03_pytorch_computer_version_video.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/alchemistklk/pytorch_tutorial/blob/master/03_pytorch_computer_version_video.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vqzse2Sjyc-W"
      },
      "source": [
        "# PyTorch Computer Version"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Uap-x_GxBlpw"
      },
      "source": [
        "## 0.Computer version libraries in PyTorch\n",
        "\n",
        "- `torchversion`- base domain library for PyTorch computer version\n",
        "- `torchversion.datasets` - get datasets and data loading function for PyTorch computer version\n",
        "- `torchversion.model` - get pretrained computer version model that you can leverage for you own problems\n",
        "- `torchversion.transform` - function to manupulating your version data(images) to be suitable for use with an ML model\n",
        "- `torch.utils.data.Dataset` - Base dataset for PyTorch\n",
        "- `torch.utils.data.DataLoader` - Creates a Python iterable over a dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1QDDU_UTDs2t"
      },
      "outputs": [],
      "source": [
        "# Import PyTorch\n",
        "import torch\n",
        "from torch import nn\n",
        "\n",
        "# Import torchversion\n",
        "import torchvision\n",
        "from torchvision import datasets\n",
        "from torchvision import transforms\n",
        "from torchvision.transforms import ToTensor"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F0Yd-ABtGNDB"
      },
      "source": [
        "## 1.Getting a dataset\n",
        "The dataset we'll be using is FashionMINIST from `torchversion.datasets`"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "iL7L6eYGIY_5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4085c05b-b3e2-49eb-fa75-a72afab9e4c3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz\n",
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz to data/FashionMNIST/raw/train-images-idx3-ubyte.gz\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 26421880/26421880 [00:01<00:00, 14870181.67it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting data/FashionMNIST/raw/train-images-idx3-ubyte.gz to data/FashionMNIST/raw\n",
            "\n",
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz\n",
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz to data/FashionMNIST/raw/train-labels-idx1-ubyte.gz\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 29515/29515 [00:00<00:00, 246407.99it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting data/FashionMNIST/raw/train-labels-idx1-ubyte.gz to data/FashionMNIST/raw\n",
            "\n",
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz\n",
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz to data/FashionMNIST/raw/t10k-images-idx3-ubyte.gz\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 4422102/4422102 [00:05<00:00, 883579.35it/s] \n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting data/FashionMNIST/raw/t10k-images-idx3-ubyte.gz to data/FashionMNIST/raw\n",
            "\n",
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz\n",
            "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz to data/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 5148/5148 [00:00<00:00, 23019485.07it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting data/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz to data/FashionMNIST/raw\n",
            "\n"
          ]
        }
      ],
      "source": [
        "train_data = datasets.FashionMNIST(\n",
        "    root=\"data\", # where to download the data to?\n",
        "    train=True, # do we want to the traing data?\n",
        "    download=True, # do we want to download yes/no?\n",
        "    transform=torchvision.transforms.ToTensor(), # how do we want to transform the data\n",
        "    target_transform=None # how do we want to transfrom lables/targets?\n",
        "    )\n",
        "\n",
        "test_data = datasets.FashionMNIST(\n",
        "    root=\"data\",\n",
        "    train=False,\n",
        "    download=True,\n",
        "    transform=ToTensor(),\n",
        "    target_transform=None\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CErblXZktOHz"
      },
      "source": [
        "### 1.1 Check input and output shapes of data\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "p7NCLjydwfeh"
      },
      "outputs": [],
      "source": [
        "class_names = train_data.classes"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oetcFnM1uB2i"
      },
      "source": [
        "### 1.2 Visualizing our data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "zZU1YYXluIVE",
        "outputId": "46a333c4-90d3-44f1-a4e7-6dde8bc3718b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Image shape: torch.Size([1, 28, 28])\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, '9')"
            ]
          },
          "metadata": {},
          "execution_count": 4
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "image, label = train_data[0]\n",
        "print(f\"Image shape: {image.shape}\")\n",
        "plt.imshow(image.squeeze())\n",
        "plt.title(label)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 445
        },
        "id": "sSnVql3CvRUU",
        "outputId": "a319755a-6509-43cc-a699-b9f1e914358b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(-0.5, 27.5, 27.5, -0.5)"
            ]
          },
          "metadata": {},
          "execution_count": 5
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.imshow(image.squeeze(), cmap=\"gray\")\n",
        "plt.title(class_names[label])\n",
        "plt.axis(False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 752
        },
        "id": "uK4VBAnzwp1D",
        "outputId": "3d65fe23-a771-4fd9-9a9a-d0b14a55f093"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 900x900 with 16 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Plot mode images\n",
        "torch.manual_seed(42)\n",
        "fig = plt.figure(figsize=(9, 9))\n",
        "rows, cols = 4 ,4\n",
        "for i in range(1, rows * cols + 1):\n",
        "  # convert tensor to index\n",
        "  random_idx = torch.randint(0, len(train_data), size=[1]).item()\n",
        "  image, label = train_data[random_idx]\n",
        "  fig.add_subplot(rows, cols, i)\n",
        "  plt.imshow(image.squeeze(), cmap=\"gray\")\n",
        "  plt.title(class_names[label])\n",
        "  plt.axis(False)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xbt6u5QY1CgM"
      },
      "source": [
        "## 2.Prepare DataLoader\n",
        "Right now, our data is in format of PyTorch Datasets\n",
        "DataLoader turns our dataset into a PyTorch iterable.\n",
        "\n",
        "More specifically, we want to turn our data into batches(or mini-batches)\n",
        "\n",
        "Why would we do this?\n",
        "\n",
        "1.It is more computationally efficient\n",
        "\n",
        "2.It gives our neural network more chances to update its gradients per epochs\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GNt_VhxFT8yk"
      },
      "outputs": [],
      "source": [
        "from torch.utils.data import DataLoader\n",
        "\n",
        "# Setup the batch size hyperparameter\n",
        "BATCH_SIZE = 32\n",
        "# Turn datasets into iterable(batches)\n",
        "train_dataloader = DataLoader(dataset=train_data,\n",
        "                              batch_size=BATCH_SIZE,\n",
        "                              shuffle=True)\n",
        "test_dataloader = DataLoader(dataset=test_data,\n",
        "                             batch_size=BATCH_SIZE,\n",
        "                             shuffle=False)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C7y1c1D1Xzzw",
        "outputId": "ca4e4732-dc54-4b4b-ec86-f23d2524dff7"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(torch.Size([32, 1, 28, 28]), torch.Size([32]))"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "# Check out what's inside the training dataloader\n",
        "train_features_batch, train_labels_batch = next(iter(train_dataloader))\n",
        "train_features_batch.shape, train_labels_batch.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 463
        },
        "id": "Y43BY0FMYwlT",
        "outputId": "238d7dde-ec76-4bdd-9d08-ed2c11e387b0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Image size: torch.Size([1, 28, 28])\n",
            "Label: 6, lable size:torch.Size([])\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "MANUAL_SEED = 42\n",
        "torch.manual_seed(MANUAL_SEED)\n",
        "torch.cuda.manual_seed(MANUAL_SEED)\n",
        "\n",
        "random_idx = torch.randint(0, len(train_features_batch), size=[1]).item()\n",
        "img, label = train_features_batch[random_idx], train_labels_batch[random_idx]\n",
        "plt.imshow(img.squeeze(), cmap=\"gray\")\n",
        "plt.title(class_names[label])\n",
