mnist).\n",
    "\n",
    "<img src=\"data/MNIST_CNN.png\" width=\"1200\" align=\"center\"/>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model(\n",
      "  (conv1): Conv2d(1, 10, kernel_size=(5, 5), stride=(1, 1))\n",
      "  (conv2): Conv2d(10, 20, kernel_size=(5, 5), stride=(1, 1))\n",
      "  (fc1): Linear(in_features=320, out_features=50, bias=True)\n",
      "  (fc2): Linear(in_features=50, out_features=10, bias=True)\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "# Formula to calculate shape as we go through layer by layer = [(X - F + 2P)/S] + 1\n",
    "# Here,\n",
    "# X = Width / Height\n",
    "# F = Kernel size\n",
    "# P = Padding\n",
    "# S = Strides (default = 1)\n",
    "\n",
    "# Our input to the first layer is going to be [batchsize, 1, 28, 28]\n",
    "# substitute, =[(28 - 5 + 2(0))/1] + 1\n",
    "#             =[(23)/1] + 1\n",
    "#             =23 + 1\n",
    "#             =24\n",
    "\n",
    "\n",
    "class Model(nn.Module):\n",
    "    '''ConvNet -> Max_Pool -> RELU -> ConvNet -> Max_Pool -> RELU -> FC -> RELU -> FC -> SOFTMAX'''\n",
    "    def __init__(self):\n",
    "        '''Define model modules.'''\n",
    "        super(Model, self).__init__()\n",
    "        self.conv1 = nn.Conv2d(1, 10, 5, 1)\n",
    "        self.conv2 = nn.Conv2d(10, 20, 5, 1)\n",
    "        self.fc1 = nn.Linear(4 * 4 * 20, 50)\n",
    "        self.fc2 = nn.Linear(50, 10)\n",
    "\n",
    "    def forward(self, x):\n",
    "        '''Define the model architecture (the sequence to place the model modules).'''\n",
    "        x = F.relu(self.conv1(x))\n",
    "        x = F.max_pool2d(x, 2, 2)\n",
    "        x = F.relu(self.conv2(x))\n",
    "        x = F.max_pool2d(x, 2, 2)\n",
    "        x = x.view(-1, 4 * 4 * 20)\n",
    "        x = F.relu(self.fc1(x))\n",
    "        x = self.fc2(x)\n",
    "        return F.log_softmax(x, dim = 1)\n",
    "\n",
    "our_CNN = Model()\n",
    "\n",
    "# If GPU available, move the model to GPU.\n",
    "if cuda:\n",
    "    our_CNN.cuda()\n",
    "\n",
    "print(our_CNN)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Visualize kernels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The shape of the weights:  torch.Size([10, 1, 5, 5])\n",
      "filter 0 min value:  -0.1952027529478073 , max value: 0.1967245191335678\n",
      "filter 1 min value:  -0.19775216281414032 , max value: 0.19976700842380524\n",
      "filter 2 min value:  -0.15170109272003174 , max value: 0.19543324410915375\n",
      "filter 3 min value:  -0.1970035880804062 , max value: 0.1823096126317978\n",
      "filter 4 min value:  -0.19860005378723145 , max value: 0.18713442981243134\n",
      "filter 5 min value:  -0.1983514130115509 , max value: 0.1939190775156021\n",
      "filter 6 min value:  -0.1795956939458847 , max value: 0.1938774734735489\n",
      "filter 7 min value:  -0.19251863658428192 , max value: 0.1914764791727066\n",
      "filter 8 min value:  -0.19645391404628754 , max value: 0.1813836246728897\n",
      "filter 9 min value:  -0.17982669174671173 , max value: 0.19924910366535187\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "params = our_CNN.state_dict()\n",
    "plt.rcParams['figure.figsize'] = [20, 5]\n",
    "for k, v in params.items():\n",
    "    # get the first layer weight\n",
    "    if k == 'conv1.weight':\n",
    "        print('The shape of the weights: ', v.shape)\n",
    "        c_int = v.size()[1]     # number of the input channels\n",
    "        c_out = v.size()[0]     # number of the output channels\n",
    "        \n",
    "        for j in range(c_out):\n",
    "            plt.subplot(1, c_out, j + 1)\n",
    "            kernel = np.squeeze(v[j, :, :, :].cpu())\n",
    "            print('filter ' + str(j), 'min value: ', kernel.min().item(), ', max value:', kernel.max().item())\n",
    "            plt.imshow(kernel, cmap = 'gray')\n",
    "            plt.title('filter ' + str(j))\n",
    "            plt.axis('off');\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Visualize our sample data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data, label = next(iter(train_loader))\n",
