03_pytorch_computer_vision.ipynb

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  "cells": [
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      "metadata": {
        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/aishwary247/Deep-Learning/blob/main/03_pytorch_computer_vision.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
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      "execution_count": 37,
      "metadata": {
        "id": "P0TOO0VUvkTc"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "from torch import nn\n",
        "\n",
        "import torchvision\n",
        "from torchvision import datasets\n",
        "from torchvision import transforms\n",
        "from torchvision.transforms import ToTensor"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "print(torch.__version__)\n",
        "print(torchvision.__version__)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6-wP86BCArEJ",
        "outputId": "ec2c0baa-22e3-4965-8751-7f4a8bec65d1"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2.1.0+cu121\n",
            "0.16.0+cu121\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Getting Dataset"
      ],
      "metadata": {
        "id": "LFj0A1xoBjG7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from  torchvision import datasets\n",
        "train_data = datasets.FashionMNIST(\n",
        "    root = \"data\", # where to download data to\n",
        "    train = True, # do we want the training set\n",
        "    download = True,\n",
        "    transform = torchvision.transforms.ToTensor(), # how do we want to transform the data\n",
        "    target_transform= None # how do we want to transform the labels/targets\n",
        ")\n",
        "\n",
        "test_data = datasets.FashionMNIST(\n",
        "    root = \"data\",\n",
        "    train = False,\n",
        "    download = True,\n",
        "    transform = torchvision.transforms.ToTensor(),\n",
        "    target_transform=None\n",
        ")"
      ],
      "metadata": {
        "id": "Q17EPq3zBhHx"
      },
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "len(train_data), len(test_data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a4NpCEpKHtOg",
        "outputId": "ef6dc304-b209-4c59-e684-c2022552be60"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
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            ]
          },
          "metadata": {},
          "execution_count": 40
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    },
    {
      "cell_type": "code",
      "source": [
        "image, label = train_data[0]\n",
        "image, label\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lf80mojoHwo7",
        "outputId": "c3c9b770-b434-49e3-e2ff-e19bb69097aa"
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      "execution_count": 41,
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              "           0.0000, 0.2431, 0.5686, 0.8000, 0.8941, 0.8118, 0.8353, 0.8667,\n",
              "           0.8549, 0.8157, 0.8275, 0.8549, 0.8784, 0.8745, 0.8588, 0.8431,\n",
              "           0.8784, 0.9569, 0.6235, 0.0000],\n",
              "          [0.0000, 0.0000, 0.0000, 0.0000, 0.0706, 0.1725, 0.3216, 0.4196,\n",
              "           0.7412, 0.8941, 0.8627, 0.8706, 0.8510, 0.8863, 0.7843, 0.8039,\n",
              "           0.8275, 0.9020, 0.8784, 0.9176, 0.6902, 0.7373, 0.9804, 0.9725,\n",
              "           0.9137, 0.9333, 0.8431, 0.0000],\n",
              "          [0.0000, 0.2235, 0.7333, 0.8157, 0.8784, 0.8667, 0.8784, 0.8157,\n",
              "           0.8000, 0.8392, 0.8157, 0.8196, 0.7843, 0.6235, 0.9608, 0.7569,\n",
              "           0.8078, 0.8745, 1.0000, 1.0000, 0.8667, 0.9176, 0.8667, 0.8275,\n",
              "           0.8627, 0.9098, 0.9647, 0.0000],\n",
              "          [0.0118, 0.7922, 0.8941, 0.8784, 0.8667, 0.8275, 0.8275, 0.8392,\n",
              "           0.8039, 0.8039, 0.8039, 0.8627, 0.9412, 0.3137, 0.5882, 1.0000,\n",
              "           0.8980, 0.8667, 0.7373, 0.6039, 0.7490, 0.8235, 0.8000, 0.8196,\n",
              "           0.8706, 0.8941, 0.8824, 0.0000],\n",
              "          [0.3843, 0.9137, 0.7765, 0.8235, 0.8706, 0.8980, 0.8980, 0.9176,\n",