        "plt.axis(False)\n",
        "print(f\"Image size: {img.shape}\")\n",
        "print(f\"Label: {label}, lable size:{label.shape  }\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kRGjj0ipa7D5"
      },
      "source": [
        "## 3. Model 0: Build a baseline model\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9CidSa9iby3A",
        "outputId": "5047bb92-c283-4edf-c8f4-4e22783c3651"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Shape before flattening:torch.Size([1, 28, 28])\n",
            "Shape before flattening:torch.Size([1, 784])\n"
          ]
        }
      ],
      "source": [
        "# Create a flatten layer\n",
        "flatten_model = nn.Flatten()\n",
        "# Get a single sample\n",
        "x = train_features_batch[0]\n",
        "# Flatten the sample\n",
        "output = flatten_model(x)\n",
        "# print out what happened\n",
        "print(f\"Shape before flattening:{x.shape}\")\n",
        "print(f\"Shape before flattening:{output.shape}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "F_keJq_0e3oT"
      },
      "outputs": [],
      "source": [
        "class FashionMNISTModelV0(nn.Module):\n",
        "  def __init__(self, input_features, hidden_units, output_features):\n",
        "    super().__init__()\n",
        "    self.linear_stack = nn.Sequential(\n",
        "        nn.Flatten(), # flatten\n",
        "        nn.Linear(in_features=input_features, out_features=hidden_units), # learnable parameters\n",
        "        nn.Linear(in_features=hidden_units, out_features=output_features) # learnable parameters\n",
        "    )\n",
        "\n",
        "  def forward(self, x):\n",
        "    return self.linear_stack(x)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7NYhdLlHgIHF",
        "outputId": "0fdc9377-01d3-45e1-d6e6-eccb47009833"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "OrderedDict([('linear_stack.1.weight',\n",
              "              tensor([[ 0.0296, -0.0084,  0.0328,  ...,  0.0093,  0.0135, -0.0188],\n",
              "                      [-0.0354,  0.0187,  0.0085,  ..., -0.0001,  0.0115, -0.0008],\n",
              "                      [ 0.0017,  0.0045,  0.0133,  ..., -0.0188,  0.0059,  0.0100],\n",
              "                      ...,\n",
              "                      [ 0.0273, -0.0344,  0.0127,  ...,  0.0283, -0.0011, -0.0230],\n",
              "                      [ 0.0257,  0.0291,  0.0243,  ..., -0.0087,  0.0001,  0.0176],\n",
              "                      [-0.0147,  0.0053, -0.0194,  ..., -0.0221,  0.0205, -0.0093]],\n",
              "                     device='cuda:0')),\n",
              "             ('linear_stack.1.bias',\n",
              "              tensor([ 0.0283, -0.0033,  0.0255,  0.0017,  0.0037, -0.0302, -0.0123,  0.0018,\n",
              "                       0.0163,  0.0069], device='cuda:0')),\n",
              "             ('linear_stack.2.weight',\n",
              "              tensor([[-0.0687,  0.0021,  0.2718,  0.2109,  0.1079, -0.2279, -0.1063,  0.2019,\n",
              "                        0.2847, -0.1495],\n",
              "                      [ 0.1344, -0.0740,  0.2006, -0.0475, -0.2514, -0.3130, -0.0118,  0.0932,\n",
              "                       -0.1864,  0.2488],\n",
              "                      [ 0.1500,  0.1907,  0.1457, -0.3050, -0.0580,  0.1643,  0.1565, -0.2877,\n",
              "                       -0.1792,  0.2305],\n",
              "                      [-0.2618,  0.2397, -0.0610,  0.0232,  0.1542,  0.0851, -0.2027,  0.1030,\n",
              "                       -0.2715, -0.1596],\n",
              "                      [-0.0555, -0.0633,  0.2302, -0.1726,  0.2654,  0.1473,  0.1029,  0.2252,\n",
              "                       -0.2160, -0.2725],\n",
              "                      [ 0.0118,  0.1559,  0.1596,  0.0132,  0.3024,  0.1124,  0.1366, -0.1533,\n",
              "                        0.0965, -0.1184],\n",
              "                      [-0.2555, -0.2057, -0.1909, -0.0477, -0.1324,  0.2905,  0.1307, -0.2629,\n",
              "                        0.0133,  0.2727],\n",
              "                      [-0.0127,  0.0513,  0.0863, -0.1043, -0.2047, -0.1185, -0.0825,  0.2488,\n",
              "                       -0.2571,  0.0425],\n",
              "                      [-0.1209, -0.0336, -0.0281, -0.1227,  0.0730,  0.0747, -0.1816,  0.1943,\n",
              "                        0.2853, -0.1310],\n",
              "                      [ 0.0645, -0.1171,  0.2168, -0.0245, -0.2820,  0.0736,  0.2621,  0.0012,\n",
              "                       -0.0810, -0.0087]], device='cuda:0')),\n",
              "             ('linear_stack.2.bias',\n",
              "              tensor([ 0.1791,  0.2712, -0.0791,  0.1685,  0.1762,  0.2825,  0.2266, -0.2612,\n",
              "                      -0.2613, -0.2624], device='cuda:0'))])"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
        "\n",
        "model_0 = FashionMNISTModelV0(input_features=784, hidden_units=10, output_features=len(class_names)).to(device)\n",
        "model_0.state_dict()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fhjCYjCRgwJ-",
        "outputId": "998e56c4-6353-44d7-9908-4bfcd3b5e682"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(tensor([[ 0.3041,  0.0994, -0.2225,  0.0223,  0.0929,  0.3192,  0.4622, -0.4053,\n",
              "          -0.1659, -0.0464]], device='cuda:0', grad_fn=<AddmmBackward0>),\n",
              " tensor([6], device='cuda:0'))"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "dummy_x = torch.rand([1, 1, 28, 28])\n",
        "logits = model_0(dummy_x.to(device))\n",
        "prob = torch.softmax(logits, dim=1).argmax(dim=1)\n",
        "logits, prob\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4yEfERl_iLBL"
      },
      "source": [
        "## 3.1 Setup loss, optimizer, and evaluation metrics\n",
        "- Loss function - since we're working with multi-classification, our loss function will be `nn.CrossEntropyLoss()`\n",
        "- Optimizer: our optimizer `torch.optim.SGD()`\n",
        "- Evaluation metric - since we're working on a classification problem, let's use accuracy as our evaluation metric\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZpC2o6LujAQ-",
        "outputId": "ad5c05e1-f2bb-4b0c-b841-13193c10398b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/841.5 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[91m━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m30.7/841.5 kB\u001b[0m \u001b[31m1.2 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[91m━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m112.6/841.5 kB\u001b[0m \u001b[31m1.6 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[91m━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m297.0/841.5 kB\u001b[0m \u001b[31m2.9 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━━━━━━\u001b[0m \u001b[32m706.6/841.5 kB\u001b[0m \u001b[31m5.1 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m841.5/841.5 kB\u001b[0m \u001b[31m5.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ],
      "source": [
        "!pip -q install torchmetrics\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "pHmkAWx2kxAG"
      },
      "outputs": [],
      "source": [
        "from torchmetrics import Accuracy\n",
        "loss_fn = nn.CrossEntropyLoss()\n",
        "optimizer = torch.optim.SGD(model_0.parameters(), lr=0.01, momentum=0.9)\n",
        "accuracy_fn = Accuracy(task=\"multiclass\", num_classes=len(class_names)).to(device)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F_p4HNB0mQ9X"
      },
      "source": [
        "### 3.2 Creatign a function to time our experiments"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "aviL6hZGnVqV"
      },
      "outputs": [],
      "source": [
        "from timeit import default_timer as timer\n",
        "\n",
        "def print_train_time(start, end, device: torch.device):\n",
        "  total_time = end - start\n",
        "  print(f\"Train time on {device}, {total_time:.3f} seconds\")\n",
        "  return total_time"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Yn_orXKTn0_5",
        "outputId": "d16afea3-89d2-45b2-b6fa-8d8c78f122c3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train time on cpu, 0.000 seconds\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5.502700003034988e-05"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ],
      "source": [
        "start_time = timer()\n",
        "end_time = timer()\n",
        "print_train_time(start_time, end_time, device=\"cpu\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w9Ma_f4to0J1"
      },
      "source": [
        "### 3.3 Create a training and testing loop on batches of data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 587,
          "referenced_widgets": [
            "b2730d154d7e4bab8077cb3944e4ea27",