    "sample_data = data[1, :, :, :]\n",
    "plt.imshow(np.squeeze(sample_data.cpu().detach()), cmap = 'gray')\n",
    "plt.axis('off');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### pass the sample image into the first layer of our kernel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The shape of the feature maps:  torch.Size([1, 10, 24, 24])\n",
      "feature_map 0 min value:  0.0 , max value: 216.06300354003906\n",
      "feature_map 1 min value:  0.0 , max value: 111.42817687988281\n",
      "feature_map 2 min value:  0.0 , max value: 217.41659545898438\n",
      "feature_map 3 min value:  0.0 , max value: 70.21971130371094\n",
      "feature_map 4 min value:  0.0 , max value: 34.2264404296875\n",
      "feature_map 5 min value:  0.0 , max value: 112.19308471679688\n",
      "feature_map 6 min value:  0.0 , max value: 156.91502380371094\n",
      "feature_map 7 min value:  0.0 , max value: 179.88145446777344\n",
      "feature_map 8 min value:  0.0 , max value: 147.3660888671875\n",
      "feature_map 9 min value:  0.0 , max value: 279.9332580566406\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x360 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "if cuda:\n",
    "    sample_img = sample_data.unsqueeze(0).cuda()\n",
    "else:\n",
    "    sample_img = sample_data.unsqueeze(0)\n",
    "\n",
    "for name, layer in our_CNN._modules.items():\n",
    "    x = sample_img\n",
    "    if name == 'conv1':\n",
    "        x = layer(x)\n",
    "        x = F.relu(x) if 'conv' in name else x\n",
    "        \n",
    "        print('The shape of the feature maps: ', x.shape)\n",
    "        c_int = x.size()[0]     # number of the input channels\n",
    "        c_out = x.size()[1]     # number of the output channels\n",
    "        \n",
    "        for j in range(c_out):\n",
    "            plt.subplot(1, c_out, j + 1)\n",
    "            feature_map = np.squeeze(x[:, j, :, :].cpu().detach())\n",
    "            print('feature_map ' + str(j), 'min value: ', feature_map.min().item(), ', max value:', feature_map.max().item())\n",
    "            plt.imshow(feature_map, cmap = 'gray')\n",
    "            plt.title('feature_map ' + str(j))\n",
    "            plt.axis('off');\n",
    "             "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define our optimizer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define our optimizer\n",
    "optimizer = SGD(our_CNN.parameters(), lr = 0.001)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Train our CNN for classification."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The shape of the weights:  torch.Size([10, 1, 5, 5])\n",
      "filter 0 min value:  -0.19600872695446014 , max value: 0.19047687947750092\n",
      "filter 1 min value:  -0.2012130618095398 , max value: 0.19595254957675934\n",
      "filter 2 min value:  -0.15486590564250946 , max value: 0.1906013935804367\n",
      "filter 3 min value:  -0.19737164676189423 , max value: 0.1826225370168686\n",
      "filter 4 min value:  -0.1988564431667328 , max value: 0.18554572761058807\n",
      "filter 5 min value:  -0.19834473729133606 , max value: 0.19162677228450775\n",
      "filter 6 min value:  -0.17755967378616333 , max value: 0.19353829324245453\n",
      "filter 7 min value:  -0.192549929022789 , max value: 0.18671612441539764\n",
      "filter 8 min value:  -0.19738346338272095 , max value: 0.17093117535114288\n",
      "filter 9 min value:  -0.18307620286941528 , max value: 0.19090652465820312\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The shape of the feature maps:  torch.Size([1, 10, 24, 24])\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x720 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0 | train_loss: 5.365622669458389 | validation_loss: 4.14507582783699\n",
      "Epoch: 1 | train_loss: 2.1477286964654922 | validation_loss: 2.0781160295009613\n",
      "Epoch: 2 | train_loss: 1.7150009870529175 | validation_loss: 1.6841303706169128\n",
      "Epoch: 3 | train_loss: 1.41792830824852 | validation_loss: 1.4040143936872482\n",
      "Epoch: 4 | train_loss: 1.1895094960927963 | validation_loss: 1.187541902065277\n",