              "           0.9765, 0.8627, 0.7608, 0.8431, 0.8510, 0.9451, 0.2549, 0.2863,\n",
              "           0.4157, 0.4588, 0.6588, 0.8588, 0.8667, 0.8431, 0.8510, 0.8745,\n",
              "           0.8745, 0.8784, 0.8980, 0.1137],\n",
              "          [0.2941, 0.8000, 0.8314, 0.8000, 0.7569, 0.8039, 0.8275, 0.8824,\n",
              "           0.8471, 0.7255, 0.7725, 0.8078, 0.7765, 0.8353, 0.9412, 0.7647,\n",
              "           0.8902, 0.9608, 0.9373, 0.8745, 0.8549, 0.8314, 0.8196, 0.8706,\n",
              "           0.8627, 0.8667, 0.9020, 0.2627],\n",
              "          [0.1882, 0.7961, 0.7176, 0.7608, 0.8353, 0.7725, 0.7255, 0.7451,\n",
              "           0.7608, 0.7529, 0.7922, 0.8392, 0.8588, 0.8667, 0.8627, 0.9255,\n",
              "           0.8824, 0.8471, 0.7804, 0.8078, 0.7294, 0.7098, 0.6941, 0.6745,\n",
              "           0.7098, 0.8039, 0.8078, 0.4510],\n",
              "          [0.0000, 0.4784, 0.8588, 0.7569, 0.7020, 0.6706, 0.7176, 0.7686,\n",
              "           0.8000, 0.8235, 0.8353, 0.8118, 0.8275, 0.8235, 0.7843, 0.7686,\n",
              "           0.7608, 0.7490, 0.7647, 0.7490, 0.7765, 0.7529, 0.6902, 0.6118,\n",
              "           0.6549, 0.6941, 0.8235, 0.3608],\n",
              "          [0.0000, 0.0000, 0.2902, 0.7412, 0.8314, 0.7490, 0.6863, 0.6745,\n",
              "           0.6863, 0.7098, 0.7255, 0.7373, 0.7412, 0.7373, 0.7569, 0.7765,\n",
              "           0.8000, 0.8196, 0.8235, 0.8235, 0.8275, 0.7373, 0.7373, 0.7608,\n",
              "           0.7529, 0.8471, 0.6667, 0.0000],\n",
              "          [0.0078, 0.0000, 0.0000, 0.0000, 0.2588, 0.7843, 0.8706, 0.9294,\n",
              "           0.9373, 0.9490, 0.9647, 0.9529, 0.9569, 0.8667, 0.8627, 0.7569,\n",
              "           0.7490, 0.7020, 0.7137, 0.7137, 0.7098, 0.6902, 0.6510, 0.6588,\n",
              "           0.3882, 0.2275, 0.0000, 0.0000],\n",
              "          [0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.1569,\n",
              "           0.2392, 0.1725, 0.2824, 0.1608, 0.1373, 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",
              " 9)"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class_names = train_data.classes\n",
        "class_names"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s63eCyMeH1gS",
        "outputId": "e0094b55-1a65-4076-84ef-887b16486ce8"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "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']"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class_to_idx = train_data.class_to_idx\n",
        "class_to_idx"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d49smOFPIXN9",
        "outputId": "952213dc-c59e-47bc-e453-145d30cb9e0c"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'T-shirt/top': 0,\n",
              " 'Trouser': 1,\n",
              " 'Pullover': 2,\n",
              " 'Dress': 3,\n",
              " 'Coat': 4,\n",
              " 'Sandal': 5,\n",
              " 'Shirt': 6,\n",
              " 'Sneaker': 7,\n",
              " 'Bag': 8,\n",
              " 'Ankle boot': 9}"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_data.targets"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MhBbpbvLIixF",
        "outputId": "aff636ed-e2d1-4572-ac35-176fad696873"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([9, 0, 0,  ..., 3, 0, 5])"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "image.shape, label"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p9TA4jj3JssW",
        "outputId": "06f7f298-7f0b-440a-d816-b36f00edc34f"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(torch.Size([1, 28, 28]), 9)"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Visualise the data"
      ],
      "metadata": {
        "id": "yuBRRQYwuy8O"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.title(class_names[label])\n",
        "plt.imshow(image.squeeze())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 470
        },
        "id": "tlv0l7rVJvx8",
        "outputId": "18ee4efa-26e0-44b5-8ccf-f59095f660e8"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7c463582bee0>"
            ]
          },
          "metadata": {},
          "execution_count": 46
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.imshow(image.squeeze(), cmap= \"gray\")\n",