            "b63143e2d6c74b2ea11693eb5b72cd90",
            "d1ae877e297d4d54afbaf088f88311be",
            "abefbdeed81e419284da92208f456a97",
            "40f7e0fd0abc420cba5bfc99cf208fb4",
            "597b4c5cdb8c4f63bf7e944aee6b298f",
            "6265465f25d14f21aeaa03873341ac9e",
            "7c47d830109c4b04ad4684290dd92b5e",
            "378f484198ed444ead3e1393e00116ff",
            "9c149c2c1d7f48f5b7970da16d76b5a3",
            "28dedfbcaefd45899938e9dae2317280"
          ]
        },
        "id": "nPHgZddr-zZ9",
        "outputId": "d33b81d2-eb66-4708-cb2c-4a78ff1bcde5"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/3 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b2730d154d7e4bab8077cb3944e4ea27"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 0\n",
            "========\n",
            "Look at 0 / 60000 samples\n",
            "Look at 12800 / 60000 samples\n",
            "Look at 25600 / 60000 samples\n",
            "Look at 38400 / 60000 samples\n",
            "Look at 51200 / 60000 samples\n",
            "\n",
            " Train loss:0.44775 | Test loss:0.47005, Test acc:0.83\n",
            "\n",
            "Epoch: 1\n",
            "========\n",
            "Look at 0 / 60000 samples\n",
            "Look at 12800 / 60000 samples\n",
            "Look at 25600 / 60000 samples\n",
            "Look at 38400 / 60000 samples\n",
            "Look at 51200 / 60000 samples\n",
            "\n",
            " Train loss:0.43565 | Test loss:0.46623, Test acc:0.84\n",
            "\n",
            "Epoch: 2\n",
            "========\n",
            "Look at 0 / 60000 samples\n",
            "Look at 12800 / 60000 samples\n",
            "Look at 25600 / 60000 samples\n",
            "Look at 38400 / 60000 samples\n",
            "Look at 51200 / 60000 samples\n",
            "\n",
            " Train loss:0.42963 | Test loss:0.49457, Test acc:0.82\n",
            "\n",
            "Train time on cuda:0, 31.558 seconds\n"
          ]
        }
      ],
      "source": [
        "from tqdm.auto import tqdm\n",
        "epochs = 3\n",
        "start_time = timer()\n",
        "for epoch in tqdm(range(epochs)):\n",
        "  train_loss = 0\n",
        "  train_acc = 0\n",
        "  print(f\"Epoch: {epoch}\\n========\")\n",
        "  for batch,(X, y) in enumerate(train_dataloader):\n",
        "    ### Train\n",
        "    model_0.train()\n",
        "\n",
        "    X = X.to(device)\n",
        "    y = y.to(device)\n",
        "\n",
        "    # 1.Forward pass\n",
        "    y_logits = model_0(X)\n",
        "    # 2. Calculate the loss\n",
        "    loss = loss_fn(y_logits, y)\n",
        "    train_loss += loss\n",
        "    # 3. zero grad\n",
        "    optimizer.zero_grad()\n",
        "    # 4. loss backward\n",
        "    loss.backward()\n",
        "    # 5. step\n",
        "    optimizer.step()\n",
        "\n",
        "    if batch % 400 == 0:\n",
        "      print(f\"Look at {batch * len(X)} / {len(train_dataloader.dataset)} samples\")\n",
        "  train_loss /= len(train_dataloader)\n",
        "\n",
        "  test_loss = 0\n",
        "  test_acc = 0\n",
        "  model_0.eval()\n",
        "  with torch.inference_mode():\n",
        "    for X, y in test_dataloader:\n",
        "      # 1.forward pass\n",
        "      X = X.to(device)\n",
        "      y = y.to(device)\n",
        "      y_logits = model_0(X)\n",
        "      loss = loss_fn(y_logits, y)\n",
        "      # 2.calculate the loss\n",
        "      test_loss += loss\n",
        "      test_acc += accuracy_fn(y_logits.argmax(dim=1), y)\n",
        "\n",
        "    test_loss /= len(test_dataloader)\n",
        "    test_acc /= len(test_dataloader)\n",
        "  print(f\"\\n Train loss:{train_loss:.5f} | Test loss:{test_loss:.5f}, Test acc:{test_acc:.2f}\\n\")\n",
        "\n",
        "end_time = timer()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "total_train_model_time_0 = print_train_time(start_time, end_time, str(next(model_0.parameters()).device))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a0BzDfGsXWOs",
        "outputId": "10e72867-62d6-4814-d8d3-b13f75c56659"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train time on cuda:0, 48.293 seconds\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YVSkZoNssx1V"
      },
      "source": [
        "## 4.Make predictions and get Model 0 result\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "S5K_ETKyxUDQ"
      },
      "outputs": [],
      "source": [
        "torch.manual_seed(MANUAL_SEED)\n",
        "\n",
        "def eval_mode(model: torch.nn.Module,\n",
        "              data_loader: torch.utils.data.DataLoader,\n",
        "              loss_fn: torch.nn.Module,\n",
        "              accuracy_fn,\n",
        "              device: torch.device=device):\n",
        "  \"\"\"Returns a dictionary containing results of model predicting on data_loader\n",
        "\n",
        "  Args:\n",
        "    model(torch.nn.Module): A PyTorch model capable of predicting on data_loader\n",
        "    data_loader(torch.utils.data.DataLoader): The target dataset to predict on\n",
        "    loss_fn(torch.nn.Module): The loss function of model\n",
        "    accuracy_fn: An accuracy function to compare model's prediction to true labels\n",
        "\n",
        "  Returns:\n",
        "    (dict): Results of model making predictions on data_loader\n",
        "  \"\"\"\n",
        "  model.eval()\n",
        "  loss = 0\n",
        "  acc = 0\n",
        "  with torch.inference_mode():\n",
        "    for X, y in data_loader:\n",
        "      X = X.to(device)\n",
        "      y = y.to(device)\n",
        "      test_logits = model(X)\n",
        "      loss += loss_fn(test_logits, y)\n",
        "      acc += accuracy_fn(test_logits.argmax(dim=1), y)\n",
        "\n",
        "    loss /= len(data_loader)\n",
        "    acc /= len(data_loader)\n",
        "\n",
        "  return {\"model_name\": model.__class__.__name__,\n",
        "          \"model_loss\": loss.item(),\n",
        "          \"model_acc\": acc}"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_0_results = eval_mode(model_0, test_dataloader, loss_fn, accuracy_fn, device)"
      ],
      "metadata": {
        "id": "Y7IJApI10AzF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EfEkf_yH1XQv"
      },
      "source": [
        "## 5.Setup device agnostic-code(for using GPU if there is one)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0avhbAig3EW5"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "from torch import nn\n",
        "from tqdm.auto import tqdm\n",
        "from timeit import default_timer as timer\n",
        "device = \"cuda\" if torch.cuda.is_available() else \"cpu\""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8MIu2YAX3K7c"
      },
      "source": [
        "## 6.Building a better model with non-linearity"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ame9LSyk1iAg",
        "outputId": "e6f80868-7015-4717-9c68-7230f1a43f0e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "OrderedDict([('layer_stack.1.weight',\n",
              "              tensor([[-0.0084,  0.0328, -0.0078,  ...,  0.0135, -0.0188, -0.0354],\n",
              "                      [ 0.0187,  0.0085, -0.0182,  ...,  0.0115, -0.0008,  0.0017],\n",
              "                      [ 0.0045,  0.0133, -0.0108,  ...,  0.0059,  0.0100,  0.0273],\n",
              "                      ...,\n",
              "                      [ 0.0170,  0.0106,  0.0352,  ..., -0.0152, -0.0014,  0.0008],\n",
              "                      [-0.0198, -0.0133, -0.0142,  ..., -0.0313,  0.0080, -0.0075],\n",
              "                      [ 0.0190,  0.0320, -0.0128,  ..., -0.0176, -0.0007,  0.0044]],\n",
              "                     device='cuda:0')),\n",
              "             ('layer_stack.1.bias',\n",
              "              tensor([ 0.0010,  0.0270, -0.0205,  0.0162,  0.0329, -0.0257, -0.0280,  0.0121,\n",
              "                      -0.0127, -0.0008, -0.0333,  0.0218, -0.0024,  0.0022, -0.0124,  0.0124,\n",
              "                      -0.0006, -0.0142,  0.0089, -0.0133], device='cuda:0')),\n",
              "             ('layer_stack.3.weight',\n",
              "              tensor([[ 0.0830, -0.2142, -0.0865, -0.1532,  0.0951, -0.1570,  0.1733,  0.0544,\n",
              "                        0.1370, -0.1459, -0.1816, -0.0367, -0.1229, -0.0757,  0.1838,  0.1195,\n",
              "                       -0.0661,  0.1358, -0.1498, -0.1299],\n",
              "                      [ 0.0990,  0.0965, -0.0647, -0.1526,  0.0349,  0.2157,  0.2201,  0.1834,\n",
              "                       -0.1677,  0.1482,  0.0409, -0.1815,  0.0144, -0.2159,  0.0480, -0.1230,\n",
              "                       -0.1505,  0.0109,  0.1508,  0.1255],\n",
