03_pytorch_computer_vision_exercises.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ea359abf-8423-4f5e-8695-a2b4853b21ac",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wed Mar 20 10:33:01 2024       \n",
      "+---------------------------------------------------------------------------------------+\n",
      "| NVIDIA-SMI 545.23.08              Driver Version: 545.23.08    CUDA Version: 12.3     |\n",
      "|-----------------------------------------+----------------------+----------------------+\n",
      "| GPU  Name                 Persistence-M | Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
      "| Fan  Temp   Perf          Pwr:Usage/Cap |         Memory-Usage | GPU-Util  Compute M. |\n",
      "|                                         |                      |               MIG M. |\n",
      "|=========================================+======================+======================|\n",
      "|   0  NVIDIA GeForce RTX 4090        On  | 00000000:01:00.0  On |                  Off |\n",
      "|  0%   35C    P8               9W / 450W |    738MiB / 24564MiB |      2%      Default |\n",
      "|                                         |                      |                  N/A |\n",
      "+-----------------------------------------+----------------------+----------------------+\n",
      "                                                                                         \n",
      "+---------------------------------------------------------------------------------------+\n",
      "| Processes:                                                                            |\n",
      "|  GPU   GI   CI        PID   Type   Process name                            GPU Memory |\n",
      "|        ID   ID                                                             Usage      |\n",
      "|=======================================================================================|\n",
      "|    0   N/A  N/A      1327      G   /usr/lib/xorg/Xorg                          163MiB |\n",
      "|    0   N/A  N/A      1634    C+G   ...libexec/gnome-remote-desktop-daemon      390MiB |\n",
      "|    0   N/A  N/A      1756      G   /usr/bin/gnome-shell                         39MiB |\n",
      "|    0   N/A  N/A      3560      G   ...24,262144 --variations-seed-version      118MiB |\n",
      "+---------------------------------------------------------------------------------------+\n"
     ]
    }
   ],
   "source": [
    "# Check for GPU\n",
    "!nvidia-smi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "13a00485-8193-4692-b7fa-b3d18086b6da",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.1.0+cu118\n"
     ]
    }
   ],
   "source": [
    "# Import torch\n",
    "import torch\n",
    "\n",
    "# Exercises require PyTorch > 1.10.0\n",
    "print(torch.__version__)\n",
    "\n",
    "# TODO: Setup device agnostic code"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4263d2c0-8dd3-4c28-884f-912aba1221bf",
   "metadata": {},
   "source": [
    "1.What are 3 areas in industry where computer vision is currently being used?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6903100-4a54-493c-8a82-dc507f1a463a",
   "metadata": {},
   "source": [
    "Self-driving cars, such as Tesla using computer vision to percieve what's happening on the road. See Tesla AI day for more - https://youtu.be/j0z4FweCy4M\n",
    "Healthcare imaging, such as using computer vision to help interpret X-rays. Google also uses computer vision for detecting polyps in the intenstines - https://ai.googleblog.com/2021/08/improved-detection-of-elusive-polyps.html\n",
    "Security, computer vision can be used to detect whether someone is invading your home or not - https://store.google.com/au/product/nest_cam_battery?hl=en-GB"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a76ee3d-1d88-4872-80a6-89cb7c9680ae",
   "metadata": {},
   "source": [
    "2.Search \"what is overfitting in machine learning\" and write down a sentence about what you find."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22a98e10-20e8-4818-bcc7-7ce6e3a66651",
   "metadata": {},
   "source": [
    "Overfitting is like memorizing for a test but then you can't answer a question that's slightly different.\n",
    "\n",
    "In other words, if a model is overfitting, it's learning the training data too well and these patterns don't generalize to unseen data."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26316385-8378-41c7-96b9-0bb5ed50cfbb",
   "metadata": {},
   "source": [
    "3.Search \"ways to prevent overfitting in machine learning\", write down 3 of the things you find and a sentence about each. Note: there are lots of these, so don't worry too much about all of them, just pick 3 and start with those."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "84fa1d6b-4465-440f-9b80-bfc2542dda40",
   "metadata": {},
   "source": [
    "See this article for some ideas: https://elitedatascience.com/overfitting-in-machine-learning\n",
    "\n",
    "3 ways to prevent overfitting:\n",
    "\n",
    "Regularization techniques - You could use dropout on your neural networks), dropout involves randomly removing neurons in different layers so that the remaining neurons hopefully learn more robust weights/patterns.\n",
    "Use a different model - maybe the model you're using for a specific problem is too complicated, as in, it's learning the data too well because it has so many layers. You could remove some layers to simplify your model. Or you could pick a totally different model altogether, one that may be more suited to your particular problem. Or... you could also use transfer learning (taking the patterns from one model and applying them to your own problem).\n",
    "Reduce noise in data/cleanup dataset/introduce data augmentation techniques - If the model is learning the data too well, it might be just memorizing the data, including the noise. One option would be to remove the noise/clean up the dataset or if this doesn't, you can introduce artificial noise through the use of data augmentation to artificially increase the diversity of your training dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6452f85-80e7-4fc7-bb3e-ce2021430238",
   "metadata": {},
   "source": [
    "4.Spend 20-minutes reading and clicking through the CNN Explainer website.\n",
    "Upload your own example image using the \"upload\" button and see what happens in each layer of a CNN as your image passes through it."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27a47fab-b7ab-4741-bbc0-f70d0a02d26d",
   "metadata": {},
   "source": [
    "https://poloclub.github.io/cnn-explainer/"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ad1a3a2-f17a-4fc3-b2ac-3680db569f20",
   "metadata": {},
   "source": [