        "plt.axis(False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "6DaNBEfEvkVu",
        "outputId": "d21d0d88-3028-4144-8373-94070bcb049d"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(-0.5, 27.5, 27.5, -0.5)"
            ]
          },
          "metadata": {},
          "execution_count": 47
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Seeing random data from the dataset\n",
        "fig = plt.figure(figsize = (9,9))\n",
        "rows, cols = 4,4\n",
        "for i in range(1,rows*cols +1 ):\n",
        "  random_idx = torch.randint(0,len(train_data),size=[1]).item()\n",
        "  img, label = train_data[random_idx]\n",
        "  fig.add_subplot(rows, cols,i)\n",
        "  plt.imshow(img.squeeze(),cmap = \"gray\")\n",
        "  plt.title(class_names[label])\n",
        "  plt.axis(False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 752
        },
        "id": "Y0Y_LKSawH60",
        "outputId": "533c4436-b2d7-4f8d-f4f9-16ca899f2aca"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 900x900 with 16 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Prepare Data Loader\n",
        "Right now, our data is in the form of PyTorch Datasets.\n",
        "\n",
        "DataLoader turns our dataset into a Python 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, as in, your computing hardware may not be able to look (store in memory) at 60000 images in one hit. So we break it down to 32 images at a time (batch size of 32).\n",
        "\n",
        "2. It gives our neural network more chances to update its gradients per epoch."
      ],
      "metadata": {
        "id": "EbGc0_Ow35kE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from torch.utils.data import DataLoader\n",
        "\n",
        "BATCH_SIZE = 32\n",
        "\n",
        "# Turn datasets to iterables\n",
        "train_dataloader = DataLoader(dataset = train_data,\n",
        "                              batch_size = BATCH_SIZE,\n",
        "                              shuffle = True)\n",
        "\n",
        "test_dataloader = DataLoader(dataset = test_data,\n",
        "                             batch_size = 32,\n",
        "                             shuffle = False)\n",
        "\n",
        "train_dataloader, test_dataloader"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EinShc25343z",
        "outputId": "86ce8b0e-3882-457b-b58f-12cd97287fcd"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(<torch.utils.data.dataloader.DataLoader at 0x7c46353e9720>,\n",
              " <torch.utils.data.dataloader.DataLoader at 0x7c4635712290>)"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(train_dataloader),len(test_dataloader)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LUwkpeOVx5r8",
        "outputId": "451df5bb-b2b1-4287-a021-76320cc4d702"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1875, 313)"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_features_batch, train_labels_batch = next(iter(train_dataloader))\n",
        "train_features_batch.shape, train_labels_batch.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NTfVI_yE6cks",
        "outputId": "33ded170-4303-4dde-d137-0ef55293f287"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(torch.Size([32, 1, 28, 28]), torch.Size([32]))"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Model 0: Build a basline model\n",
        "When starting to build a series of maachine learning modelling experiments, its best practice to start with a baseline model.\n",
        "\n",
        "A baseline model is a simple model you will try and improvw upon with subsequent models/experiments.\n",
        "\n",
        "In other words: start simply and add complexity when necessary"
      ],
      "metadata": {
        "id": "j5AtzZdmYgPw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a flatten layer\n",
        "flatten_model = nn.Flatten()\n",
        "\n",
        "x = train_features_batch[0]\n",
        "\n",
        "output = flatten_model(x)\n",
        "\n",
        "x.shape, output.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ccM190qA7WRU",
        "outputId": "ad5aa599-91a3-4d3e-fc83-ea7c04108938"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(torch.Size([1, 28, 28]), torch.Size([1, 784]))"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from torch import nn\n",