              "                      [-0.1384,  0.1272,  0.0832, -0.1493, -0.0577, -0.0536, -0.1817, -0.2040,\n",
              "                       -0.1518,  0.2033, -0.0594,  0.0215, -0.1857, -0.0385,  0.0845, -0.0045,\n",
              "                       -0.0176, -0.0030,  0.1503, -0.1575],\n",
              "                      [-0.2008, -0.2154,  0.0909, -0.1246, -0.0978, -0.0296,  0.1311,  0.0019,\n",
              "                        0.0438, -0.0628,  0.1217, -0.1116,  0.1724,  0.1841, -0.2011, -0.0708,\n",
              "                       -0.0269, -0.1204,  0.0120, -0.0734],\n",
              "                      [ 0.1851, -0.0177, -0.1250, -0.1110, -0.2090, -0.1374,  0.0434,  0.0675,\n",
              "                       -0.0964,  0.0023,  0.1122,  0.0296,  0.0252, -0.1467,  0.2102, -0.0660,\n",
              "                        0.0184, -0.0275,  0.0971, -0.1852],\n",
              "                      [ 0.0509, -0.1736,  0.1765, -0.0881,  0.0456,  0.0148, -0.0889, -0.0298,\n",
              "                        0.2127, -0.1131, -0.0581,  0.1474,  0.1313,  0.0619, -0.1957, -0.0361,\n",
              "                       -0.2203, -0.1105, -0.0239, -0.0845],\n",
              "                      [ 0.0918,  0.0022,  0.0440,  0.0656,  0.0540, -0.2192,  0.0451,  0.1840,\n",
              "                        0.0755,  0.1605, -0.1211, -0.0568, -0.2040, -0.2042, -0.1686,  0.1143,\n",
              "                       -0.1315,  0.0751,  0.1549,  0.0799],\n",
              "                      [-0.0937,  0.1428,  0.0820,  0.0507, -0.0561,  0.1776,  0.1081, -0.0393,\n",
              "                        0.1031, -0.1490, -0.0194, -0.1856, -0.1995,  0.1821,  0.1189, -0.0759,\n",
              "                       -0.1246, -0.0858, -0.0531, -0.0380],\n",
              "                      [ 0.1915,  0.2140,  0.1168, -0.1251, -0.0788,  0.0279, -0.1332,  0.2000,\n",
              "                       -0.1325,  0.2070, -0.0010,  0.2026,  0.1461,  0.0201,  0.2195,  0.0195,\n",
              "                        0.0858, -0.0577, -0.1969, -0.1371],\n",
              "                      [-0.1800,  0.1532,  0.0994,  0.0630,  0.2073, -0.1579,  0.0629,  0.0096,\n",
              "                       -0.1957, -0.2160,  0.2172,  0.1899,  0.0406, -0.1435,  0.0312,  0.1594,\n",
              "                       -0.0487,  0.0031, -0.1100,  0.1690]], device='cuda:0')),\n",
              "             ('layer_stack.3.bias',\n",
              "              tensor([-0.1363, -0.1257, -0.0574, -0.0071,  0.0563,  0.1741,  0.1369,  0.0323,\n",
              "                       0.0586,  0.1895], device='cuda:0'))])"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ],
      "source": [
        "class FashionMNISTModelV1(nn.Module):\n",
        "  def __init__(self, input_shape, hidden_units, output_shape):\n",
        "    super().__init__()\n",
        "    self.layer_stack = nn.Sequential(\n",
        "        nn.Flatten(),\n",
        "        nn.Linear(in_features=input_shape, out_features=hidden_units),\n",
        "        nn.ReLU(),\n",
        "        nn.Linear(in_features=hidden_units, out_features=output_shape),\n",
        "        nn.ReLU()\n",
        "    )\n",
        "\n",
        "  def forward(self, x):\n",
        "    return self.layer_stack(x)\n",
        "\n",
        "model_1 = FashionMNISTModelV1(input_shape=784, hidden_units=20, output_shape=10).to(device)\n",
        "model_1.state_dict()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4eEGusTDAODa"
      },
      "source": [
        "### 6.1 Setup loss and accuracy function"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "PgqfCbqcATgW"
      },
      "outputs": [],
      "source": [
        "from torchmetrics import Accuracy\n",
        "loss_fn = nn.CrossEntropyLoss()\n",
        "optimizer = torch.optim.SGD(model_1.parameters(), lr=0.01, momentum=0.9)\n",
        "accuracy_fn = Accuracy(task=\"multiclass\", num_classes=len(class_names)).to(device)\n",
        "\n",
        "def print_train_time(start, end, device: torch.device):\n",
        "  total_time = end - start\n",
        "  print(f\"Train time on {device}, {total_time:.3f} seconds\")\n",
        "  return total_time"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qIYjD9F11h8H"
      },
      "source": [
        "### 6.2 Functionizing training and test loops"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "oEZsksHt1h5-"
      },
      "outputs": [],
      "source": [
        "def train_step(model: torch.nn.Module,\n",
        "               data_loader: torch.utils.data.DataLoader,\n",
        "               loss_fn: torch.nn.Module,\n",
        "               optimizer: torch.optim.Optimizer,\n",
        "               accuracy_fn,\n",
        "               device: torch.device = device):\n",
        "  train_loss = 0\n",
        "  train_acc = 0\n",
        "\n",
        "  model.to(device)\n",
        "  for X, y in data_loader:\n",
        "    X = X.to(device)\n",
        "    y = y.to(device)\n",
        "    y_logits = model(X)\n",
        "\n",
        "    loss = loss_fn(y_logits, y)\n",
        "    train_loss += loss\n",
        "    train_acc += accuracy_fn(y_logits.argmax(dim=1), y)\n",
        "\n",
        "    optimizer.zero_grad()\n",
        "\n",
        "    loss.backward()\n",
        "\n",
        "    optimizer.step()\n",
        "\n",
        "  train_loss /= len(data_loader)\n",
        "  train_acc /= len(data_loader)\n",
        "\n",
        "  print(f\"Train loss: {train_loss:.5f} | Train Acc:{train_acc:.2f}\")\n",
        "\n",
        "\n",
        "def test_step(model: torch.nn.Module,\n",
        "              data_loader: torch.utils.data.DataLoader,\n",
        "              loss_fn: torch.nn.Module,\n",
        "              accuracy_fn,\n",
        "              device: torch.device = device\n",
        "              ):\n",
        "  model.to(device)\n",
        "  model.eval()\n",
        "  test_loss, test_acc = 0, 0\n",
        "  with torch.inference_mode():\n",
        "    for X, y in data_loader:\n",
        "      X = X.to(device)\n",
        "      y = y.to(device)\n",
        "\n",
        "      test_logits = model(X)\n",
        "\n",
        "      test_loss += loss_fn(test_logits, y)\n",
        "      test_acc += accuracy_fn(test_logits.argmax(dim=1), y)\n",
        "\n",
        "    test_loss /= len(data_loader)\n",
        "    test_acc /= len(data_loader)\n",
        "\n",
        "  print(f\"Test loss:{test_loss:.5f} | Test acc:{test_acc:.2f}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 275,
          "referenced_widgets": [
            "2a86b90bf095434f909595f1bd938463",
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          ]
        },
        "id": "k2ohHqBn1h3-",
        "outputId": "38a07794-5b75-4ef8-dc15-aedcc98e0694"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/3 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2a86b90bf095434f909595f1bd938463"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epochs:0\n",
            "============\n",
            "Train loss: 0.59057 | Train Acc:0.79\n",
            "Test loss:0.46598 | Test acc:0.83\n",
            "Epochs:1\n",
            "============\n",
            "Train loss: 0.43812 | Train Acc:0.84\n",
            "Test loss:0.42979 | Test acc:0.85\n",
            "Epochs:2\n",
            "============\n",
            "Train loss: 0.40676 | Train Acc:0.86\n",
            "Test loss:0.45734 | Test acc:0.83\n",
            "Train time on cuda, 37.512 seconds\n"
          ]
        }
      ],
      "source": [
        "torch.manual_seed(42)\n",
        "\n",
        "epochs = 3\n",
        "start_time = timer()\n",
        "for epoch in tqdm(range(epochs)):\n",
        "  print(f\"Epochs:{epoch}\\n============\")\n",
        "\n",
        "  train_step(model_1, train_dataloader, loss_fn, optimizer, accuracy_fn)\n",
        "\n",
        "  test_step(model_1, test_dataloader, loss_fn, accuracy_fn)\n",
        "\n",
        "end_time = timer()\n",
        "\n",
        "total_train_model_time_1 = print_train_time(start_time, end_time, device)\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_1_results = eval_mode(model_1, test_dataloader, loss_fn, accuracy_fn)"
      ],
      "metadata": {
        "id": "yFMooNpgWwv0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rB990Zb98hv-"
      },
      "source": [
        "## 7.Model2: Building a Convolutional Neural Network"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "20hgpdUy1huv"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "from torch import nn\n",
        "from tqdm.auto import tqdm\n",
        "from timeit import default_timer as timer\n",
        "class FashionMINISTModelV2(nn.Module):\n",
        "  def __init__(self,input_shape: int, hidden_units: int, output_shape: int):\n",
        "    super().__init__()\n",