    "5.Load the torchvision.datasets.MNIST() train and test datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "abdbca8e-a887-4bbe-9e7b-c5d28d02a22d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torchvision\n",
    "from torchvision import datasets\n",
    "\n",
    "from torchvision import transforms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a75ec3b8-1051-4b14-be7a-7cf50549ec86",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get the MNIST train dataset \n",
    "train_data = datasets.MNIST(root=\".\",\n",
    "                            train=True,\n",
    "                            download=True,\n",
    "                            transform=transforms.ToTensor()) # do we want to transform the data as we download it? \n",
    "\n",
    "# Get the MNIST test dataset\n",
    "test_data = datasets.MNIST(root=\".\",\n",
    "                           train=False,\n",
    "                           download=True,\n",
    "                           transform=transforms.ToTensor()) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d38cbd74-7e36-4fbb-be4e-1b0c4eeeda67",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(Dataset MNIST\n",
       "     Number of datapoints: 60000\n",
       "     Root location: .\n",
       "     Split: Train\n",
       "     StandardTransform\n",
       " Transform: ToTensor(),\n",
       " Dataset MNIST\n",
       "     Number of datapoints: 10000\n",
       "     Root location: .\n",
       "     Split: Test\n",
       "     StandardTransform\n",
       " Transform: ToTensor())"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data, test_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "47a3530a-dcbb-4a67-8a34-a48f45dc0c46",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000, 10000)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train_data), len(test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a1902124-5c8c-40e8-b113-d48cf78a022a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
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      "Image:\n",
      " tensor([[[0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
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      "          0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
      "          0.0000, 0.0000, 0.0000, 0.0000],\n",
      "         [0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
      "          0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
      "          0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
      "          0.0000, 0.0000, 0.0000, 0.0000],\n",
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      "          0.0000, 0.0000, 0.0000, 0.0000],\n",
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      "          0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
      "          0.0000, 0.0000, 0.0000, 0.0000],\n",
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      "          0.0000, 0.0000, 0.0000, 0.0000],\n",
      "         [0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
      "          0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
      "          0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
      "          0.0000, 0.0000, 0.0000, 0.0000],\n",
      "         [0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
      "          0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
      "          0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
      "          0.0000, 0.0000, 0.0000, 0.0000],\n",
      "         [0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
      "          0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
      "          0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
      "          0.0000, 0.0000, 0.0000, 0.0000]]])\n",
      "Label:\n",
      " 5\n"
     ]
    }
   ],
   "source": [
    "# Data is in tuple form (image, label)\n",
    "img = train_data[0][0]\n",
    "label = train_data[0][1]\n",
    "print(f\"Image:\\n {img}\") \n",
    "print(f\"Label:\\n {label}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "850a1376-560f-41ab-8d56-e3dc844d5387",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Image shape: torch.Size([1, 28, 28]) -> [color_channels, height, width] (CHW)\n",
      "Label: 5 -> no shape, due to being integer\n"
     ]
    }
   ],
   "source": [
    "# Check out the shapes of our data\n",
    "print(f\"Image shape: {img.shape} -> [color_channels, height, width] (CHW)\")\n",
    "print(f\"Label: {label} -> no shape, due to being integer\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a7f7db43-8272-4ec0-ace3-21b081edbb03",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['0 - zero',\n",
       " '1 - one',\n",
       " '2 - two',\n",
       " '3 - three',\n",
       " '4 - four',\n",
       " '5 - five',\n",
       " '6 - six',\n",
       " '7 - seven',\n",
       " '8 - eight',\n",
       " '9 - nine']"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the class names from the dataset\n",
    "class_names = train_data.classes\n",
    "class_names"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "182ae802-e768-4dff-8fba-ab2bcd0a3356",
   "metadata": {},
   "source": [
    "6.Visualize at least 5 different samples of the MNIST training dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "af4010fa-687c-4bec-bd9d-2e3ed43051f8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([1, 28, 28])\n",
      "torch.Size([28, 28])\n",
      "torch.Size([1, 28, 28])\n",
      "torch.Size([28, 28])\n",
      "torch.Size([1, 28, 28])\n",
      "torch.Size([28, 28])\n",
      "torch.Size([1, 28, 28])\n",
      "torch.Size([28, 28])\n",
      "torch.Size([1, 28, 28])\n",
      "torch.Size([28, 28])\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 300x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 300x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 300x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 300x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 300x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "for i in range(5):\n",
    "  img = train_data[i][0]\n",
    "  print(img.shape)\n",
    "  img_squeeze = img.squeeze()\n",
    "  print(img_squeeze.shape)\n",
    "  label = train_data[i][1]\n",
    "  plt.figure(figsize=(3, 3))\n",
    "  plt.imshow(img_squeeze, cmap=\"gray\")\n",
    "  plt.title(label)\n",
    "  plt.axis(False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dcb70e58-1fad-41f7-8873-f4cbd4eed122",
   "metadata": {},
   "source": [
    "7.Turn the MNIST train and test datasets into dataloaders using torch.utils.data.DataLoader, set the batch_size=32."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "98976b10-5e61-47b4-b3a1-8283386d2221",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create train dataloader\n",
    "from torch.utils.data import DataLoader\n",
    "\n",