        "class FashionMNISTmodelVo(nn.Module):\n",
        "  def __init__(self,\n",
        "               input_features: int,\n",
        "               hidden_units: int,\n",
        "               output_features: int):\n",
        "    super().__init__()\n",
        "    self.layer_stack = nn.Sequential(\n",
        "        nn.Flatten(),\n",
        "        nn.Linear(in_features = input_features, out_features = hidden_units),\n",
        "        nn.Linear(in_features = hidden_units, out_features = output_features)\n",
        "    )\n",
        "  def forward(self,x):\n",
        "    return self.layer_stack(x)"
      ],
      "metadata": {
        "id": "lPlei5DTaJWX"
      },
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_0 = FashionMNISTmodelVo(input_features = 784, # 28 * 28\n",
        "                              output_features = len(class_names),\n",
        "                              hidden_units = 10).to(\"cpu\")\n",
        "model_0"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "31f18ovTcNt0",
        "outputId": "685ddb8b-e74f-4c2d-b793-c0d315922b0e"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "FashionMNISTmodelVo(\n",
              "  (layer_stack): Sequential(\n",
              "    (0): Flatten(start_dim=1, end_dim=-1)\n",
              "    (1): Linear(in_features=784, out_features=10, bias=True)\n",
              "    (2): Linear(in_features=10, out_features=10, bias=True)\n",
              "  )\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dummy_x = torch.rand([1,1,28,28])\n",
        "model_0(dummy_x).shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qsHawklIcqMY",
        "outputId": "2796d627-fc39-4f39-c1df-045968445759"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([1, 10])"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###  3.1 Setup a loss function and an optimiser\n"
      ],
      "metadata": {
        "id": "5sapYamjeKfs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "loss_fn = nn.CrossEntropyLoss()\n",
        "optimizer = torch.optim.SGD(params = model_0.parameters(),\n",
        "                            lr = 0.1)"
      ],
      "metadata": {
        "id": "MOayh6AMeJ5p"
      },
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from timeit import default_timer as timer"
      ],
      "metadata": {
        "id": "_QgCwEDReJ3X"
      },
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "start_time = timer()\n",
        "# ..code\n",
        "end_time = timer()\n",
        "print(f\"time taken: {end_time-start_time}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PVhXCKRQc3Ay",
        "outputId": "b1ad44f9-44ce-4c75-a55f-c8c6347de924"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "time taken: 3.881800000726798e-05\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Highlight that the optimizer will update a model's parameters once per batch rather than once per epoch"
      ],
      "metadata": {
        "id": "nG6YurcC21j7"
      },
      "execution_count": 59,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def accuracy_fn(y_pred,y_true):\n",
        "  correct = torch.eq(y_true, y_pred).sum().item()\n",
        "  acc = (correct/len(y_pred)) * 100\n",
        "  return acc"
      ],
      "metadata": {
        "id": "e8Cn3yVy7irJ"
      },
      "execution_count": 60,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Creating a training loop and training a model on batches of data\n",
        "\n"
      ],
      "metadata": {
        "id": "6dnFwcDR0K3P"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from tqdm.auto import tqdm"
      ],
      "metadata": {
        "id": "1rDvoNe4laM8"
      },
      "execution_count": 61,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "str(next(model_0.parameters()).device)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "FNi1Fw4-Hq7h",
        "outputId": "038a0d5b-fa8f-4a93-d167-c9ea15b15c12"
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'cpu'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Import tqdm for progress bar\n",
        "from tqdm.auto import tqdm\n",
        "\n",
        "# Set the seed and start the timer\n",
        "torch.manual_seed(42)\n",
        "start_time = timer()\n",
        "\n",
        "epochs = 3\n",