        "    self.block1 = nn.Sequential(\n",
        "        nn.Conv2d(in_channels=input_shape,\n",
        "                  out_channels=hidden_units,\n",
        "                  kernel_size=3, # how big is the square that's going to over the image?\n",
        "                  stride=1, # default\n",
        "                  padding=1),\n",
        "        nn.ReLU(),\n",
        "        nn.Conv2d(\n",
        "            in_channels=hidden_units,\n",
        "            out_channels=hidden_units,\n",
        "            kernel_size=3,\n",
        "            stride=1,\n",
        "            padding=1\n",
        "        ),\n",
        "        nn.ReLU(),\n",
        "        nn.MaxPool2d(kernel_size=2,stride=2)\n",
        "    )\n",
        "\n",
        "    self.block2 = nn.Sequential(\n",
        "        nn.Conv2d(hidden_units, hidden_units,3,padding=1),\n",
        "        nn.ReLU(),\n",
        "        nn.Conv2d(hidden_units,hidden_units,3,padding=1),\n",
        "        nn.ReLU(),\n",
        "        nn.MaxPool2d(2)\n",
        "    )\n",
        "\n",
        "    self.classifier = nn.Sequential(\n",
        "        nn.Flatten(),\n",
        "        nn.Linear(in_features=hidden_units*7*7, out_features=output_shape)\n",
        "        )\n",
        "  def forward(self, x):\n",
        "    x = self.block1(x)\n",
        "    x = self.block2(x)\n",
        "    return self.classifier(x)\n",
        "\n",
        "MANUAL_SEED = 42\n",
        "torch.manual_seed(MANUAL_SEED)\n",
        "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
        "model2 = FashionMINISTModelV2(input_shape=1, hidden_units=10, output_shape=10).to(device)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 7.2 Setup loss and optimizer function\n"
      ],
      "metadata": {
        "id": "ys1iloyNwDVK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "loss_fn = nn.CrossEntropyLoss()\n",
        "accuracy_fn = Accuracy(task=\"multiclass\", num_classes=len(class_names)).to(device)\n",
        "optimizer = torch.optim.SGD(model2.parameters(), lr=0.01, momentum=0.9)"
      ],
      "metadata": {
        "id": "YvTx60bOwJVR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "bsGdVFOGp_69",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 171,
          "referenced_widgets": [
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        },
        "outputId": "da2b87da-03b0-4712-e5bc-407c68b2ebfe"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/3 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8219c888d14c4e4586998df7ce6c36bf"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train loss: 0.61353 | Train Acc:0.78\n",
            "Test loss:0.41722 | Test acc:0.85\n",
            "Train loss: 0.36861 | Train Acc:0.87\n",
            "Test loss:0.36356 | Test acc:0.87\n",
            "Train loss: 0.32153 | Train Acc:0.88\n",
            "Test loss:0.34064 | Test acc:0.88\n",
            "Train time on cuda, 48.293 seconds\n"
          ]
        }
      ],
      "source": [
        "torch.manual_seed(42)\n",
        "start_time = timer()\n",
        "epochs = 3\n",
        "for epoch in tqdm(range(epochs)):\n",
        "  train_step(model2, train_dataloader, loss_fn=loss_fn, optimizer=optimizer, accuracy_fn=accuracy_fn)\n",
        "  test_step(model2, test_dataloader, loss_fn=loss_fn, accuracy_fn=accuracy_fn)\n",
        "\n",
        "end_time = timer()\n",
        "total_train_model_time_2 = print_train_time(start_time, end_time, device)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# get model_2 result\n",
        "model_2_results = eval_mode(model2, test_dataloader, loss_fn, accuracy_fn)"
      ],
      "metadata": {
        "id": "6c9-meRGzdDF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 8.Compare model results and training time"
      ],
      "metadata": {
        "id": "Y5n2uR8cUOBe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "compare_results = pd.DataFrame([model_0_results, model_1_results, model_2_results])\n",
        "compare_results"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "wPlU-jB4Ue7_",
        "outputId": "aeee2e53-efd7-4711-ac7c-6d7f46122ae3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             model_name  model_loss                        model_acc\n",
              "0   FashionMNISTModelV0    0.494567  tensor(0.8228, device='cuda:0')\n",
              "1   FashionMNISTModelV1    0.457337  tensor(0.8337, device='cuda:0')\n",
              "2  FashionMINISTModelV2    0.340644  tensor(0.8789, device='cuda:0')"
            ],
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              "      <th>model_name</th>\n",
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              "  <tbody>\n",
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              "      <th>0</th>\n",
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              "      <td>0.494567</td>\n",
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              "        const element = document.querySelector('#df-1f801592-67f0-4299-ae6d-f5effe751752');\n",
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              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
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              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
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              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "\n",
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              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
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              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('compare_results')\"\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('compare_results');\n",
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              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "compare_results",
              "summary": "{\n  \"name\": \"compare_results\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"model_name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"FashionMNISTModelV0\",\n          \"FashionMNISTModelV1\",\n          \"FashionMINISTModelV2\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"model_loss\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.08030736504064523,\n        \"min\": 0.34064412117004395,\n        \"max\": 0.49456703662872314,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.49456703662872314,\n          0.4573374390602112,\n          0.34064412117004395\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"model_acc\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"tensor(0.8228, device='cuda:0')\",\n          \"tensor(0.8337, device='cuda:0')\",\n          \"tensor(0.8789, device='cuda:0')\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Add traning time to result comparison\n",
        "compare_results[\"training_time\"] = [total_train_model_time_0, total_train_model_time_1, total_train_model_time_2]"
      ],
      "metadata": {
        "id": "duwZYWToVP-2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "compare_results"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "4YSTqXmlXwPH",
        "outputId": "b3bc2707-764d-4fef-dc68-e7dfdea9ba3d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             model_name  model_loss                        model_acc  \\\n",
              "0   FashionMNISTModelV0    0.494567  tensor(0.8228, device='cuda:0')   \n",
              "1   FashionMNISTModelV1    0.457337  tensor(0.8337, device='cuda:0')   \n",
              "2  FashionMINISTModelV2    0.340644  tensor(0.8789, device='cuda:0')   \n",
              "\n",
              "   training_time  \n",
              "0      48.293302  \n",
              "1      37.511719  \n",
              "2      48.293302  "
            ],
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              "type": "dataframe",
              "variable_name": "compare_results",
              "summary": "{\n  \"name\": \"compare_results\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"model_name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"FashionMNISTModelV0\",\n          \"FashionMNISTModelV1\",\n          \"FashionMINISTModelV2\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"model_loss\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.08030736504064523,\n        \"min\": 0.34064412117004395,\n        \"max\": 0.49456703662872314,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.49456703662872314,\n          0.4573374390602112,\n          0.34064412117004395\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"model_acc\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"tensor(0.8228, device='cuda:0')\",\n          \"tensor(0.8337, device='cuda:0')\",\n          \"tensor(0.8789, device='cuda:0')\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"training_time\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6.224749711085666,\n        \"min\": 37.51171882899996,\n        \"max\": 48.293301593000024,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          37.51171882899996,\n          48.293301593000024\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 8.1 Performace-tradeoff"