    "train_dataloader = DataLoader(dataset=train_data,\n",
    "                              batch_size=32,\n",
    "                              shuffle=True)\n",
    "\n",
    "test_dataloader = DataLoader(dataset=test_data,\n",
    "                             batch_size=32,\n",
    "                             shuffle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "915760a0-ba52-4e9a-926c-7e0980bb8e2f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<torch.utils.data.dataloader.DataLoader at 0x732e3b3adbd0>,\n",
       " <torch.utils.data.dataloader.DataLoader at 0x732e3b3bc3d0>)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_dataloader, test_dataloader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "eb1bb21b-ff8c-442b-92ea-a87ff692b656",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([32, 1, 28, 28])\n",
      "torch.Size([32])\n"
     ]
    }
   ],
   "source": [
    "for sample in next(iter(train_dataloader)):\n",
    "  print(sample.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9ff59bc2-500b-4379-be35-b71dfe7fccea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1875, 313)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train_dataloader), len(test_dataloader)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fde68115-4ea4-419c-8744-c462cd738207",
   "metadata": {},
   "source": [
    "8.Recreate model_2 used in this notebook (the same model from the CNN Explainer website, also known as TinyVGG) capable of fitting on the MNIST dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b4ffadfa-b13c-47e2-a82f-19ea3132f80a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch import nn \n",
    "class MNIST_model(torch.nn.Module):\n",
    "  \"\"\"Model capable of predicting on MNIST dataset.\n",
    "  \"\"\"\n",
    "  def __init__(self, input_shape: int, hidden_units: int, output_shape: int): \n",
    "    super().__init__()\n",
    "    self.conv_block_1 = nn.Sequential(\n",
    "      nn.Conv2d(in_channels=input_shape, \n",
    "                out_channels=hidden_units,\n",
    "                kernel_size=3,\n",
    "                stride=1,\n",
    "                padding=1), \n",
    "      nn.ReLU(),\n",
    "      nn.Conv2d(in_channels=hidden_units,\n",
    "                out_channels=hidden_units,\n",
    "                kernel_size=3,\n",
    "                stride=1,\n",
    "                padding=1),\n",
    "      nn.ReLU(),\n",
    "      nn.MaxPool2d(kernel_size=2)\n",
    "    )\n",
    "    self.conv_block_2 = nn.Sequential(\n",
    "      nn.Conv2d(in_channels=hidden_units,\n",
    "                out_channels=hidden_units,\n",
    "                kernel_size=3,\n",
    "                stride=1,\n",
    "                padding=1),\n",
    "      nn.ReLU(),\n",
    "      nn.Conv2d(in_channels=hidden_units,\n",
    "                out_channels=hidden_units,\n",
    "                kernel_size=3,\n",
    "                stride=1,\n",
    "                padding=1),\n",
    "      nn.ReLU(),\n",
    "      nn.MaxPool2d(kernel_size=2)                   \n",
    "    )\n",
    "    self.classifier = nn.Sequential(\n",
    "      nn.Flatten(),\n",
    "      nn.Linear(in_features=hidden_units*7*7,\n",
    "                out_features=output_shape)\n",
    "    )\n",
    "\n",
    "  def forward(self, x):\n",
    "    x = self.conv_block_1(x)\n",
    "    # print(f\"Output shape of conv block 1: {x.shape}\")\n",
    "    x = self.conv_block_2(x)\n",
    "    # print(f\"Output shape of conv block 2: {x.shape}\")\n",
    "    x = self.classifier(x)\n",
    "    # print(f\"Output shape of classifier: {x.shape}\")\n",
    "    return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "47986fd9-2a07-47ac-a613-d93e4d29b202",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'cuda'"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Setup device agnostic code\n",
    "import torch\n",
    "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
    "device"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b8d019c3-ee16-4b02-b79e-4adebc250f08",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MNIST_model(\n",
       "  (conv_block_1): Sequential(\n",
       "    (0): Conv2d(1, 10, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (1): ReLU()\n",
       "    (2): Conv2d(10, 10, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (3): ReLU()\n",
       "    (4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "  )\n",
       "  (conv_block_2): Sequential(\n",
       "    (0): Conv2d(10, 10, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (1): ReLU()\n",
       "    (2): Conv2d(10, 10, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (3): ReLU()\n",
       "    (4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "  )\n",
       "  (classifier): Sequential(\n",
       "    (0): Flatten(start_dim=1, end_dim=-1)\n",
       "    (1): Linear(in_features=490, out_features=10, bias=True)\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = MNIST_model(input_shape=1,\n",
    "                    hidden_units=10,\n",
    "                    output_shape=10).to(device)\n",
    "model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "7daba8a8-b964-4ef1-b48a-fd79aed79835",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Check out the model state dict to find out what patterns our model wants to learn\n",
    "#model.state_dict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "6823edd3-8f1d-449d-a046-50a105fa1520",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([1, 1, 28, 28])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Try a dummy forward pass to see what shapes our data is\n",
    "dummy_x = torch.rand(size=(1, 28, 28)).unsqueeze(dim=0).to(device)\n",
    "dummy_x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "9c426968-1951-4fc6-a7d1-3dc28906edae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[-0.0587,  0.0155,  0.0134,  0.0730,  0.0113,  0.0214, -0.0127,  0.0174,\n",
       "          0.0296, -0.0071]], device='cuda:0', grad_fn=<AddmmBackward0>)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model(dummy_x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "724af53e-956a-4d98-8c62-26ede17c0add",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([1, 10, 7, 7])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dummy_x_2 = torch.rand(size=([1, 10, 7, 7]))\n",
    "dummy_x_2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "734d6caf-9fdf-490c-805d-2b878c9328fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([1, 490])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flatten_layer = nn.Flatten()\n",
    "flatten_layer(dummy_x_2).shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ea69385-cab6-43db-9f03-9c48936e4575",
   "metadata": {},
   "source": [