        "\n",
        "for epoch in tqdm(range(epochs)):\n",
        "  print(f\"Epoch: {epoch}\\n-------\")\n",
        "  ### Training\n",
        "  train_loss = 0\n",
        "  # Add a loop to loop through the training batches\n",
        "  for batch, (X,y) in enumerate(train_dataloader):\n",
        "    model_0.train()\n",
        "\n",
        "    # 1. Forward pass\n",
        "    y_pred = model_0(X)\n",
        "\n",
        "    # 2. Calculate the loss\n",
        "\n",
        "    loss = loss_fn(y_pred,y)\n",
        "    train_loss += loss\n",
        "\n",
        "    # 3. Optimize zero grad\n",
        "    optimizer.zero_grad()\n",
        "\n",
        "    # 4. Loss backward\n",
        "    loss.backward()\n",
        "\n",
        "    #5. Optimizer step\n",
        "    optimizer.step()\n",
        "\n",
        "    # Print out what's happening\n",
        "    if batch % 400 == 0 :\n",
        "      print(f\"Lokked at {batch * len(X)}/ {len(train_dataloader.dataset)} samples\")\n",
        "\n",
        "  train_loss /= len(train_dataloader)\n",
        "\n",
        "  ## Testing\n",
        "  test_loss, test_acc = 0, 0\n",
        "  model_0.eval()\n",
        "  with torch.inference_mode():\n",
        "    for X_test,y_test in test_dataloader:\n",
        "\n",
        "      test_pred = model_0(X_test)\n",
        "      test_loss += loss_fn(test_pred, y_test)\n",
        "      test_acc += accuracy_fn(test_pred.argmax(dim = 1), y_test)\n",
        "\n",
        "    test_loss /= len(test_dataloader)\n",
        "    test_acc /= len(test_dataloader)\n",
        "\n",
        "  print(f\"\\nTrainloss: {train_loss:.4f} | Test loss: {test_loss:.4f}, Test acc: {test_acc:.2f}\")\n",
        "\n",
        "end_time = timer()\n",
        "model_0_time = end_time-start_time\n",
        "print(f\"time taken: {end_time-start_time}\")"
      ],
      "metadata": {
        "id": "IR-kz7goznKP",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 549,
          "referenced_widgets": [
            "fdd7e2c765eb45db9e22324b000f813c",
            "903b713e56664b699d6168f3e39b8ebd",
            "bdbbac8e527642b8b7c28c0910d4c930",
            "8225548172334973be4c6a04d2cf5a92",
            "a09c61dbeb914a6fa6eb4ad9cbdac74c",
            "2c307c1feac44c8aaf61705001d2237c",
            "bffb8452aa234c90960f8cce1250e362",
            "9b3c8b18313a44a7b11020bb402a0b1a",
            "155e8aade9684147b2c2ae603342f54b",
            "5224c2403c954161aa4dad66a5b447d8",
            "c67972c5e9a14de88a1d82ad1d3e037b"
          ]
        },
        "outputId": "e367a9da-ff6a-4c84-e09a-55e36771704d"
      },
      "execution_count": 63,
      "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": "fdd7e2c765eb45db9e22324b000f813c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 0\n",
            "-------\n",
            "Lokked at 0/ 60000 samples\n",
            "Lokked at 12800/ 60000 samples\n",
            "Lokked at 25600/ 60000 samples\n",
            "Lokked at 38400/ 60000 samples\n",
            "Lokked at 51200/ 60000 samples\n",
            "\n",
            "Trainloss: 0.5896 | Test loss: 0.5054, Test acc: 82.33\n",
            "Epoch: 1\n",
            "-------\n",
            "Lokked at 0/ 60000 samples\n",
            "Lokked at 12800/ 60000 samples\n",
            "Lokked at 25600/ 60000 samples\n",
            "Lokked at 38400/ 60000 samples\n",
            "Lokked at 51200/ 60000 samples\n",
            "\n",
            "Trainloss: 0.4757 | Test loss: 0.4842, Test acc: 82.89\n",
            "Epoch: 2\n",
            "-------\n",
            "Lokked at 0/ 60000 samples\n",
            "Lokked at 12800/ 60000 samples\n",
            "Lokked at 25600/ 60000 samples\n",
            "Lokked at 38400/ 60000 samples\n",
            "Lokked at 51200/ 60000 samples\n",
            "\n",
            "Trainloss: 0.4545 | Test loss: 0.4782, Test acc: 83.34\n",
            "time taken: 26.990753192\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 4. Making predictions and getting model_0 results"
      ],
      "metadata": {
        "id": "bbYyMsgAKDD9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "torch.manual_seed(42)\n",
        "def eval_model(model: torch.nn.Module,\n",
        "               data_loader: torch.utils.data.DataLoader,\n",