      "Epoch: 5 | train_loss: 1.0201418548822403 | validation_loss: 1.025242142379284\n",
      "Epoch: 6 | train_loss: 0.8910167887806892 | validation_loss: 0.9009859785437584\n",
      "Epoch: 7 | train_loss: 0.7911502122879028 | validation_loss: 0.8061347454786301\n",
      "Epoch: 8 | train_loss: 0.7125504836440086 | validation_loss: 0.7312761098146439\n",
      "Epoch: 9 | train_loss: 0.6488602235913277 | validation_loss: 0.6711285412311554\n",
      "The shape of the weights:  torch.Size([10, 1, 5, 5])\n",
      "filter 0 min value:  -0.1880827248096466 , max value: 0.19322969019412994\n",
      "filter 1 min value:  -0.2021421641111374 , max value: 0.19305728375911713\n",
      "filter 2 min value:  -0.15372668206691742 , max value: 0.19617043435573578\n",
      "filter 3 min value:  -0.19961874186992645 , max value: 0.18423962593078613\n",
      "filter 4 min value:  -0.19908598065376282 , max value: 0.182852640748024\n",
      "filter 5 min value:  -0.198389932513237 , max value: 0.19697368144989014\n",
      "filter 6 min value:  -0.1721174120903015 , max value: 0.20048119127750397\n",
      "filter 7 min value:  -0.1903052031993866 , max value: 0.18864843249320984\n",
      "filter 8 min value:  -0.19771529734134674 , max value: 0.16835558414459229\n",
      "filter 9 min value:  -0.180115208029747 , max value: 0.19615305960178375\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The shape of the feature maps:  torch.Size([1, 10, 24, 24])\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x720 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 10 | train_loss: 0.5969463288784027 | validation_loss: 0.6228205636143684\n",
      "Epoch: 11 | train_loss: 0.5537934191524982 | validation_loss: 0.5828677043318748\n",
      "Epoch: 12 | train_loss: 0.5172886177897453 | validation_loss: 0.5491354241967201\n",
      "Epoch: 13 | train_loss: 0.48649607226252556 | validation_loss: 0.5201747119426727\n",
      "Epoch: 14 | train_loss: 0.4601834714412689 | validation_loss: 0.4951137974858284\n",
      "Epoch: 15 | train_loss: 0.4370631128549576 | validation_loss: 0.4732147715985775\n",
      "Epoch: 16 | train_loss: 0.4168235957622528 | validation_loss: 0.454192154109478\n",
      "Epoch: 17 | train_loss: 0.39878327772021294 | validation_loss: 0.4369506165385246\n",
      "Epoch: 18 | train_loss: 0.3824440948665142 | validation_loss: 0.4211338199675083\n",
      "Epoch: 19 | train_loss: 0.36767715215682983 | validation_loss: 0.4070267863571644\n",
      "The shape of the weights:  torch.Size([10, 1, 5, 5])\n",
      "filter 0 min value:  -0.18784219026565552 , max value: 0.19604909420013428\n",
      "filter 1 min value:  -0.20207622647285461 , max value: 0.19280914962291718\n",
      "filter 2 min value:  -0.15278398990631104 , max value: 0.1993378847837448\n",
      "filter 3 min value:  -0.2007870227098465 , max value: 0.1849815398454666\n",
      "filter 4 min value:  -0.199003204703331 , max value: 0.1815870851278305\n",
      "filter 5 min value:  -0.19840304553508759 , max value: 0.2005038857460022\n",
      "filter 6 min value:  -0.17119479179382324 , max value: 0.20279553532600403\n",
      "filter 7 min value:  -0.19006210565567017 , max value: 0.18866905570030212\n",
      "filter 8 min value:  -0.197509303689003 , max value: 0.16972675919532776\n",
      "filter 9 min value:  -0.17884279787540436 , max value: 0.19747360050678253\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The shape of the feature maps:  torch.Size([1, 10, 24, 24])\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x720 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 20 | train_loss: 0.3541276678442955 | validation_loss: 0.3946778066456318\n",
      "Epoch: 21 | train_loss: 0.34179312735795975 | validation_loss: 0.38344831392169\n",
      "Epoch: 22 | train_loss: 0.3302284777164459 | validation_loss: 0.3731550946831703\n",
      "Epoch: 23 | train_loss: 0.3194890581071377 | validation_loss: 0.3635157123208046\n",
      "Epoch: 24 | train_loss: 0.309505395591259 | validation_loss: 0.35468560084700584\n",
      "Epoch: 25 | train_loss: 0.30024024844169617 | validation_loss: 0.34656061232089996\n",
      "Epoch: 26 | train_loss: 0.2915116064250469 | validation_loss: 0.3389814607799053\n",