    "9.Train the model you built in exercise 8. on CPU and GPU and see how long it takes on each."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "8ba268d8-d29d-4d5e-949b-0c7df2bb20fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6e370e317083445b9c014b235343db94",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/5 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0 | Loss: 0.320 | Test loss: 0.070\n",
      "Epoch: 1 | Loss: 0.077 | Test loss: 0.063\n",
      "Epoch: 2 | Loss: 0.060 | Test loss: 0.048\n",
      "Epoch: 3 | Loss: 0.053 | Test loss: 0.051\n",
      "Epoch: 4 | Loss: 0.046 | Test loss: 0.049\n",
      "CPU times: user 15min 45s, sys: 7.49 s, total: 15min 53s\n",
      "Wall time: 45.1 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "from tqdm.auto import tqdm \n",
    "\n",
    "# Train on CPU\n",
    "model_cpu = MNIST_model(input_shape=1,\n",
    "                        hidden_units=10,\n",
    "                        output_shape=10).to(\"cpu\")\n",
    "\n",
    "# Create a loss function and optimizer\n",
    "loss_fn = nn.CrossEntropyLoss()\n",
    "optimizer = torch.optim.SGD(model_cpu.parameters(), lr=0.1)\n",
    "\n",
    "### Training loop\n",
    "epochs = 5\n",
    "for epoch in tqdm(range(epochs)):\n",
    "  train_loss = 0\n",
    "  for batch, (X, y) in enumerate(train_dataloader):\n",
    "    model_cpu.train()\n",
    "\n",
    "    # Put data on CPU\n",
    "    X, y = X.to(\"cpu\"), y.to(\"cpu\")\n",
    "\n",
    "    # Forward pass\n",
    "    y_pred = model_cpu(X)\n",
    "\n",
    "    # Loss calculation\n",
    "    loss = loss_fn(y_pred, y)\n",
    "    train_loss += loss\n",
    "\n",
    "    # Optimizer zero grad\n",
    "    optimizer.zero_grad()\n",
    "\n",
    "    # Loss backward\n",
    "    loss.backward()\n",
    "\n",
    "    # Step the optimizer\n",
    "    optimizer.step()\n",
    "  \n",
    "  # Adjust train loss for number of batches\n",
    "  train_loss /= len(train_dataloader)\n",
    "\n",
    "  ### Testing loop\n",
    "  test_loss_total = 0\n",
    "\n",
    "  # Put model in eval mode \n",
    "  model_cpu.eval()\n",
    "\n",
    "  # Turn on inference mode\n",
    "  with torch.inference_mode():\n",
    "    for batch, (X_test, y_test) in enumerate(test_dataloader):\n",
    "      # Make sure test data on CPU\n",
    "      X_test, y_test = X_test.to(\"cpu\"), y_test.to(\"cpu\")\n",
    "      test_pred = model_cpu(X_test)\n",
    "      test_loss = loss_fn(test_pred, y_test)\n",
    "\n",
    "      test_loss_total += test_loss\n",
    "    \n",
    "    test_loss_total /= len(test_dataloader)\n",
    "  \n",
    "  # Print out what's happening\n",
    "  print(f\"Epoch: {epoch} | Loss: {train_loss:.3f} | Test loss: {test_loss_total:.3f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "291ff68f-cebc-4346-83f8-27d8f78c49b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8ceb38c530cb4fcba5bb6e8873864a27",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/5 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0 | Loss: 0.327 | Test loss: 0.071\n",
      "Epoch: 1 | Loss: 0.077 | Test loss: 0.051\n",
      "Epoch: 2 | Loss: 0.058 | Test loss: 0.049\n",
      "Epoch: 3 | Loss: 0.051 | Test loss: 0.041\n",
      "Epoch: 4 | Loss: 0.044 | Test loss: 0.045\n",
      "CPU times: user 13.4 s, sys: 107 ms, total: 13.5 s\n",
      "Wall time: 12.2 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "from tqdm.auto import tqdm \n",
    "\n",
    "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
    "\n",
    "# Train on GPU\n",
    "model_gpu = MNIST_model(input_shape=1,\n",
    "                        hidden_units=10,\n",
    "                        output_shape=10).to(device)\n",
    "\n",
    "# Create a loss function and optimizer\n",
    "loss_fn = nn.CrossEntropyLoss()\n",
    "optimizer = torch.optim.SGD(model_gpu.parameters(), lr=0.1)\n",
    "\n",
    "# Training loop\n",
    "epochs = 5\n",
    "for epoch in tqdm(range(epochs)):\n",
    "  train_loss = 0\n",
    "  model_gpu.train()\n",
    "  for batch, (X, y) in enumerate(train_dataloader):\n",
    "    # Put data on target device\n",
    "    X, y = X.to(device), y.to(device)\n",
    "\n",
    "    # Forward pass\n",
    "    y_pred = model_gpu(X)\n",
    "\n",
    "    # Loss calculation\n",
    "    loss = loss_fn(y_pred, y)\n",
    "    train_loss += loss\n",
    "\n",
    "    # Optimizer zero grad\n",
    "    optimizer.zero_grad()\n",
    "\n",
    "    # Loss backward\n",
    "    loss.backward()\n",
    "\n",
    "    # Step the optimizer\n",
    "    optimizer.step()\n",
    "  \n",
    "  # Adjust train loss to number of batches\n",
    "  train_loss /= len(train_dataloader)\n",
    "\n",
    "  ### Testing loop\n",
    "  test_loss_total = 0\n",
    "  # Put model in eval mode and turn on inference mode\n",
    "  model_gpu.eval()\n",
    "  with torch.inference_mode():\n",
    "    for batch, (X_test, y_test) in enumerate(test_dataloader):\n",
    "      # Make sure test data on target device\n",
    "      X_test, y_test = X_test.to(device), y_test.to(device)\n",
    "      \n",
    "      test_pred = model_gpu(X_test)\n",
    "      test_loss = loss_fn(test_pred, y_test)\n",
    "\n",
    "      test_loss_total += test_loss\n",
    "\n",
    "    # Adjust test loss total for number of batches\n",
    "    test_loss_total /= len(test_dataloader)\n",
    "  \n",
    "  # Print out what's happening\n",
    "  print(f\"Epoch: {epoch} | Loss: {train_loss:.3f} | Test loss: {test_loss_total:.3f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "060516ce-5290-4386-9dfd-2079310c7b5a",
   "metadata": {},
   "source": [
    "10.Make predictions using your trained model and visualize at least 5 of them comparing the prediciton to the target label."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "6190f9ab-0ff7-47da-af64-7eb0dd9ba5dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([1, 28, 28])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data[0][0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "58ea75f5-4822-4b71-b205-1a449cd70fff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x732e388820d0>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Make predictions with the trained model\n",
    "plt.imshow(test_data[0][0].squeeze(), cmap=\"gray\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "d0de8df1-793b-4f86-bafe-30c45c384918",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([7], device='cuda:0')"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Logits -> Prediction probabilities -> Prediction labels\n",
    "model_pred_logits = model_gpu(test_data[0][0].unsqueeze(dim=0).to(device)) # make sure image is right shape + on right device\n",
    "model_pred_probs = torch.softmax(model_pred_logits, dim=1)\n",
    "model_pred_label = torch.argmax(model_pred_probs, dim=1)\n",