        "               loss_fn: torch.nn.Module,\n",
        "               accuracy_fn,\n",
        "               device = \"cpu\"):\n",
        "  \"\"\" Returns a dictionary containing the results of model predicting on data_loader. \"\"\"\n",
        "  loss, acc = 0, 0\n",
        "  model.eval()\n",
        "  with torch.inference_mode():\n",
        "    for X,y in data_loader:\n",
        "      X,y = X.to(device), y.to(device)\n",
        "      y_pred = model(X)\n",
        "      loss += loss_fn(y_pred,y)\n",
        "      acc += accuracy_fn(y_pred.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}"
      ],
      "metadata": {
        "id": "mZhAysV3IcEB"
      },
      "execution_count": 64,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_0_results = eval_model(model_0,\n",
        "                             data_loader = test_dataloader,\n",
        "                             loss_fn = loss_fn,\n",
        "                              accuracy_fn =accuracy_fn)\n",
        "model_0_results\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YrpCGV8aMXDe",
        "outputId": "8cb6bc12-bad9-4873-e093-63afe4a462ef"
      },
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'model_name': 'FashionMNISTmodelVo',\n",
              " 'model_loss': 0.4782179594039917,\n",
              " 'model_acc': 83.33666134185303}"
            ]
          },
          "metadata": {},
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Model 1: Build a better model with non-linearity"
      ],
      "metadata": {
        "id": "qyGpQuHN4nEc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Setup device agnostic code\n",
        "import torch\n",
        "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
        "device"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "ENqopsOA49yV",
        "outputId": "d6ab1a8f-af0b-45f0-f6ff-007ce8a95ca9"
      },
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'cuda'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class FashionMNISTModelV1(nn.Module):\n",
        "  def __init__(self,\n",
        "               input_shape: int,\n",
        "               hidden_units: int,\n",
        "               output_shape: int):\n",
        "    super().__init__()\n",
        "    self.layer_stack = nn.Sequential(\n",
        "        nn.Flatten(), # flatten inputs into a single vector\n",
        "        nn.Linear(in_features=input_shape,\n",
        "                  out_features=hidden_units),\n",
        "        nn.ReLU(),\n",
        "        nn.Linear(in_features=hidden_units,\n",
        "                  out_features=output_shape),\n",
        "        nn.ReLU()\n",
        "    )\n",
        "\n",
        "  def forward(self, x: torch.Tensor):\n",
        "    return self.layer_stack(x)"
      ],
      "metadata": {
        "id": "Od8NysqL5WPc"
      },
      "execution_count": 67,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "torch.manual_seed(42)\n",
        "model_1 = FashionMNISTModelV1(input_shape = 784,\n",
        "                              hidden_units = 10,\n",
        "                              output_shape = len(class_names)).to(device)\n",
        "next(model_1.parameters()).device\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yNJCiMyH67Bx",
        "outputId": "b79ab86d-fae0-4495-952c-5d5de515422e"
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "device(type='cuda', index=0)"
            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "loss_fn = nn.CrossEntropyLoss()\n",
        "optimizer = torch.optim.SGD(params = model_1.parameters(),\n",
        "                            lr = 0.1)"
      ],
      "metadata": {
        "id": "wDumjxyw7fam"
      },
      "execution_count": 69,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Functionising the training and testing loop"
      ],
      "metadata": {
        "id": "7fOhNRmo9Txk"
      }
    },
    {
      "cell_type": "code",
      "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, train_acc = 0, 0\n",
        "\n",
        "  model.train()\n",
        "\n",
        "  # Add a loop to loop through the training batches\n",
        "  for batch, (X,y) in enumerate(data_loader):\n",
        "\n",
        "    X, y = X.to(device), y.to(device)\n",