      "Epoch: 27 | train_loss: 0.28340955823659897 | validation_loss: 0.33193082734942436\n",
      "Epoch: 28 | train_loss: 0.2758928034454584 | validation_loss: 0.3254658132791519\n",
      "Epoch: 29 | train_loss: 0.2687051948159933 | validation_loss: 0.31947674974799156\n",
      "The shape of the weights:  torch.Size([10, 1, 5, 5])\n",
      "filter 0 min value:  -0.18915313482284546 , max value: 0.1980411410331726\n",
      "filter 1 min value:  -0.20231716334819794 , max value: 0.19306926429271698\n",
      "filter 2 min value:  -0.15182027220726013 , max value: 0.20158958435058594\n",
      "filter 3 min value:  -0.20149175822734833 , max value: 0.18519319593906403\n",
      "filter 4 min value:  -0.19894921779632568 , max value: 0.18089745938777924\n",
      "filter 5 min value:  -0.19836606085300446 , max value: 0.20276838541030884\n",
      "filter 6 min value:  -0.17063093185424805 , max value: 0.20402143895626068\n",
      "filter 7 min value:  -0.18988129496574402 , max value: 0.1883949190378189\n",
      "filter 8 min value:  -0.19731350243091583 , max value: 0.17155806720256805\n",
      "filter 9 min value:  -0.17801298201084137 , max value: 0.19786249101161957\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The shape of the feature maps:  torch.Size([1, 10, 24, 24])\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x720 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 30 | train_loss: 0.2619558647274971 | validation_loss: 0.3138485439121723\n",
      "Epoch: 31 | train_loss: 0.2556765265762806 | validation_loss: 0.3084294982254505\n",
      "Epoch: 32 | train_loss: 0.24961226247251034 | validation_loss: 0.3033972941339016\n",
      "Epoch: 33 | train_loss: 0.2438242882490158 | validation_loss: 0.2986367382109165\n",
      "Epoch: 34 | train_loss: 0.2383242528885603 | validation_loss: 0.29415080323815346\n",
      "Epoch: 35 | train_loss: 0.23316188901662827 | validation_loss: 0.2898753322660923\n",
      "Epoch: 36 | train_loss: 0.22817356698215008 | validation_loss: 0.285808227956295\n",
      "Epoch: 37 | train_loss: 0.22343757189810276 | validation_loss: 0.28208596631884575\n",
      "Epoch: 38 | train_loss: 0.21891207434237003 | validation_loss: 0.27854737639427185\n",
      "Epoch: 39 | train_loss: 0.21461474895477295 | validation_loss: 0.27514219656586647\n",
      "The shape of the weights:  torch.Size([10, 1, 5, 5])\n",
      "filter 0 min value:  -0.1901889145374298 , max value: 0.199605792760849\n",
      "filter 1 min value:  -0.20272503793239594 , max value: 0.19375544786453247\n",
      "filter 2 min value:  -0.15100207924842834 , max value: 0.20340251922607422\n",
      "filter 3 min value:  -0.2019185572862625 , max value: 0.1853918880224228\n",
      "filter 4 min value:  -0.1989273875951767 , max value: 0.18052954971790314\n",
      "filter 5 min value:  -0.19823835790157318 , max value: 0.2041180282831192\n",
      "filter 6 min value:  -0.17039932310581207 , max value: 0.20501026511192322\n",
      "filter 7 min value:  -0.1895730197429657 , max value: 0.18824321031570435\n",
      "filter 8 min value:  -0.1975727528333664 , max value: 0.17304682731628418\n",
      "filter 9 min value:  -0.1773560345172882 , max value: 0.19761504232883453\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The shape of the feature maps:  torch.Size([1, 10, 24, 24])\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x720 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 40 | train_loss: 0.2104166727513075 | validation_loss: 0.27179789543151855\n",
      "Epoch: 41 | train_loss: 0.20636961609125137 | validation_loss: 0.2685241028666496\n",
      "Epoch: 42 | train_loss: 0.202509181573987 | validation_loss: 0.26552047207951546\n",
      "Epoch: 43 | train_loss: 0.1987678576260805 | validation_loss: 0.2625433914363384\n",
      "Epoch: 44 | train_loss: 0.19522840715944767 | validation_loss: 0.2596866004168987\n",
      "Epoch: 45 | train_loss: 0.1918133981525898 | validation_loss: 0.2568371668457985\n",
      "Epoch: 46 | train_loss: 0.18849769234657288 | validation_loss: 0.25436193495988846\n",