    "model_pred_label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "90309c4b-8aac-487d-a195-072faf9fe0ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "num_to_plot = 5 \n",
    "for i in range(num_to_plot):\n",
    "  # Get image and labels from the test data\n",
    "  img = test_data[i][0]\n",
    "  label = test_data[i][1]\n",
    "\n",
    "  # Make prediction on image\n",
    "  model_pred_logits = model_gpu(img.unsqueeze(dim=0).to(device))\n",
    "  model_pred_probs = torch.softmax(model_pred_logits, dim=1)\n",
    "  model_pred_label = torch.argmax(model_pred_probs, dim=1)\n",
    "\n",
    "  # Plot the image and prediction\n",
    "  plt.figure()\n",
    "  plt.imshow(img.squeeze(), cmap=\"gray\")\n",
    "  plt.title(f\"Truth: {label} | Pred: {model_pred_label.cpu().item()}\") \n",
    "  plt.axis(False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "340ee624-307b-40b9-81ff-bcd7672abb9f",
   "metadata": {},
   "source": [
    "11.Plot a confusion matrix comparing your model's predictions to the truth labels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "da118c7f-23af-4951-9739-0d2cb35ee2f4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mlxtend version: 0.23.1\n"
     ]
    }
   ],
   "source": [
    "# See if torchmetrics exists, if not, install it\n",
    "try:\n",
    "    import torchmetrics, mlxtend\n",
    "    print(f\"mlxtend version: {mlxtend.__version__}\")\n",
    "    assert int(mlxtend.__version__.split(\".\")[1]) >= 19, \"mlxtend verison should be 0.19.0 or higher\"\n",
    "except:\n",
    "    !pip install -q torchmetrics -U mlxtend # <- Note: If you're using Google Colab, this may require restarting the runtime\n",
    "    import torchmetrics, mlxtend\n",
    "    print(f\"mlxtend version: {mlxtend.__version__}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "f441f1e7-bf05-4065-9854-cda1370a7e66",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.23.1\n"
     ]
    }
   ],
   "source": [
    "# Import mlxtend upgraded version\n",
    "import mlxtend \n",
    "print(mlxtend.__version__)\n",
    "assert int(mlxtend.__version__.split(\".\")[1]) >= 19 # should be version 0.19.0 or higher"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "1d51391e-eb6b-4a43-ba48-41a36907d511",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2fe64cbdc540483d85f646610a0ef177",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "10000"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Make predictions across all test data\n",
    "from tqdm.auto import tqdm\n",
    "model_gpu.eval()\n",
    "y_preds = []\n",
    "with torch.inference_mode():\n",
    "  for batch, (X, y) in tqdm(enumerate(test_dataloader)):\n",
    "    # Make sure data on right device\n",
    "    X, y = X.to(device), y.to(device)\n",
    "    # Forward pass\n",
    "    y_pred_logits = model_gpu(X)\n",
    "    # Logits -> Pred probs -> Pred label\n",
    "    y_pred_labels = torch.argmax(torch.softmax(y_pred_logits, dim=1), dim=1)\n",
    "    # Append the labels to the preds list\n",
    "    y_preds.append(y_pred_labels)\n",
    "  y_preds=torch.cat(y_preds).cpu()\n",
    "len(y_preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "4ff46ca5-c57a-4272-bdde-87dabade0003",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(tensor([7, 2, 1, 0, 4, 1, 4, 9, 5, 9]),\n",
       " tensor([7, 2, 1, 0, 4, 1, 4, 9, 5, 9]))"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.targets[:10], y_preds[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "f130fc8b-0cef-41bd-b0ab-8f301f1f9f0d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from torchmetrics import ConfusionMatrix\n",
    "from mlxtend.plotting import plot_confusion_matrix\n",
    "\n",
    "# Setup confusion matrix \n",
    "confmat = ConfusionMatrix(task=\"multiclass\", num_classes=len(class_names))\n",
    "confmat_tensor = confmat(preds=y_preds,\n",
    "                         target=test_data.targets)\n",
    "\n",
    "# Plot the confusion matrix\n",
    "fix, ax = plot_confusion_matrix(\n",
    "    conf_mat=confmat_tensor.numpy(),\n",
    "    class_names=class_names,\n",
    "    figsize=(10, 7)\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c846ef7-799d-42c3-ae80-39546feb6efd",
   "metadata": {},
   "source": [
    "12.Create a random tensor of shape [1, 3, 64, 64] and pass it through a nn.Conv2d() layer with various hyperparameter settings (these can be any settings you choose), what do you notice if the kernel_size parameter goes up and down?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "062950e0-c391-4ccc-af25-e2993947ae61",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([1, 3, 64, 64])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random_tensor = torch.rand([1, 3, 64, 64])\n",
    "random_tensor.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "0cbd6555-a029-4d63-b119-7573804a4977",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random tensor original shape: torch.Size([1, 3, 64, 64])\n",
      "Random tensor through conv layer shape: torch.Size([1, 64, 32, 32])\n"
     ]
    }
   ],
   "source": [
    "conv_layer = nn.Conv2d(in_channels=3,\n",
    "                       out_channels=64,\n",
    "                       kernel_size=3,\n",
    "                       stride=2,\n",
    "                       padding=1)\n",
    "\n",
    "print(f\"Random tensor original shape: {random_tensor.shape}\")\n",
    "random_tensor_through_conv_layer = conv_layer(random_tensor)\n",
    "print(f\"Random tensor through conv layer shape: {random_tensor_through_conv_layer.shape}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "e8600b3f-c60d-40ba-942b-cb522b029cb9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random tensor original shape: torch.Size([1, 3, 64, 64])\n",
      "Random tensor through conv layer shape: torch.Size([1, 64, 64, 64])\n"
     ]
    }
   ],
   "source": [
    "conv_layer = nn.Conv2d(in_channels=3,\n",
    "                       out_channels=64,\n",
    "                       kernel_size=3,\n",
    "                       stride=1,\n",
    "                       padding=1)\n",
    "\n",
    "print(f\"Random tensor original shape: {random_tensor.shape}\")\n",
    "random_tensor_through_conv_layer = conv_layer(random_tensor)\n",
    "print(f\"Random tensor through conv layer shape: {random_tensor_through_conv_layer.shape}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "0c2b8ecd-4d09-49db-a754-0fcc41bce1f1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random tensor original shape: torch.Size([1, 3, 64, 64])\n",
      "Random tensor through conv layer shape: torch.Size([1, 64, 31, 31])\n"
     ]
    }
   ],
   "source": [
    "conv_layer = nn.Conv2d(in_channels=3,\n",
    "                       out_channels=64,\n",
    "                       kernel_size=5,\n",