        "    # 1. Forward pass\n",
        "    y_pred = model(X)\n",
        "\n",
        "    # 2. Calculate the loss\n",
        "\n",
        "    loss = loss_fn(y_pred,y)\n",
        "    train_loss += loss\n",
        "    train_acc += accuracy_fn(y_pred.argmax(dim = 1), y)\n",
        "\n",
        "    # 3. Optimize zero grad\n",
        "    optimizer.zero_grad()\n",
        "\n",
        "    # 4. Loss backward\n",
        "    loss.backward()\n",
        "\n",
        "    #5. Optimizer step\n",
        "    optimizer.step()\n",
        "\n",
        "\n",
        "\n",
        "  train_loss /= len(data_loader)\n",
        "  train_acc /= len(data_loader)\n",
        "\n",
        "  print(f\"Train Loss: {train_loss:.5f} | Train accuracy: {train_acc:.2f}\\n\")\n"
      ],
      "metadata": {
        "id": "e5zOTj3I89jq"
      },
      "execution_count": 70,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "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",
        "  test_loss, test_acc = 0, 0\n",
        "  model.eval()\n",
        "  with torch.inference_mode():\n",
        "    for X_test,y_test in data_loader:\n",
        "      X_test, y_test = X_test.to(device), y_test.to(device)\n",
        "\n",
        "      test_pred = model(X_test)\n",
        "      test_loss += loss_fn(test_pred, y_test)\n",
        "\n",
        "      test_acc += accuracy_fn(test_pred.argmax(dim = 1), y_test)\n",
        "\n",
        "    test_loss /= len(test_dataloader)\n",
        "    test_acc /= len(test_dataloader)\n",
        "\n",
        "    print(f\" Test loss: {test_loss:.4f}| Test acc: {test_acc:.2f} \\n\")"
      ],
      "metadata": {
        "id": "PqzdeU1VBL4j"
      },
      "execution_count": 71,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "torch.manual_seed(42)\n",
        "epochs = 3\n",
        "\n",
        "start_time = timer()\n",
        "# for epoch in tqdm(range(epochs)):\n",
        "#   print(f\"Epoch: {epoch} \\n ------\")\n",
        "#   train_step(model_1,train_dataloader,loss_fn, optimizer,\n",
        "#              accuracy_fn,device)\n",
        "\n",
        "#   test_step(model_1,test_dataloader,loss_fn,\n",
        "#             accuracy_fn, device )\n",
        "for epoch in tqdm(range(epochs)):\n",
        "  print(f\"Epoch: {epoch}\\n----------\")\n",
        "  train_step(model=model_1,\n",
        "             data_loader=train_dataloader,\n",
        "             loss_fn=loss_fn,\n",
        "             optimizer=optimizer,\n",
        "             accuracy_fn=accuracy_fn,\n",
        "             device=device)\n",
        "  test_step(model=model_1,\n",
        "            data_loader=test_dataloader,\n",
        "            loss_fn=loss_fn,\n",
        "            accuracy_fn=accuracy_fn,\n",
        "            device=device)\n",
        "\n",
        "\n",
        "end_time = timer()\n",
        "model_1_time = end_time - start_time\n",
        "print(f\"time taken: {end_time - start_time}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 388,
          "referenced_widgets": [
            "f6791d81d0cc463f8e53f5b07cf8e246",
            "6e99cd7d4ced40d18da8a721046ce0c6",
            "c0b0cfe4aa074bbfa054bf4bb1ce5ff9",
            "51b7c361f1da452fa0ed007132fcb439",
            "9d8cf201b9e147c7aa2eaa16523d69e9",
            "4520f75d1aa648f5aab2a556cb98b411",
            "17dbc724fe6740b3abe416227c6e35c6",
            "fa2e3b3436ee43199c996223aae95487",
            "00f110d89ae346c38e45015f87f240f7",
            "f003ec347fb94208be88b4d2a3830db5",
            "b9e4c658e953436fa5811f8f92d40d92"
          ]
        },
        "id": "qVzsOPT8CCVZ",
        "outputId": "de9057ef-c127-42f6-8e57-ca059f948b6e"
      },
      "execution_count": 72,
      "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": "f6791d81d0cc463f8e53f5b07cf8e246"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 0\n",
            "----------\n",
            "Train Loss: 1.09199 | Train accuracy: 61.34\n",
            "\n",
            " Test loss: 0.9564| Test acc: 65.00 \n",
            "\n",
            "Epoch: 1\n",
            "----------\n",
            "Train Loss: 0.78101 | Train accuracy: 71.93\n",
            "\n",
            " Test loss: 0.7223| Test acc: 73.91 \n",
            "\n",