      "Epoch: 47 | train_loss: 0.18531766906380653 | validation_loss: 0.2520151771605015\n",
      "Epoch: 48 | train_loss: 0.1822340190410614 | validation_loss: 0.24969900026917458\n",
      "Epoch: 49 | train_loss: 0.17924355529248714 | validation_loss: 0.24743853695690632\n"
     ]
    }
   ],
   "source": [
    "EPOCHS = 50\n",
    "\n",
    "train_epoch_loss = []\n",
    "validation_epoch_loss = []\n",
    "\n",
    "for epoch in range(EPOCHS):\n",
    "    train_loss = []\n",
    "    validation_loss = []\n",
    "\n",
    "    for batch_index, (train_image, train_label) in enumerate(train_loader):\n",
    "        # If GPU is available, move the data to the GPU for faster computation.\n",
    "        if cuda:\n",
    "            #######################################################\n",
    "            ####################### Train #########################\n",
    "            #######################################################\n",
    "            # Set the model to train mode so that the parameters can be updated.\n",
    "            our_CNN.train()\n",
    "\n",
    "            train_label_predicted = our_CNN(train_image.cuda())\n",
    "\n",
    "            # compute the loss\n",
    "            loss = F.cross_entropy(train_label_predicted, train_label.cuda())\n",
    "            train_loss.append(loss.cpu().data.item())\n",
    "\n",
    "            # reset the gradient \n",
    "            optimizer.zero_grad()\n",
    "            # backpropagate the loss\n",
    "            loss.backward()\n",
    "            # update the parameters\n",
    "            optimizer.step()\n",
    "\n",
    "            #######################################################\n",
    "            ###################### Validation #####################\n",
    "            #######################################################\n",
    "            # Set the model to evaluation mode so that parameters are fixed.\n",
    "            our_CNN.eval()\n",
    "\n",
    "            validation_label_predicted = our_CNN(image_validation_torch.cuda())\n",
    "\n",
    "            loss = F.cross_entropy(validation_label_predicted, label_validation_torch.cuda())\n",
    "            validation_loss.append(loss.cpu().data.item())\n",
    "            \n",
    "        \n",
    "        # If GPU is not available.\n",
    "        else:\n",
    "            #######################################################\n",
    "            ####################### Train #########################\n",
    "            #######################################################\n",
    "            # Set the model to train mode so that the parameters can be updated.\n",
    "            our_CNN.train()\n",
    "\n",
    "            train_label_predicted = our_CNN(train_image)\n",
    "\n",
    "            # compute the loss\n",
    "            loss = F.cross_entropy(train_label_predicted, train_label)\n",
    "            train_loss.append(loss.cpu().data.item())\n",
    "\n",
    "            # reset the gradient\n",
    "            optimizer.zero_grad()\n",
    "            # backpropagate the loss\n",
    "            loss.backward()\n",
    "            # update the parameters\n",
    "            optimizer.step()\n",
    "\n",
    "            #######################################################\n",
    "            ###################### Validation #####################\n",
    "            #######################################################\n",
    "            # Set the model to evaluation mode so that parameters are fixed.\n",
    "            our_CNN.eval()\n",
    "\n",
    "            validation_label_predicted = our_CNN(image_validation_torch)\n",
    "\n",
    "            loss = F.cross_entropy(validation_label_predicted, label_validation_torch)\n",
    "            validation_loss.append(loss.cpu().data.item())\n",
    "\n",
    "    # kernal visualiztion\n",
    "    our_CNN.eval()\n",
    "    if epoch % 10 == 0:\n",
    "        params = our_CNN.state_dict()\n",
    "        plt.rcParams['figure.figsize'] = [20, 10]\n",
    "        for k, v in params.items():\n",
    "            # get the first layer weight\n",
    "            if k == 'conv1.weight':\n",