    "                       stride=2,\n",
    "                       padding=1)\n",
    "\n",
    "print(f\"Random tensor original shape: {random_tensor.shape}\")\n",
    "random_tensor_through_conv_layer = conv_layer(random_tensor)\n",
    "print(f\"Random tensor through conv layer shape: {random_tensor_through_conv_layer.shape}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "ce57d806-60b7-420a-bbbc-68a1915c93c5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random tensor original shape: torch.Size([1, 3, 64, 64])\n",
      "Random tensor through conv layer shape: torch.Size([1, 64, 62, 62])\n"
     ]
    }
   ],
   "source": [
    "conv_layer = nn.Conv2d(in_channels=3,\n",
    "                       out_channels=64,\n",
    "                       kernel_size=5,\n",
    "                       stride=1,\n",
    "                       padding=1)\n",
    "\n",
    "print(f\"Random tensor original shape: {random_tensor.shape}\")\n",
    "random_tensor_through_conv_layer = conv_layer(random_tensor)\n",
    "print(f\"Random tensor through conv layer shape: {random_tensor_through_conv_layer.shape}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06a249aa-a49a-4cad-a44b-e5e3d301ca2e",
   "metadata": {},
   "source": [
    "13.Use a model similar to the trained model_2 from this notebook to make predictions on the test torchvision.datasets.FashionMNIST dataset.\n",
    "Then plot some predictions where the model was wrong alongside what the label of the image should've been.\n",
    "After visualing these predictions do you think it's more of a modelling error or a data error?\n",
    "As in, could the model do better or are the labels of the data too close to each other (e.g. a \"Shirt\" label is too close to \"T-shirt/top\")?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "310b2297-34fd-4f2a-b1ca-03bdd4de48b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000, 10000)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Download FashionMNIST train & test\n",
    "from torchvision import datasets\n",
    "from torchvision import transforms\n",
    "\n",
    "fashion_mnist_train = datasets.FashionMNIST(root=\"data\",\n",
    "                                            download=True,\n",
    "                                            train=True,\n",
    "                                            transform=transforms.ToTensor())\n",
    "\n",
    "fashion_mnist_test = datasets.FashionMNIST(root=\"data\",\n",
    "                                           train=False,\n",
    "                                           download=True,\n",
    "                                           transform=transforms.ToTensor())\n",
    "\n",
    "len(fashion_mnist_train), len(fashion_mnist_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "e2a04505-5d93-4d4e-88cb-97d6619c1067",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['T-shirt/top',\n",
       " 'Trouser',\n",
       " 'Pullover',\n",
       " 'Dress',\n",
       " 'Coat',\n",
       " 'Sandal',\n",
       " 'Shirt',\n",
       " 'Sneaker',\n",
       " 'Bag',\n",
       " 'Ankle boot']"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the class names of the Fashion MNIST dataset\n",
    "fashion_mnist_class_names = fashion_mnist_train.classes\n",
    "fashion_mnist_class_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "717923db-be77-4c31-89b3-b2c3e2fbc530",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1875, 313)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Turn FashionMNIST datasets into dataloaders\n",
    "from torch.utils.data import DataLoader\n",
    "\n",
    "fashion_mnist_train_dataloader = DataLoader(fashion_mnist_train,\n",
    "                                            batch_size=32,\n",
    "                                            shuffle=True)\n",
    "\n",
    "fashion_mnist_test_dataloader = DataLoader(fashion_mnist_test,\n",
    "                                           batch_size=32,\n",
    "                                           shuffle=False)\n",
    "\n",
    "len(fashion_mnist_train_dataloader), len(fashion_mnist_test_dataloader)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "c3785c82-c0af-44d6-a4dc-8c2a95566941",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MNIST_model(\n",
       "  (conv_block_1): Sequential(\n",
       "    (0): Conv2d(1, 10, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (1): ReLU()\n",
       "    (2): Conv2d(10, 10, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (3): ReLU()\n",
       "    (4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "  )\n",
       "  (conv_block_2): Sequential(\n",
       "    (0): Conv2d(10, 10, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (1): ReLU()\n",
       "    (2): Conv2d(10, 10, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (3): ReLU()\n",
       "    (4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "  )\n",
       "  (classifier): Sequential(\n",
       "    (0): Flatten(start_dim=1, end_dim=-1)\n",
       "    (1): Linear(in_features=490, out_features=10, bias=True)\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# model_2 is the same architecture as MNIST_model\n",
    "model_2 = MNIST_model(input_shape=1,\n",
    "                      hidden_units=10,\n",
    "                      output_shape=10).to(device)\n",
    "model_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "7bb265dc-2aec-4934-a910-1d6edc2b6a37",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Setup loss and optimizer\n",
    "from torch import nn\n",
    "loss_fn = nn.CrossEntropyLoss()\n",
    "optimizer = torch.optim.SGD(model_2.parameters(), lr=0.01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "7c046cf2-104c-42c9-b5fb-60f85fd5a84c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "35f4dcb0cd2245de837a2784123aa97b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/5 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0 | Train loss: 1.132 | Train acc: 0.60 | Test loss: 193.603 | Test acc: 0.78\n",
      "Epoch: 1 | Train loss: 0.506 | Train acc: 0.82 | Test loss: 151.362 | Test acc: 0.83\n",
      "Epoch: 2 | Train loss: 0.438 | Train acc: 0.84 | Test loss: 139.229 | Test acc: 0.84\n",
      "Epoch: 3 | Train loss: 0.405 | Train acc: 0.85 | Test loss: 133.510 | Test acc: 0.85\n",
      "Epoch: 4 | Train loss: 0.379 | Train acc: 0.86 | Test loss: 126.600 | Test acc: 0.86\n"
     ]
    }
   ],
   "source": [
    "# Setup metrics\n",
    "from tqdm.auto import tqdm\n",
    "from torchmetrics import Accuracy\n",
    "\n",
    "acc_fn = Accuracy(task = 'multiclass', num_classes=len(fashion_mnist_class_names)).to(device) \n",
    "\n",
    "# Setup training/testing loop\n",
    "epochs = 5\n",
    "for epoch in tqdm(range(epochs)):\n",
    "  train_loss, test_loss_total = 0, 0\n",