      ],
      "metadata": {
        "id": "tu1N56fUX4n6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "model_acc = np.array(compare_results['model_acc'])\n",
        "compare_results['model_acc'] = torch.tensor([x.item() for x in model_acc], dtype=torch.float32)\n"
      ],
      "metadata": {
        "id": "SBdDEva5ZCiN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualize our model results\n",
        "compare_results.set_index(\"model_name\")[\"model_acc\"].plot(kind=\"barh\")\n",
        "plt.xlabel(\"accuracy (%)\")\n",
        "plt.ylabel(\"model\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "sG64BsBzYIxj",
        "outputId": "49ff80a2-bdcb-4f4a-e96b-3e7c25b4541b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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zc6vqMgAAwG3GnWAAAACYDiEYAAAApkMIBgAAgOkQggEAAGA6hGAAAACYDiEYAAAApkMIBgAAgOkQggEAAGA6hGAAAACYDiEYAAAApkMIBgAAgOkQggEAAGA6hGAAAACYDiEYAAAApkMIBgAAgOkQggEAAGA6hGAAAACYDiEYAAAApkMIBgAAgOkQggEAAGA6hGAAAACYDiEYAAAApkMIBgAAgOkQggEAAGA6hGAAAACYDiEYAAAApkMIBgAAgOkQggEAAGA6hGAAAACYDiEYAAAApkMIBgAAgOkQggEAAGA6hGAAAACYDiEYAAAApkMIBgAAgOkQggEAAGA6hGAAAACYDiEYAAAApkMIBgAAgOkQggEAAGA6hGAAAACYDiEYAAAApkMIBgAAgOkQggEAAGA6hGAAAACYDiEYAAAApkMIBgAAgOkQggEAAGA6hGAAAACYDiEYAAAApkMIBgAAgOkQggEAAGA6hGAAAACYDiEYAAAApkMIBgAAgOkQggEAAGA6hGAAAACYDiEYAAAApkMIBgAAgOkQggEAAGA69lVdAHAveyh+rao5OVd1GQAA3DUHEsOruoS7gjvBAAAAMB1CMAAAAEyHEAwAAADTIQQDAADAdAjBAAAAMB1CMAAAAEyHEAwAAADTIQQDAADAdAjBAAAAMB1CMAAAAEyHEAwAAADTIQQDAADAdAjBAAAAMB37ijasW7eu7OzsKtT29OnTNhcEAAAA3GkVDsGzZs26g2UAAAAAd0+FQ/CQIUPuZB0AAADAXWPznOB9+/Zp0qRJGjBggE6cOCFJ+uc//6nvv//+thUHAAAA3Ak2heAvv/xS/v7++ve//6309HSdP39ekrR9+3bFx8ff1gIBAACA282mEDxhwgRNmzZN69atk6Ojo2V9aGiovvnmm9tWHAAAAHAn2BSCc3Nz9eSTT5ZZ36BBA/33v/+95aIAAACAO8mmEFynTh39+OOPZdZv27ZNTZo0ueWiAAAAgDvJphDcv39/jR8/XseOHZOdnZ1KSkqUlZWlMWPGKCoq6nbXCAAAANxWNoXgGTNmqFWrVvLy8tL58+fVpk0bdevWTUFBQZo0adLtrhEAAAC4rSr8nuBfcnR01Pz58/WXv/xF3333nc6fP6/27durZcuWt7s+AAAA4LazKQSX8vb2lre39+2qBQAAALgrKhyCR40aVeFO33jjDZuKuRMyMjIUEhKiM2fOqE6dOuW2mTx5sj7++GPl5OTc1drMpiLX4lo+Pj6Ki4tTXFzcHa0NAACYS4XnBG/bts1qWbhwof72t78pIyNDGRkZeu+997Rw4cJbCpJDhw6VnZ1dmWXv3r0291kRY8aM0fr1629rnxkZGbKzs1PdunV1+fJlq22bN2+2nNu17du2bavi4mKr9nXq1FFqaqrls4+Pj2bNmmX5vH37dvXp00cNGjRQjRo15OPjo379+unEiROaPHlyuWP6y0X6v7EfNmxYmXMZPny47OzsNHTo0FsfGBtduXJFHh4eSkxMLHf71KlT1bBhQxUVFenHH3/UwIED5efnp2rVqhGgAQBAGRUOwV988YVliYiIUPfu3XXkyBFt3bpVW7du1eHDhxUSEqLw8PBbKqhXr1768ccfrZZmzZrdUp83U6tWLdWrV++O9O3q6qqVK1darVu4cOF1p5Hk5+fr/fffr3D/J0+eVI8ePeTu7q61a9cqLy9PKSkp8vT01IULFzRmzBirsXzggQf06quvWq0r5eXlpWXLlunSpUuWdZcvX9bSpUurfNqLo6OjBg8erJSUlDLbDMNQamqqoqKi5ODgoMLCQtWvX1+TJk1Su3btqqBaAABwr7Pp7RDJyclKSEhQ3bp1Levq1q2radOmKTk5+ZYKcnJyUqNGjayW2bNny9/fXy4uLvLy8tKLL75o+VXNknTw4EFFRESobt26cnFxUdu2bbV69WqrfrOzs9WxY0c5OzsrKChIu3btsmybPHmyAgMDLZ9LSkr06quv6oEHHpCTk5MCAwO1Zs0ay/YDBw7Izs5O6enpCgkJkbOzs9q1a6dNmzaVOZ8hQ4Zo0aJFls+XLl3SsmXLNGTIkHLP/6WXXlJ8fLwKCwsrNF5ZWVk6e/asFixYoPbt26tZs2YKCQnRm2++qWbNmqlWrVpWY1m9enW5urparSvVoUMHeXl5KT093bIuPT1d3t7eat++vdVxCwsLNXLkSMvd50cffVSbN2+2arN69Wr5+fmpZs2aCgkJ0YEDB8rUn5mZqa5du6pmzZry8vLSyJEjdeHChXLPNSYmRrt371ZmZqbV+i+//FL5+fmKiYmR9POd8tmzZysqKkq1a9eu0DgCAABzsSkEFxQU6OTJk2XWnzx5UufOnbvloq5VrVo1zZkzR99//70WL16sDRs2aNy4cZbtw4cPV2Fhob766ivl5uZq5syZqlWrllUfEydOVHJysrZs2SJ7e3tFR0df93izZ89WcnKykpKStGPHDoWFhalPnz7as2dPmT7HjBmjnJwc+fn5acCAAbp69apVm8jISH399dc6dOiQJCktLU0+Pj7q0KFDuceOi4vT1atXNXfu3AqNTaNGjXT16lWtXLlShmFUaJ8biY6OtrrbumjRIj333HNl2o0bN05paWlavHixtm7dqhYtWigsLEynT5+WJB0+fFhPPfWUIiIilJOTo9jYWE2YMMGqj3379qlXr156+umntWPHDi1fvlyZmZkaMWJEubX5+/vr4YcftvpSIUkpKSkKCgpSq1atbD7vwsJCFRQUWC0AAOD+ZVMIfvLJJ/Xcc88pPT1dR44c0ZEjR5SWlqaYmBg99dRTt1TQqlWrVKtWLcvSt29fxcXFKSQkRD4+PgoNDdW0adO0YsUKyz6HDh1ScHCw/P391bx5cz3++OPq1q2bVb/Tp09X9+7d1aZNG02YMEEbN24sM1e3VFJSksaPH6/+/fvrwQcf1MyZMxUYGGg1D1f6eS5xeHi4/Pz8NGXKFB08eLDM/OUGDRqod+/eljm9ixYtumEAd3Z2Vnx8vBISEnT27Nmbjlfnzp31yiuvaODAgfLw8FDv3r31+uuv6/jx4zfdtzyDBw9WZmamDh48qIMHDyorK0uDBw+2anPhwgW9++67ev3119W7d2+1adNG8+fPV82aNbVw4UJJ0rvvvitfX18lJyfrwQcf1KBBg8rMKU5ISNCgQYMUFxenli1bKigoSHPmzNH7779/3WsTExOjDz/80PIvAefOndNHH310wzGtiISEBNWuXduyeHl53VJ/AADg3mZTCJ43b5569+6tgQMHqmnTpmratKkGDhyoXr166Z133rmlgkJCQpSTk2NZ5syZo88//1w9evRQkyZN5OrqqsjISJ06dUoXL16UJI0cOVLTpk1TcHCw4uPjtWPHjjL9BgQEWP7cuHFjSdKJEyfKtCsoKNDRo0cVHBxstT44OFh5eXk29RkdHa3U1FTl5+dr06ZNGjRo0A3HICYmRvXq1dPMmTNv2K7U9OnTdezYMc2bN09t27bVvHnz1KpVK+Xm5lZo/1+qX7++wsPDlZqaqpSUFIWHh8vDw8Oqzb59+1RUVGQ1Rg4ODurUqZNljPLy8vTII49Y7delSxerz9u3b1dqaqrVl56wsDCVlJRo//795dY3YMAAFRcXW74ELV++XNWqVVO/fv0qfa6/9PLLL+vs2bOW5fDhw7fUHwAAuLfZFIKdnZ31zjvv6NSpU5a3RZw+fVrvvPOOXFxcbqkgFxcXtWjRwrIUFhbq8ccfV0BAgNLS0pSdna23335b0s9vDJCk2NhY5efnKzIyUrm5uerYsWOZ6QQODg6WP5e+EaGkpOSWaq1on71799alS5cUExOjiIiImz6EZ29vr+nTp2v27Nk6evRohWqpV6+e+vbtq6SkJOXl5cnT01NJSUmVOJv/UxraFy9efMt3WG/k/Pnzev75562+9Gzfvl179uyRr69vufu4ubnpmWeesUzZSElJ0bPPPltm+ktlOTk5yc3NzWoBAAD3L5tCcCkXFxe5u7vL3d39lsPv9WRnZ6ukpETJycnq3Lmz/Pz8yg2GXl5eGjZsmNLT0zV69GjNnz/fpuO5ubnJ09NTWVlZVuuzsrLUpk0bm/q0t7dXVFSUMjIyKhwq+/btq7Zt22rKlCmVPp6jo6N8fX2v+4DZzfTq1UtXrlxRUVGRwsLCymz39fWVo6Oj1RgVFRVp8+bNljFq3bq1vv32W6v9vvnmG6vPHTp00M6dO62+9JQujo6O160vJiZGmZmZWrVqlTZu3Gh5IA4AAKCibArBpW9PqF27tmU6RJ06dTR16tRbvrt6rRYtWqioqEhz585Vfn6+/t//+3+aN2+eVZu4uDitXbtW+/fv19atW/XFF1+odevWNh9z7NixmjlzppYvX65du3ZpwoQJysnJ0Z/+9Ceb+5w6dapOnjxZbqi8nsTERC1atOiGYXbVqlUaPHiwVq1apd27d2vXrl1KSkrS6tWr9cQTT9hUa/Xq1ZWXl6edO3eqevXqZba7uLjohRde0NixY7VmzRrt3LlTf/jDH3Tx4kVLIB02bJj27NmjsWPHateuXVq6dKnVu44lafz48dq4caNGjBihnJwc7dmzR//4xz+u+2BcqW7duqlFixaKiopSq1atFBQUVKZN6Z3l8+fP6+TJk8rJydHOnTttGg8AAHD/senXJk+cOFELFy5UYmKiZV5oZmamJk+erMuXL2v69Om3rcB27drpjTfe0MyZM/Xyyy+rW7duSkhIUFRUlKVNcXGxhg8friNHjsjNzU29evXSm2++afMxR44cqbNnz2r06NE6ceKE2rRpo08++UQtW7a0uU9HR8cyc2tvJjQ0VKGhofrXv/513TZt2rSRs7OzRo8ercOHD8vJyUktW7bUggULFBkZaXO9N5sOkJiYqJKSEkVGRurcuXPq2LGj1q5da3ltnre3t9LS0vTnP/9Zc+fOVadOnTRjxgyrO+EBAQH68ssvNXHiRHXt2lWGYcjX1/em83vt7OwUHR2tV155RS+//HK5bX75Srfs7GwtXbpUTZs2Lfc1bQAAwHzsDBveq+Xp6al58+apT58+Vuv/8Y9/6MUXX9QPP/xw2woEqkJBQcHPb4mIW6FqTs5VXQ4AAHfNgcRb+8VnVan07++zZ8/e9IaeTdMhTp8+Xe47WVu1amV5TywAAABwr7IpBLdr105vvfVWmfVvvfUWv6YWAAAA9zyb5gS/9tprCg8P1+eff2559+umTZt06NAh/fOf/7ytBQIAAAC3m013grt3765du3bpqaee0k8//aSffvpJTz31lHbv3q2uXbve7hoBAACA28qmO8HSz7+coU+fPurcubPltWhbtmyRpDIPzAEAAAD3EptC8Jo1axQVFaVTp07p2pdL2NnZqbi4+LYUBwAAANwJNk2HeOmll9S3b18dPXpUJSUlVgsBGAAAAPc6m0Lw8ePHNWrUKDVs2PB21wMAAADccTaF4GeeeUYZGRm3uRQAAADg7rBpTvBbb72lvn376uuvv5a/v78cHBysto8cOfK2FAcAAADcCTaF4A8++ED/+te/VKNGDWVkZMjOzs6yzc7OjhAMAACAe5pNIXjixImaMmWKJkyYoGrVbJpRAQAAAFQZmxLslStX1K9fPwIwAAAAfpVsSrFDhgzR8uXLb3ctAAAAwF1h03SI4uJivfbaa1q7dq0CAgLKPBj3xhtv3JbiAAAAgDvBphCcm5ur9u3bS5K+++47q22/fEgOAAAAuBfZFIK/+OKL210HAAAAcNfwZBsAAABMhxAMAAAA0yEEAwAAwHQIwQAAADAdQjAAAABMhxAMAAAA0yEEAwAAwHRsek8wYBbfTQmTm5tbVZcBAABuM+4EAwAAwHQIwQAAADAdQjAAAABMhxAMAAAA0yEEAwAAwHQIwQAAADAdQjAAAABMhxAMAAAA0yEEAwAAwHQIwQAAADAdQjAAAABMhxAMAAAA0yEEAwAAwHQIwQAAADAdQjAAAABMhxAMAAAA0yEEAwAAwHQIwQAAADAdQjAAAABMhxAMAAAA0yEEAwAAwHQIwQAAADAdQjAAAABMhxAMAAAA0yEEAwAAwHQIwQAAADAdQjAAAABMhxAMAAAA0yEEAwAAwHQIwQAAADAdQjAAAABMhxAMAAAA0yEEAwAAwHQIwQAAADAdQjAAAABMhxAMAAAA0yEEAwAAwHQIwQAAADAdQjAAAABMhxAMAAAA0yEEAwAAwHQIwQAAADAdQjAAAABMhxAMAAAA0yEEAwAAwHQIwQAAADAdQjAAAABMhxAMAAAA0yEEAwAAwHQIwQAAADAdQjAAAABMhxAMAAAA0yEEAwAAwHQIwQAAADAdQjAAAABMhxAMAAAA0yEEAwAAwHQIwQAAADAdQjAAAABMhxAMAAAA07Gv6gKAe9lD8WtVzcm5qssAAOCOOJAYXtUlVBnuBAMAAMB0CMEAAAAwHUIwAAAATIcQDAAAANMhBAMAAMB0CMEAAAAwHUIwAAAATIcQDAAAANMhBAMAAMB0CMEAAAAwHUIwAAAATIcQDAAAANMhBAMAAMB0CMEAAAAwHUIwAAAATIcQDAAAANMhBAMAAMB0CMEAAAAwHUIwAAAATIcQDAAAANMhBAMAAMB0CMEAAAAwHUIwAAAATIcQDAAAANMhBAMAAMB0CMEAAAAwnfs+BGdkZMjOzk4//fTTddtMnjxZgYGBd60ms6rItbiWj4+PZs2adcdqAgAA5nRPheChQ4fKzs6uzLJ37947etwxY8Zo/fr1t7XP0sBXt25dXb582Wrb5s2bLed2bfu2bduquLjYqn2dOnWUmppq+XxtMNy+fbv69OmjBg0aqEaNGvLx8VG/fv104sQJTZ48udwx/eUi/d/YDxs2rMy5DB8+XHZ2dho6dOitD4yNrly5Ig8PDyUmJpa7ferUqWrYsKGKiook/TyeHTp0kJOTk1q0aGE1fgAAAPdUCJakXr166ccff7RamjVrdkePWatWLdWrV++O9O3q6qqVK1darVu4cKG8vb3LbZ+fn6/333+/wv2fPHlSPXr0kLu7u9auXau8vDylpKTI09NTFy5c0JgxY6zG8oEHHtCrr75qta6Ul5eXli1bpkuXLlnWXb58WUuXLr1uvXeLo6OjBg8erJSUlDLbDMNQamqqoqKi5ODgoP379ys8PFwhISHKyclRXFycYmNjtXbt2iqoHAAA3IvuuRDs5OSkRo0aWS2zZ8+Wv7+/XFxc5OXlpRdffFHnz5+37HPw4EFFRESobt26cnFxUdu2bbV69WqrfrOzs9WxY0c5OzsrKChIu3btsmy7djpESUmJXn31VT3wwANycnJSYGCg1qxZY9l+4MAB2dnZKT09XSEhIXJ2dla7du20adOmMuczZMgQLVq0yPL50qVLWrZsmYYMGVLu+b/00kuKj49XYWFhhcYrKytLZ8+e1YIFC9S+fXs1a9ZMISEhevPNN9WsWTPVqlXLaiyrV68uV1dXq3WlOnToIC8vL6Wnp1vWpaeny9vbW+3bt7c6bmFhoUaOHGm5+/zoo49q8+bNVm1Wr14tPz8/1axZUyEhITpw4ECZ+jMzM9W1a1fVrFlTXl5eGjlypC5cuFDuucbExGj37t3KzMy0Wv/ll18qPz9fMTExkqR58+apWbNmSk5OVuvWrTVixAg988wzevPNN687joWFhSooKLBaAADA/eueC8HlqVatmubMmaPvv/9eixcv1oYNGzRu3DjL9uHDh6uwsFBfffWVcnNzNXPmTNWqVcuqj4kTJyo5OVlbtmyRvb29oqOjr3u82bNnKzk5WUlJSdqxY4fCwsLUp08f7dmzp0yfY8aMUU5Ojvz8/DRgwABdvXrVqk1kZKS+/vprHTp0SJKUlpYmHx8fdejQodxjx8XF6erVq5o7d26FxqZRo0a6evWqVq5cKcMwKrTPjURHR1vdbV20aJGee+65Mu3GjRuntLQ0LV68WFu3blWLFi0UFham06dPS5IOHz6sp556ShEREcrJyVFsbKwmTJhg1ce+ffvUq1cvPf3009qxY4eWL1+uzMxMjRgxotza/P399fDDD1t9qZCklJQUBQUFqVWrVpKkTZs2qWfPnlZtwsLCyv2SUiohIUG1a9e2LF5eXjcYJQAA8Gt3z4XgVatWqVatWpalb9++iouLU0hIiHx8fBQaGqpp06ZpxYoVln0OHTqk4OBg+fv7q3nz5nr88cfVrVs3q36nT5+u7t27q02bNpowYYI2btxYZq5uqaSkJI0fP179+/fXgw8+qJkzZyowMLDMA1pjxoxReHi4/Pz8NGXKFB08eLDM/OUGDRqod+/eljmpixYtumEAd3Z2Vnx8vBISEnT27Nmbjlfnzp31yiuvaODAgfLw8FDv3r31+uuv6/jx4zfdtzyDBw9WZmamDh48qIMHDyorK0uDBw+2anPhwgW9++67ev3119W7d2+1adNG8+fPV82aNbVw4UJJ0rvvvitfX18lJyfrwQcf1KBBg8rMKU5ISNCgQYMUFxenli1bKigoSHPmzNH7779/3WsTExOjDz/80PIvAefOndNHH31kNabHjh1Tw4YNrfZr2LChCgoKrKZ6/NLLL7+ss2fPWpbDhw9XatwAAMCvyz0XgkvncZYuc+bM0eeff64ePXqoSZMmcnV1VWRkpE6dOqWLFy9KkkaOHKlp06YpODhY8fHx2rFjR5l+AwICLH9u3LixJOnEiRNl2hUUFOjo0aMKDg62Wh8cHKy8vDyb+oyOjlZqaqry8/O1adMmDRo06IZjEBMTo3r16mnmzJk3bFdq+vTpOnbsmObNm6e2bdtq3rx5atWqlXJzcyu0/y/Vr19f4eHhSk1NVUpKisLDw+Xh4WHVZt++fSoqKrIaIwcHB3Xq1MkyRnl5eXrkkUes9uvSpYvV5+3btys1NdXqS09YWJhKSkq0f//+cusbMGCAiouLLV+Cli9frmrVqqlfv36VPtdfcnJykpubm9UCAADuX/dcCHZxcVGLFi0sS2FhoR5//HEFBAQoLS1N2dnZevvttyX9/MYASYqNjVV+fr4iIyOVm5urjh07lplO4ODgYPlz6RsRSkpKbqnWivbZu3dvXbp0STExMYqIiLjpQ3j29vaaPn26Zs+eraNHj1aolnr16qlv375KSkpSXl6ePD09lZSUVImz+T+loX3x4sU3vGt9q86fP6/nn3/e6kvP9u3btWfPHvn6+pa7j5ubm5555hnLlI2UlBQ9++yzVtNfGjVqVOZO+PHjx+Xm5qaaNWvesfMBAAC/HvdcCL5Wdna2SkpKlJycrM6dO8vPz6/cYOjl5aVhw4YpPT1do0eP1vz58206npubmzw9PZWVlWW1PisrS23atLGpT3t7e0VFRSkjI6PCobJv375q27atpkyZUunjOTo6ytfX97oPmN1Mr169dOXKFRUVFSksLKzMdl9fXzk6OlqNUVFRkTZv3mwZo9atW+vbb7+12u+bb76x+tyhQwft3LnT6ktP6eLo6Hjd+mJiYpSZmalVq1Zp48aNlgfiSnXp0qXMK+/WrVtX5k40AAAwL/uqLuBmWrRooaKiIs2dO1cRERHKysrSvHnzrNrExcWpd+/e8vPz05kzZ/TFF1+odevWNh9z7Nixio+Pl6+vrwIDA5WSkqKcnBwtWbLE5j6nTp2qsWPHVupVbImJieWG0F9atWqVli1bpv79+8vPz0+GYejTTz/V6tWry32dWEVUr17dMq2hevXqZba7uLjohRde0NixY+Xu7i5vb2+99tprunjxoiWQDhs2TMnJyRo7dqxiY2OVnZ1d5l2948ePV+fOnTVixAjFxsbKxcVFO3fu1Lp16/TWW29dt75u3bqpRYsWioqKUqtWrRQUFGS1fdiwYXrrrbc0btw4RUdHa8OGDVqxYoU+++wzm8YDAADcf+75O8Ht2rXTG2+8oZkzZ+qhhx7SkiVLlJCQYNWmuLhYw4cPV+vWrdWrVy/5+fnpnXfesfmYI0eO1KhRozR69Gj5+/trzZo1+uSTT9SyZUub+3R0dJSHh4fVL8i4mdDQUIWGhpZ548QvtWnTRs7Ozho9erQCAwPVuXNnrVixQgsWLFBkZKTN9d5sXmxiYqKefvppRUZGqkOHDtq7d6/Wrl2runXrSpK8vb2Vlpamjz/+WO3atdO8efM0Y8YMqz4CAgL05Zdfavfu3eratavat2+vv/71r/L09LxhbXZ2doqOjtaZM2fKvbPerFkzffbZZ1q3bp3atWun5ORkLViw4KZfKAAAgHnYGbfjvVrAfaagoODnV6XFrVA1J+eqLgcAgDviQGJ4VZdwW5X+/X327NmbPuR+z98JBgAAAG43QjAAAABMhxAMAAAA0yEEAwAAwHQIwQAAADAdQjAAAABMhxAMAAAA0yEEAwAAwHQIwQAAADAdQjAAAABMhxAMAAAA0yEEAwAAwHQIwQAAADAdQjAAAABMhxAMAAAA0yEEAwAAwHQIwQAAADAdQjAAAABMhxAMAAAA0yEEAwAAwHQIwQAAADAdQjAAAABMhxAMAAAA07Gv6gKAe9l3U8Lk5uZW1WUAAIDbjDvBAAAAMB1CMAAAAEyHEAwAAADTIQQDAADAdAjBAAAAMB1CMAAAAEyHEAwAAADTIQQDAADAdAjBAAAAMB1CMAAAAEyHEAwAAADTIQQDAADAdAjBAAAAMB1CMAAAAEyHEAwAAADTIQQDAADAdAjBAAAAMB1CMAAAAEyHEAwAAADTIQQDAADAdAjBAAAAMB1CMAAAAEyHEAwAAADTIQQDAADAdAjBAAAAMB1CMAAAAEyHEAwAAADTIQQDAADAdAjBAAAAMB37qi4AuBcZhiFJKigoqOJKAABARZX+vV369/iNEIKBcpw6dUqS5OXlVcWVAACAyjp37pxq1659wzaEYKAc7u7ukqRDhw7d9IcIVa+goEBeXl46fPiw3Nzcqroc3ADX6teF6/XrwbX6mWEYOnfunDw9PW/alhAMlKNatZ+ny9euXdvU/zP5tXFzc+N6/UpwrX5duF6/HlwrVfjmFQ/GAQAAwHQIwQAAADAdQjBQDicnJ8XHx8vJyamqS0EFcL1+PbhWvy5cr18PrlXl2RkVeYcEAAAAcB/hTjAAAABMhxAMAAAA0yEEAwAAwHQIwQAAADAdQjBM6+2335aPj49q1KihRx55RN9+++0N23/44Ydq1aqVatSoIX9/f61evfouVQqpctdr/vz56tq1q+rWrau6deuqZ8+eN72+uH0q+7NVatmyZbKzs9Pvf//7O1sgrFT2ev30008aPny4GjduLCcnJ/n5+fH/w7ukstdq1qxZevDBB1WzZk15eXnpz3/+sy5fvnyXqv0VMAATWrZsmeHo6GgsWrTI+P77740//OEPRp06dYzjx4+X2z4rK8uoXr268dprrxk7d+40Jk2aZDg4OBi5ubl3uXJzquz1GjhwoPH2228b27ZtM/Ly8oyhQ4catWvXNo4cOXKXKzefyl6rUvv37zeaNGlidO3a1XjiiSfuTrGo9PUqLCw0OnbsaDz22GNGZmamsX//fiMjI8PIycm5y5WbT2Wv1ZIlSwwnJydjyZIlxv79+421a9cajRs3Nv785z/f5crvXYRgmFKnTp2M4cOHWz4XFxcbnp6eRkJCQrntn332WSM8PNxq3SOPPGI8//zzd7RO/Kyy1+taV69eNVxdXY3FixffqRLxv2y5VlevXjWCgoKMBQsWGEOGDCEE30WVvV7vvvuu0bx5c+PKlSt3q0T8r8peq+HDhxuhoaFW60aNGmUEBwff0Tp/TZgOAdO5cuWKsrOz1bNnT8u6atWqqWfPntq0aVO5+2zatMmqvSSFhYVdtz1uH1uu17UuXryooqIiubu736kyIduv1auvvqoGDRooJibmbpSJ/2XL9frkk0/UpUsXDR8+XA0bNtRDDz2kGTNmqLi4+G6VbUq2XKugoCBlZ2dbpkzk5+dr9erVeuyxx+5Kzb8G9lVdAHC3/fe//1VxcbEaNmxotb5hw4b6z3/+U+4+x44dK7f9sWPH7lid+Jkt1+ta48ePl6enZ5kvMri9bLlWmZmZWrhwoXJycu5ChfglW65Xfn6+NmzYoEGDBmn16tXau3evXnzxRRUVFSk+Pv5ulG1KtlyrgQMH6r///a8effRRGYahq1evatiwYXrllVfuRsm/CtwJBnBfS0xM1LJly7Ry5UrVqFGjqsvBL5w7d06RkZGaP3++PDw8qrocVEBJSYkaNGig9957T7/5zW/Ur18/TZw4UfPmzavq0nCNjIwMzZgxQ++88462bt2q9PR0ffbZZ5o6dWpVl3bP4E4wTMfDw0PVq1fX8ePHrdYfP35cjRo1KnefRo0aVao9bh9brleppKQkJSYm6vPPP1dAQMCdLBOq/LXat2+fDhw4oIiICMu6kpISSZK9vb127dolX1/fO1u0idnys9W4cWM5ODioevXqlnWtW7fWsWPHdOXKFTk6Ot7Rms3Klmv1l7/8RZGRkYqNjZUk+fv768KFC/rjH/+oiRMnqlo17oMyAjAdR0dH/eY3v9H69est60pKSrR+/Xp16dKl3H26dOli1V6S1q1bd932uH1suV6S9Nprr2nq1Klas2aNOnbseDdKNb3KXqtWrVopNzdXOTk5lqVPnz4KCQlRTk6OvLy87mb5pmPLz1ZwcLD27t1r+bIiSbt371bjxo0JwHeQLdfq4sWLZYJu6ZcXwzDuXLG/JlX9ZB5QFZYtW2Y4OTkZqampxs6dO40//vGPRp06dYxjx44ZhmEYkZGRxoQJEyzts7KyDHt7eyMpKcnIy8sz4uPjeUXaXVTZ65WYmGg4OjoaH330kfHjjz9alnPnzlXVKZhGZa/VtXg7xN1V2et16NAhw9XV1RgxYoSxa9cuY9WqVUaDBg2MadOmVdUpmEZlr1V8fLzh6upqfPDBB0Z+fr7xr3/9y/D19TWeffbZqjqFew4hGKY1d+5cw9vb23B0dDQ6depkfPPNN5Zt3bt3N4YMGWLVfsWKFYafn5/h6OhotG3b1vjss8/ucsXmVpnr1bRpU0NSmSU+Pv7uF25Clf3Z+iVC8N1X2eu1ceNG45FHHjGcnJyM5s2bG9OnTzeuXr16l6s2p8pcq6KiImPy5MmGr6+vUaNGDcPLy8t48cUXjTNnztz9wu9RdobBPXEAAACYC3OCAQAAYDqEYAAAAJgOIRgAAACmQwgGAACA6RCCAQAAYDqEYAAAAJgOIRgAAACmQwgGAACA6RCCAQC4A7p166alS5feUh+dO3dWWlrabaoIwC8RggEAuM0++eQTHT9+XP3797esGzVqlNzd3eXl5aUlS5ZYtf/www8VERFRpp9JkyZpwoQJKikpueM1A2bDr00GANyXioqK5ODgUCXH7tmzp3r27KkJEyZIkj799FP94Q9/0KpVq7Rnzx5FR0fr8OHD8vDw0NmzZ/Xwww/r888/l7e3t1U/xcXFatKkiRYuXKjw8PCqOBXgvsWdYADALVuzZo0effRR1alTR/Xq1dPjjz+uffv2WbU5cuSIBgwYIHd3d7m4uKhjx47697//bdn+6aef6uGHH1aNGjXk4eGhJ5980rLNzs5OH3/8sVV/derUUWpqqiTpwIEDsrOz0/Lly9W9e3fVqFFDS5Ys0alTpzRgwAA1adJEzs7O8vf31wcffGDVT0lJiV577TW1aNFCTk5O8vb21vTp0yVJoaGhGjFihFX7kydPytHRUevXry93LE6ePKkNGzZY3dnNy8vT//zP/6hjx44aMGCA3NzctH//fknSuHHj9MILL5QJwJJUvXp1PfbYY1q2bFm5xwJgO0IwAOCWXbhwQaNGjdKWLVu0fv16VatWTU8++aTln/HPnz+v7t2764cfftAnn3yi7du3a9y4cZbtn332mZ588kk99thj2rZtm9avX69OnTpVuo4JEyboT3/6k/Ly8hQWFqbLly/rN7/5jT777DN99913+uMf/6jIyEh9++23ln1efvllJSYm6i9/+Yt27typpUuXqmHDhpKk2NhYLV26VIWFhZb2f//739WkSROFhoaWW0NmZqacnZ3VunVry7p27dppy5YtOnPmjLKzs3Xp0iW1aNFCmZmZ2rp1q0aOHHndc+rUqZO+/vrrSo8FgJswAAC4zU6ePGlIMnJzcw3DMIy//e1vhqurq3Hq1Kly23fp0sUYNGjQdfuTZKxcudJqXe3atY2UlBTDMAxj//79hiRj1qxZN60tPDzcGD16tGEYhlFQUGA4OTkZ8+fPL7ftpUuXjLp16xrLly+3rAsICDAmT5583f7ffPNNo3nz5mXWx8fHG76+vsZDDz1kpKenG4WFhcZDDz1kbNmyxZg7d67h5+dnBAUFGd99953Vfv/4xz+MatWqGcXFxTc9NwAVx51gAMAt27NnjwYMGKDmzZvLzc1NPj4+kqRDhw5JknJyctS+fXu5u7uXu39OTo569Ohxy3V07NjR6nNxcbGmTp0qf39/ubu7q1atWlq7dq2lrry8PBUWFl732DVq1FBkZKQWLVokSdq6dau+++47DR069Lo1XLp0STVq1CizfvLkydq7d69yc3P15JNPKiEhQT179pSDg4OmTZumzMxMxcbGKioqymq/mjVrqqSkxOpuNIBbZ1/VBQAAfv0iIiLUtGlTzZ8/X56eniopKdFDDz2kK1euSPo5yN3Izbbb2dnJuOY57qKiojLtXFxcrD6//vrrmj17tmbNmiV/f3+5uLgoLi6uwnVJP0+JCAwM1JEjR5SSkqLQ0FA1bdr0uu09PDx05syZG/b5n//8R3//+9+1bds2LVq0SN26dVP9+vX17LPPKjo6WufOnZOrq6sk6fTp03JxcalQrQAqjjvBAIBbcurUKe3atUuTJk1Sjx491Lp16zIhMCAgQDk5OTp9+nS5fQQEBFz3QTNJql+/vn788UfL5z179ujixYs3rS0rK0tPPPGEBg8erHbt2ql58+bavXu3ZXvLli1Vs2bNGx7b399fHTt21Pz587V06VJFR0ff8Jjt27fXsWPHrhuEDcPQ888/rzfeeEO1atVScXGxJdCX/re4uNjS/rvvvlP79u1veq4AKocQDAC4JXXr1lW9evX03nvvae/evdqwYYNGjRpl1WbAgAFq1KiRfv/73ysrK0v5+flKS0vTpk2bJEnx8fH64IMPFB8fr7y8POXm5mrmzJmW/UNDQ/XWW29p27Zt2rJli4YNG1ah15+1bNlS69at08aNG5WXl6fnn39ex48ft2yvUaOGxo8fr3Hjxun999/Xvn379M0332jhwoVW/cTGxioxMVGGYVi9taI87du3l4eHh7KyssrdvmDBAtWvX9/y9ojg4GBt2LBB33zzjd588021adNGderUsbT/+uuv9bvf/e6m5wqgkqp4TjIA4D6wbt06o3Xr1oaTk5MREBBgZGRklHmY7cCBA8bTTz9tuLm5Gc7OzkbHjh2Nf//735btaWlpRmBgoOHo6Gh4eHgYTz31lGXbDz/8YPzud78zXFxcjJYtWxqrV68u98G4bdu2WdV16tQp44knnjBq1aplNGjQwJg0aZIRFRVlPPHEE5Y2xcXFxrRp04ymTZsaDg4Ohre3tzFjxgyrfs6dO2c4OzsbL774YoXGY9y4cUb//v3LrD927JjRtGlT44cffrBaP2XKFMPd3d1o1aqV1ZgcOXLEcHBwMA4fPlyh4wKoOH5ZBgAAN3HgwAH5+vpq8+bN6tChw03bHzt2TG3bttXWrVtvOH/4ZsaPH68zZ87ovffes7kPAOVjOgQAANdRVFSkY8eOadKkSercuXOFArAkNWrUSAsXLrS8hcJWDRo00NSpU2+pDwDl404wAADXkZGRoZCQEPn5+emjjz6Sv79/VZcE4DYhBAMAAMB0mA4BAAAA0yEEAwAAwHQIwQAAADAdQjAAAABMhxAMAAAA0yEEAwAAwHQIwQAAADAdQjAAAABM5/8Do5sEouqJmHsAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 9. Make and evaluate random predictions with best model"
      ],
      "metadata": {
        "id": "Ie9N7rYhc4R7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def make_predictions(model: torch.nn.Module, data: list, device: torch.device = device):\n",
        "  pred_probs = []\n",
        "  model.eval()\n",
        "  with torch.inference_mode():\n",
        "    for sample in data:\n",
        "      # add batch size dimension\n",
        "      sample = torch.unsqueeze(sample, dim=0).to(device)\n",
        "      pred_logits = model(sample)\n",
        "      pred_prob = torch.softmax(pred_logits.squeeze(), dim=0)\n",
        "      pred_probs.append(pred_prob.cpu())\n",
        "  return torch.stack(pred_probs)"
      ],
      "metadata": {
        "id": "HRWuY3CgdN46"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import random\n",