    "                print('The shape of the weights: ', v.shape)\n",
    "                c_int = v.size()[1]     # number of the input channels\n",
    "                c_out = v.size()[0]     # number of the output channels\n",
    "\n",
    "                for j in range(c_out):\n",
    "                    plt.subplot(1, c_out, j + 1)\n",
    "                    kernel = np.squeeze(v[j, :, :, :].cpu())\n",
    "                    print('filter ' + str(j), 'min value: ', kernel.min().item(), ', max value:', kernel.max().item())\n",
    "                    plt.imshow(kernel, cmap = 'gray')\n",
    "                    plt.title('filter ' + str(j))\n",
    "                    plt.axis('off');\n",
    "        plt.show()\n",
    "        \n",
    "        if cuda:\n",
    "            sample_img = sample_data.unsqueeze(0).cuda()\n",
    "        else:\n",
    "            sample_img = sample_data.unsqueeze(0)\n",
    "        \n",
    "        for name, layer in our_CNN._modules.items():\n",
    "            x = sample_img\n",
    "            if name == 'conv1':\n",
    "                x = layer(x)\n",
    "                x = F.relu(x) if 'conv' in name else x\n",
    "\n",
    "                print('The shape of the feature maps: ', x.shape)\n",
    "                c_int = x.size()[0]     # number of the input channels\n",
    "                c_out = x.size()[1]     # number of the output channels\n",
    "\n",
    "                for j in range(c_out):\n",
    "                    plt.subplot(1, c_out, j + 1)\n",
    "                    feature_map = np.squeeze(x[:, j, :, :].cpu().detach())\n",
    "                    plt.imshow(feature_map, cmap = 'gray')\n",
    "                    plt.title('feature_map ' + str(j))\n",
    "                    plt.axis('off');\n",
    "        plt.show()\n",
    "    train_epoch_loss.append(np.mean(train_loss))\n",
    "    validation_epoch_loss.append(np.mean(validation_loss))\n",
    "    \n",
    "    # save models\n",
    "    os.makedirs('./saved_models', exist_ok = True)\n",
    "    torch.save(our_CNN.state_dict(), './saved_models/checkpoint_epoch_%s.pth' % (epoch))\n",
    "\n",
    "    print(\"Epoch: {} | train_loss: {} | validation_loss: {}\".format(epoch, train_epoch_loss[-1], validation_epoch_loss[-1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Explanation on train, validation and test split.\n",
    "While you are waiting for your CNN to be trained, let's look back on what it means to and why we need to perform a train/validation/test split.\n",
    "\n",
    "- The train set is quite straightforward: you allocate a portion of the data to train your model.\n",
    "- The test set is a set of data you keep to yourself and make sure the model doesn't have access to. You need that set of data to evaluate the performance of your model once it is ready to be challenged.\n",
    "- The validation set is a set of data you quiz your model before it is ready for the \"final exam\" given by the test set. It gives you an idea how well the model is likely to perform on an unseen set of data.\n",
    "\n",
    "In an ideal case, you design and train your model using the train data, adjust the hyperparameters (learning rate, etc.) and decide on when to stop the training (either by adjusting # epoch or by setting automated criteria for early stop) along with other intelligent approaches to make sure the model is likely to generalize well, and then test its performance on the test data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Draw the train loss and validation loss curves."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (5, 2), dpi = 200)\n",
    "plt.plot(train_epoch_loss, '-o', label = 'training loss', markersize = 3)\n",
    "plt.plot(validation_epoch_loss, '-o', label = 'validation loss', markersize = 3)\n",
    "plt.legend(loc = 'upper right');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Let's check at which epoch the model performs the best on the validation set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best epoch:  49\n"
     ]
    }
   ],
   "source": [
    "best_epoch = np.argmin(validation_epoch_loss)\n",