    "  train_acc, test_acc = 0, 0 \n",
    "\n",
    "  ### Training\n",
    "  model_2.train()\n",
    "  for batch, (X_train, y_train) in enumerate(fashion_mnist_train_dataloader):\n",
    "    X_train, y_train = X_train.to(device), y_train.to(device)\n",
    "\n",
    "    # Forward pass and loss\n",
    "    y_pred = model_2(X_train)\n",
    "    loss = loss_fn(y_pred, y_train)\n",
    "    train_loss += loss\n",
    "    train_acc += acc_fn(y_pred, y_train)\n",
    "\n",
    "    # Backprop and gradient descent\n",
    "    optimizer.zero_grad()\n",
    "    loss.backward() \n",
    "    optimizer.step()\n",
    "\n",
    "  # Adjust the loss/acc (find the loss/acc per epoch)\n",
    "  train_loss /= len(fashion_mnist_train_dataloader)\n",
    "  train_acc /= len(fashion_mnist_train_dataloader)\n",
    "\n",
    "  ### Testing\n",
    "  model_2.eval()\n",
    "  with torch.inference_mode():\n",
    "    for batch, (X_test, y_test) in enumerate(fashion_mnist_test_dataloader):\n",
    "      X_test, y_test = X_test.to(device), y_test.to(device)\n",
    "\n",
    "      # Forward pass and loss\n",
    "      y_pred_test = model_2(X_test)\n",
    "      test_loss = loss_fn(y_pred_test, y_test)\n",
    "      test_loss_total += test_loss\n",
    "\n",
    "      test_acc += acc_fn(y_pred_test, y_test)\n",
    "  \n",
    "    # Adjust the loss/acc (find the loss/acc per epoch)\n",
    "    test_loss /= len(fashion_mnist_test_dataloader)\n",
    "    test_acc /= len(fashion_mnist_test_dataloader)\n",
    "    \n",
    "  # Print out what's happening\n",
    "  print(f\"Epoch: {epoch} | Train loss: {train_loss:.3f} | Train acc: {train_acc:.2f} | Test loss: {test_loss_total:.3f} | Test acc: {test_acc:.2f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "2cfe91de-c54d-41bb-8171-9e59cb0b5235",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9818b5b983fa4a1397e61c4b4db640f9",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/313 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(tensor([9, 2, 1, 1, 6, 1, 4, 6, 5, 7]), 10000)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Make predictions with trained model_2\n",
    "test_preds = []\n",
    "model_2.eval()\n",
    "with torch.inference_mode():\n",
    "  for X_test, y_test in tqdm(fashion_mnist_test_dataloader):\n",
    "    y_logits = model_2(X_test.to(device))\n",
    "    y_pred_probs = torch.softmax(y_logits, dim=1)\n",
    "    y_pred_labels = torch.argmax(y_pred_probs, dim=1)\n",
    "    test_preds.append(y_pred_labels)\n",
    "test_preds = torch.cat(test_preds).cpu() # matplotlib likes CPU\n",
    "test_preds[:10], len(test_preds)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "67f8071f-2ebe-455b-947e-1bc899cd7a39",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1442"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get wrong prediction indexes\n",
    "import numpy as np\n",
    "wrong_pred_indexes = np.where(test_preds != fashion_mnist_test.targets)[0]\n",
    "len(wrong_pred_indexes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "3a3da87a-96e5-424b-9469-07d55875ad99",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Select random 9 wrong predictions and plot them\n",
    "import random\n",
    "random_selection = random.sample(list(wrong_pred_indexes), k=9)\n",
    "\n",
    "plt.figure(figsize=(10, 10))\n",
    "for i, idx in enumerate(random_selection):\n",
    "  # Get true and pred labels\n",
    "  true_label = fashion_mnist_class_names[fashion_mnist_test[idx][1]]\n",
    "  pred_label = fashion_mnist_class_names[test_preds[idx]]\n",
    "\n",
    "  # Plot the wrong prediction with its original label\n",
    "  plt.subplot(3, 3, i+1)\n",
    "  plt.imshow(fashion_mnist_test[idx][0].squeeze(), cmap=\"gray\")\n",
    "  plt.title(f\"True: {true_label} | Pred: {pred_label}\", c=\"r\")\n",
    "  plt.axis(False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b8c3abe-bad2-4c23-b85f-117c78d2f345",
   "metadata": {},
   "source": [
    "From the look of some of these predictions, the model is getting about as confused as I would...\n",
    "\n",
    "For example it predicts \"Coat\" instead of \"Shirt\" when it could have easily been a \"Coat\".\n",
    "\n",
    "The same goes for the confusion between the classes of \"Coat\" and \"Dress\", many of the examples here look similar."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce3bdb45-43bd-4134-b910-3421e84ab811",
   "metadata": {},
   "source": [
    "now let's test with a different model with dropout,batchnorm2d and kernel size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "f62d0328-aea6-491a-b999-9cb5a89502da",
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch import nn \n",
    "class MNIST_dropout_model(torch.nn.Module):\n",
    "  \"\"\"Model capable of predicting on MNIST dataset.\n",
    "  \"\"\"\n",
    "  def __init__(self, input_shape: int, hidden_units: int, output_shape: int): \n",
    "    super().__init__()\n",
    "    self.conv_block_1 = nn.Sequential(\n",
    "      nn.Conv2d(in_channels=input_shape, \n",
    "                out_channels=hidden_units,\n",
    "                kernel_size=5,\n",
    "                stride=1,\n",
    "                padding=1), \n",
    "      nn.BatchNorm2d(hidden_units),\n",
    "      nn.ReLU(),\n",
    "      nn.Conv2d(in_channels=hidden_units,\n",
    "                out_channels=hidden_units,\n",
    "                kernel_size=5,\n",
    "                stride=1,\n",
    "                padding=1),\n",
    "      nn.BatchNorm2d(hidden_units),\n",
    "      nn.ReLU(),\n",
    "      nn.MaxPool2d(kernel_size=2)\n",
    "    )\n",
    "    self.conv_block_2 = nn.Sequential(\n",
    "      nn.Conv2d(in_channels=hidden_units,\n",
    "                out_channels=hidden_units,\n",
    "                kernel_size=5,\n",
    "                stride=1,\n",
    "                padding=1),\n",
    "      nn.ReLU(),\n",
    "      nn.BatchNorm2d(hidden_units),\n",
    "      nn.Conv2d(in_channels=hidden_units,\n",
    "                out_channels=hidden_units,\n",
    "                kernel_size=5,\n",
    "                stride=1,\n",
    "                padding=1),\n",
    "      nn.ReLU(),\n",
    "      nn.BatchNorm2d(hidden_units),\n",
    "      nn.MaxPool2d(kernel_size=2)                   \n",
    "    )\n",
    "\n",
    "    self.fc = nn.Sequential(\n",
    "      nn.Dropout(0.5),\n",
    "      nn.ReLU())\n",
    "\n",
    "    self.classifier = nn.Sequential(\n",
    "      nn.Flatten(),\n",
    "      nn.Linear(in_features=160,\n",
    "                out_features=output_shape)\n",
    "    )\n",
    "      \n",
    "  def forward(self, x):\n",
    "    x = self.conv_block_1(x)\n",
    "    # print(f\"Output shape of conv block 1: {x.shape}\")\n",
    "    x = self.conv_block_2(x)\n",