        "random.seed(42)\n",
        "test_samples = []\n",
        "test_labels = []\n",
        "for sample, label in random.sample(list(test_data), k=9):\n",
        "  test_samples.append(sample)\n",
        "  test_labels.append(label)\n",
        "\n",
        "print(f\"Test sample image shape: {test_samples[0].shape}\\nTest sample label: {test_labels[0]} ({class_names[test_labels[0]]})\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AzXLv8lheRuu",
        "outputId": "a620e90c-48aa-4c96-fbff-46e7744b8a1c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test sample image shape: torch.Size([1, 28, 28])\n",
            "Test sample label: 5 (Sandal)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Make predictions on test samples with model 2\n",
        "pred_probs= make_predictions(model=model2,\n",
        "                             data=test_samples)\n",
        "\n",
        "# View first two prediction probabilities list\n",
        "pred_probs = torch.softmax(pred_probs, dim=1).argmax(dim=1)\n",
        "pred_probs[:2]\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qRs1Vrw0emrS",
        "outputId": "d744a94d-4abb-4262-dcda-630df0a51388"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([5, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot predictions\n",
        "plt.figure(figsize=(9, 9))\n",
        "nrows = 3\n",
        "ncols = 3\n",
        "\n",
        "for i, sample in enumerate(test_samples):\n",
        "  # Create subplot\n",
        "  plt.subplot(nrows, ncols, i+1)\n",
        "\n",
        "  # Plot the target image\n",
        "  plt.imshow(sample.squeeze(), cmap=\"gray\")\n",
        "\n",
        "  # Find the prediction label\n",
        "  pred_label = class_names[pred_probs[i]]\n",
        "\n",
        "  # Get the truth label\n",
        "  truth_label = class_names[test_labels[i]]\n",
        "\n",
        "  # Create a title for the plot\n",
        "  title_text = f\"Pred: {pred_label} | Truth: {truth_label}\"\n",
        "\n",
        "  # Check for equality\n",
        "  if pred_label == truth_label:\n",
        "    color = \"green\"\n",
        "  else:\n",
        "    color = \"red\"\n",
        "\n",
        "  # Change the color of the title text\n",
        "  plt.title(title_text, fontsize=10, color=color)\n",
        "  plt.axis(False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 749
        },
        "id": "6ot3a7egf6Jq",
        "outputId": "e4a0f067-61f3-42ce-f2e3-7294c9c1dede"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 900x900 with 9 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 10.Making a confusion matrix for further prediction evaluation"
      ],
      "metadata": {
        "id": "mxAaqWF5hQlq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from tqdm.auto import tqdm\n",
        "y_preds = []\n",
        "model2.eval()\n",
        "with torch.inference_mode():\n",
        "  for X, y in tqdm(test_dataloader, desc=\"Making predictions...\"):\n",
        "    X, y = X.to(device), y.to(device)\n",
        "    y_logit = model2(X)\n",
        "    y_pred = torch.softmax(y_logit, dim=1).argmax(dim=1)\n",
        "    y_preds.append(y_pred)\n",
        "y_pred_tensor = torch.cat(y_preds)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "c6e60d71cf7f49d9b6ef130ad5d2abe4",
            "da1e677af7f14fea84c67202fe170763",
            "18c6deca2689449cb3b003da94d042b1",
            "63063ee568a74ca68f4664b11ac5bf09",
            "320d0e3690e54f839b00da31c88bb1c3",
            "062c90de8d3b46488c022d404cbf7a7b",
            "b08d6ffeb7c34be2998a3c68345b356e",
            "ed4d56ba46b5405c9603e0b40eacdfc9",
            "c7fa8b89046f4aada480210e623d4f32",
            "6126f1b9917247ed8f3857c1179de103",
            "0440ca0e4e3c437d805e742611658e87"
          ]
        },
        "id": "RzXzZ1lfhXzW",
        "outputId": "1177f18b-2c7d-4f6b-e60a-48b558d1a318"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Making predictions...:   0%|          | 0/313 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c6e60d71cf7f49d9b6ef130ad5d2abe4"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "try:\n",
        "  import torchmetrics, mlxtend\n",
        "  print(f\"mlxtend version: {mlxtend.__version__}\")\n",
        "  assert int(mlxtend.__version__.split(\".\")[1]) >= 19, \"mlxtend version should be 0.19.0 or higher\"\n",
        "\n",
        "except:\n",
        "  %pip install -q torchmetrics -U mlxtend\n",
        "  import torchmetrics, mlxtend\n",
        "  print(f\"mlxtend version: {mlxtend.__version__}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0gcSsFbihy-H",
        "outputId": "a4f81e49-6ae0-41da-f332-767fa81daba3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "mlxtend version: 0.22.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from torchmetrics import ConfusionMatrix\n",
        "from mlxtend.plotting import plot_confusion_matrix\n",
        "\n",
        "# 2. setup confusion matrix instance and compare predictions targets\n",
        "confmat = ConfusionMatrix(num_classes=len(class_names), task=\"multiclass\")\n",
        "confmat_tensor = confmat(preds=y_pred_tensor.cpu(), target=test_data.targets.cpu())\n",
        "\n",
        "# 3.Plot the confusion matrix\n",
        "fig, ax = plot_confusion_matrix(\n",
        "    conf_mat=confmat_tensor.numpy(),\n",
        "    class_names=class_names,\n",
        "    figsize=(9, 9)\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 766
        },
        "id": "zyc2HoPEjXWd",
        "outputId": "cdfecf86-3dfe-41b2-c3d9-6a286aa8136a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 900x900 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 11. Save and load best preforming model"
      ],
      "metadata": {
        "id": "Tf78BIqPktlQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from pathlib import Path\n",
        "\n",
        "MODEL_PATH = Path(\"models\")\n",
        "MODEL_PATH.mkdir(parents=True, exist_ok=True)\n",
        "\n",
        "MODEL_NAME = \"03_pytorch_computer_version_model_2.pth\"\n",
        "\n",
        "MODEL_SAVE_PATH = MODEL_PATH / MODEL_NAME\n",
        "\n",
        "# Save the model state dict()\n",
        "print(f\"Save the model to {MODEL_SAVE_PATH}\")\n",
        "\n",
        "torch.save(model2.state_dict(), f=MODEL_SAVE_PATH)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m2R5usqqkzqu",
        "outputId": "41280400-6064-46ad-93cd-d8a3ee0b6aca"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Save the model to models/03_pytorch_computer_version_model_2.pth\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "loaded_model_2 = FashionMINISTModelV2(input_shape=1,\n",
        "                     hidden_units=10,\n",
        "                     output_shape=10)\n",
        "loaded_model_2.load_state_dict(torch.load(f=MODEL_SAVE_PATH))\n",
        "\n",
        "loaded_model_2 = loaded_model_2.to(device)"
      ],
      "metadata": {
        "id": "cQsH_ZIAl5V0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluate loaded model\n",
        "torch.manual_seed(42)\n",
        "\n",
        "loaded_model_2_results = eval_mode(\n",
        "    model=loaded_model_2,\n",
        "    data_loader=test_dataloader,\n",
        "    loss_fn=loss_fn,\n",
        "    accuracy_fn=accuracy_fn\n",
        ")\n",
        "\n",
        "loaded_model_2_resultss"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pFyPyqVTmd9j",
        "outputId": "9ca97d10-24b9-48d8-ff74-abb4b66389a7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'model_name': 'FashionMINISTModelV2',\n",
              " 'model_loss': 0.34064412117004395,\n",
              " 'model_acc': tensor(0.8789, device='cuda:0')}"
            ]
          },
          "metadata": {},
          "execution_count": 90
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check to see if results are close to each other\n",
        "# Check to see if results are close to each other (if they are very far away, there may be an error)\n",
        "torch.isclose(torch.tensor(model_2_results[\"model_loss\"]),\n",
        "              torch.tensor(loaded_model_2_results[\"model_loss\"]),\n",
        "              atol=1e-08, # absolute tolerance\n",
        "              rtol=0.0001) # relative tolerance"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AcmmtasNmf2U",
        "outputId": "42001023-4a2a-4d9f-d200-c58e8f5e0538"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor(True)"
            ]
          },
          "metadata": {},
          "execution_count": 91
        }
      ]
    }
  ],
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    "colab": {
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      "toc_visible": true,
      "provenance": [],
      "authorship_tag": "ABX9TyPcuIRmsLJ03UeMKA81W5Z5",
      "include_colab_link": true
    },
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