    "print('best epoch: ', best_epoch)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### We will call that as our best model and use that for inference on the test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "odict_keys(['conv1.weight', 'conv1.bias', 'conv2.weight', 'conv2.bias', 'fc1.weight', 'fc1.bias', 'fc2.weight', 'fc2.bias'])\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<All keys matched successfully>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "state_dict = torch.load('./saved_models/checkpoint_epoch_%s.pth' % (best_epoch))\n",
    "print(state_dict.keys())\n",
    "our_CNN.load_state_dict(state_dict)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predict the labels on the test set.\n",
    "\n",
    "### 1. Do that on a sample image."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def view_classify(image, probabilities, version = \"MNIST\"):\n",
    "    ''' Function for viewing an image and it's predicted classes.\n",
    "    '''\n",
    "    probabilities = probabilities.data.numpy().squeeze()\n",
    "\n",
    "    fig, (ax1, ax2) = plt.subplots(figsize = (6, 9), ncols = 2)\n",
    "    ax1.imshow(image.resize_(1, 28, 28).numpy().squeeze(), cmap = 'gray')\n",
    "    ax1.set_title('Original Image')\n",
    "    ax1.axis('off')\n",
    "    ax2.bar(np.arange(10), probabilities)\n",
    "    ax2.set_aspect(10)\n",
    "    ax2.set_xticks(np.arange(10))\n",
    "        \n",
    "    ax2.set_title('Class Probability')\n",
    "    ax2.set_ylim(0, 1.1)\n",
    "\n",
    "    plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([1000, 1, 28, 28])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image_test_torch.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "our_CNN.eval()\n",
    "\n",
    "sample_test_image = image_test_torch[0, :, :, :][np.newaxis, :, :, :]\n",
    "\n",
    "if cuda:\n",
    "    sample_prediction = our_CNN(sample_test_image.cuda())\n",
    "else:\n",
    "    sample_prediction = our_CNN(sample_test_image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "if cuda:\n",
    "    view_classify(sample_test_image, 2 ** sample_prediction.cpu())\n",
    "else:\n",
    "    view_classify(sample_test_image, 2 ** sample_prediction)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. This one is good, but what is the overall performance?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict_with_pytorch(model, input_data):\n",
    "    model.eval()\n",
    "    label_predicted_all = []\n",
    "\n",
    "    label_predicted_one_hot = model(input_data)\n",
    "    label_predicted_probability, label_predicted_index = torch.max(label_predicted_one_hot.data, 1)\n",
    "    \n",
    "    for current_prediction in label_predicted_index:\n",
    "        label_predicted_all.append(current_prediction.detach().cpu().numpy().item())\n",
    "\n",
    "    return label_predicted_all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 92.9 %\n"
     ]
    }
   ],
   "source": [
    "if cuda:\n",
    "    test_label_predicted = predict_with_pytorch(our_CNN, image_test_torch.cuda())\n",
    "else:\n",
    "    test_label_predicted = predict_with_pytorch(our_CNN, image_test_torch)\n",
    "\n",
    "accuracy = accuracy_score(label_test, test_label_predicted)\n",
    "\n",
    "print(\"Accuracy:\", accuracy * 100, \"%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Lastly, can I have a more intuitive (and cooler) way to visualize the performance?\n",
    "\n",
    "### Yes! The answer is a [confusion matrix](https://en.wikipedia.org/wiki/Confusion_matrix)! "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1250x1250 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "CM = confusion_matrix(label_test, test_label_predicted)\n",
    "\n",
    "plt.figure(figsize = (5,5), dpi = 250)\n",
    "sns.set(font_scale = 0.5)\n",
    "sns.heatmap(CM, annot = True, annot_kws = {\"size\": 5})\n",
    "plt.ylim([0, 10]);\n",
    "plt.ylabel('True labels');\n",
    "plt.xlabel('predicted labels');"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "[RR (python3.7)]",
   "language": "python",
   "name": "rr"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}