    "    # print(f\"Output shape of conv block 2: {x.shape}\")\n",
    "    x = self.fc(x)\n",
    "    x = self.classifier(x)\n",
    "    # print(f\"Output shape of classifier: {x.shape}\")\n",
    "    return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "352d56a4-ed1b-4bb6-9423-5e467ee335f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MNIST_dropout_model(\n",
       "  (conv_block_1): Sequential(\n",
       "    (0): Conv2d(1, 10, kernel_size=(5, 5), stride=(1, 1), padding=(1, 1))\n",
       "    (1): BatchNorm2d(10, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    (2): ReLU()\n",
       "    (3): Conv2d(10, 10, kernel_size=(5, 5), stride=(1, 1), padding=(1, 1))\n",
       "    (4): BatchNorm2d(10, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    (5): ReLU()\n",
       "    (6): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "  )\n",
       "  (conv_block_2): Sequential(\n",
       "    (0): Conv2d(10, 10, kernel_size=(5, 5), stride=(1, 1), padding=(1, 1))\n",
       "    (1): ReLU()\n",
       "    (2): BatchNorm2d(10, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    (3): Conv2d(10, 10, kernel_size=(5, 5), stride=(1, 1), padding=(1, 1))\n",
       "    (4): ReLU()\n",
       "    (5): BatchNorm2d(10, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    (6): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "  )\n",
       "  (fc): Sequential(\n",
       "    (0): Dropout(p=0.5, inplace=False)\n",
       "    (1): ReLU()\n",
       "  )\n",
       "  (classifier): Sequential(\n",
       "    (0): Flatten(start_dim=1, end_dim=-1)\n",
       "    (1): Linear(in_features=160, out_features=10, bias=True)\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_dropout = MNIST_dropout_model(input_shape=1,\n",
    "                      hidden_units=10,\n",
    "                      output_shape=10).to(device)\n",
    "model_dropout"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "5c2ea65b-61d1-4822-9a6b-ab7e1d057ab8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Setup loss and AdamW optimizer\n",
    "from torch import nn\n",
    "loss_fn = nn.CrossEntropyLoss()\n",
    "optimizer = torch.optim.AdamW(model_dropout.parameters(), lr=0.01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "59ee689e-7221-46e2-9692-63d82026812e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "69f205abd05d40769cb0b0a2c672ffe2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/5 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0 | Train loss: 0.594 | Train acc: 0.78 | Test loss: 125.635 | Test acc: 0.85\n",
      "Epoch: 1 | Train loss: 0.450 | Train acc: 0.83 | Test loss: 113.724 | Test acc: 0.87\n",
      "Epoch: 2 | Train loss: 0.413 | Train acc: 0.85 | Test loss: 110.323 | Test acc: 0.87\n",
      "Epoch: 3 | Train loss: 0.394 | Train acc: 0.86 | Test loss: 103.211 | Test acc: 0.88\n",
      "Epoch: 4 | Train loss: 0.381 | Train acc: 0.86 | Test loss: 102.905 | Test acc: 0.88\n"
     ]
    }
   ],
   "source": [
    "# Setup metrics\n",
    "from tqdm.auto import tqdm\n",
    "from torchmetrics import Accuracy\n",
    "\n",
    "acc_fn = Accuracy(task = 'multiclass', num_classes=len(fashion_mnist_class_names)).to(device) \n",
    "\n",
    "# Setup training/testing loop\n",
    "epochs = 5\n",
    "for epoch in tqdm(range(epochs)):\n",
    "  train_loss, test_loss_total = 0, 0\n",
    "  train_acc, test_acc = 0, 0 \n",
    "\n",
    "  ### Training\n",
    "  model_dropout.train()\n",
    "  for batch, (X_train, y_train) in enumerate(fashion_mnist_train_dataloader):\n",
    "    X_train, y_train = X_train.to(device), y_train.to(device)\n",
    "\n",
    "    # Forward pass and loss\n",
    "    y_pred = model_dropout(X_train)\n",
    "    loss = loss_fn(y_pred, y_train)\n",
    "    train_loss += loss\n",
    "    train_acc += acc_fn(y_pred, y_train)\n",
    "\n",
    "    # Backprop and gradient descent\n",
    "    optimizer.zero_grad()\n",
    "    loss.backward() \n",
    "    optimizer.step()\n",
    "\n",
    "  # Adjust the loss/acc (find the loss/acc per epoch)\n",
    "  train_loss /= len(fashion_mnist_train_dataloader)\n",
    "  train_acc /= len(fashion_mnist_train_dataloader)\n",
    "\n",
    "  ### Testing\n",
    "  model_dropout.eval()\n",
    "  with torch.inference_mode():\n",
    "    for batch, (X_test, y_test) in enumerate(fashion_mnist_test_dataloader):\n",
    "      X_test, y_test = X_test.to(device), y_test.to(device)\n",
    "\n",
    "      # Forward pass and loss\n",
    "      y_pred_test = model_dropout(X_test)\n",
    "      test_loss = loss_fn(y_pred_test, y_test)\n",
    "      test_loss_total += test_loss\n",
    "\n",
    "      test_acc += acc_fn(y_pred_test, y_test)\n",
    "  \n",
    "    # Adjust the loss/acc (find the loss/acc per epoch)\n",
    "    test_loss /= len(fashion_mnist_test_dataloader)\n",
    "    test_acc /= len(fashion_mnist_test_dataloader)\n",
    "    \n",
    "  # Print out what's happening\n",
    "  print(f\"Epoch: {epoch} | Train loss: {train_loss:.3f} | Train acc: {train_acc:.2f} | Test loss: {test_loss_total:.3f} | Test acc: {test_acc:.2f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "4ec9e3ec-a98a-466e-98a5-edccdd227f0a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "57382189088645d18623c8fe0409bc1d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/313 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(tensor([9, 2, 1, 1, 6, 1, 4, 6, 5, 7]), 10000)"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Make predictions with trained model_2\n",
    "test_preds = []\n",
    "model_dropout.eval()\n",
    "with torch.inference_mode():\n",
    "  for X_test, y_test in tqdm(fashion_mnist_test_dataloader):\n",
    "    y_logits = model_dropout(X_test.to(device))\n",
    "    y_pred_probs = torch.softmax(y_logits, dim=1)\n",
    "    y_pred_labels = torch.argmax(y_pred_probs, dim=1)\n",
    "    test_preds.append(y_pred_labels)\n",
    "test_preds = torch.cat(test_preds).cpu() # matplotlib likes CPU\n",
    "test_preds[:10], len(test_preds)  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e091c0d-e0da-46ab-96dd-5d43ee5c7a81",
   "metadata": {},
   "source": [
    "as u can see, the new model generalized on test data well than the default model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "a585eedf-f420-4af7-89a2-15a7eba61ed3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1214"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get wrong prediction indexes\n",
    "import numpy as np\n",
    "wrong_pred_indexes = np.where(test_preds != fashion_mnist_test.targets)[0]\n",
    "len(wrong_pred_indexes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "88b957ed-29a7-448d-aa3a-a836e88af814",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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