            "Epoch: 2\n",
            "----------\n",
            "Train Loss: 0.67027 | Train accuracy: 75.94\n",
            "\n",
            " Test loss: 0.6850| Test acc: 75.02 \n",
            "\n",
            "time taken: 30.023441552999998\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "device"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "c_7u1-IUH1-b",
        "outputId": "a85cf9db-2b24-43a0-8a2c-c5b8eaab9708"
      },
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'cuda'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_1_results = eval_model(model_1,\n",
        "                             test_dataloader,\n",
        "                             loss_fn,\n",
        "                             accuracy_fn,\n",
        "                             device = \"cuda\")\n",
        "model_1_results"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FZ2TktYeIDwx",
        "outputId": "62855039-d8e7-48b6-9ae0-a8c04c007cd6"
      },
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'model_name': 'FashionMNISTModelV1',\n",
              " 'model_loss': 0.6850008964538574,\n",
              " 'model_acc': 75.01996805111821}"
            ]
          },
          "metadata": {},
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Model 2: Building a Convolutional Neural Network(CNN)"
      ],
      "metadata": {
        "id": "uuABCqpoD16q"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def accuracy_fn(y_pred,y_true):\n",
        "  correct = torch.eq(y_true, y_pred).sum().item()\n",
        "  acc = (correct/len(y_pred)) * 100\n",
        "  return acc"
      ],
      "metadata": {
        "id": "K_J3Hs8zr9oM"
      },
      "execution_count": 75,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a convolutional neural network (tiny VVG architecture)\n",
        "class FashionMNISTModelV2(nn.Module):\n",
        "  \"\"\"\n",
        "  Model architecture that replicates the TinyVGG\n",
        "  model from CNN explainer website\n",
        "  \"\"\"\n",
        "  def __init__(self, input_shape, hidden_units, output_shape):\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(x.shape)\n",
        "    x = self.conv_block_2(x)\n",
        "    # print(x.shape)\n",
        "    x = self.classifier(x)\n",
        "    return x\n",
        "\n"
      ],
      "metadata": {
        "id": "aBsYPSjwQf_j"
      },
      "execution_count": 76,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "image.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z6Gheas-CJQf",
        "outputId": "3e1f824a-3eee-4476-c270-d6351c4a9d6e"
      },
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([1, 28, 28])"
            ]
          },
          "metadata": {},
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "torch.manual_seed(42)\n",
        "model_2 = FashionMNISTModelV2(input_shape =1 , # denotes number of color channels\n",
        "                              hidden_units = 10,\n",
        "                              output_shape = len(class_names)).to(device)"
      ],
      "metadata": {
        "id": "K9pNn8EGCKn7"
      },
      "execution_count": 78,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Setting up loss function and optimizer for model_2\n",
        "loss_fn = nn.CrossEntropyLoss()\n",
        "optimizer = torch.optim.SGD(params = model_2.parameters(),\n",
        "                            lr = 0.1)"
      ],
      "metadata": {
        "id": "EdOtxCMHE98e"
      },
      "execution_count": 79,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from timeit import default_timer as timer\n",
        "from tqdm.auto import tqdm"
      ],
      "metadata": {
        "id": "BIKQVonfl2rb"
      },
      "execution_count": 80,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Training and testing loop\n",
        "epochs = 3\n",
        "\n",
        "start_time = timer()\n",
        "for epoch in tqdm(range(epochs)):\n",
        "  print(f\"Epoch: {epoch}\\n ------\")\n",
        "  train_step(model = model_2,\n",
        "            data_loader = train_dataloader,\n",
        "            loss_fn = loss_fn,\n",
        "            optimizer= optimizer,\n",
        "            accuracy_fn = accuracy_fn,\n",
        "            device = device)\n",
        "  test_step(model = model_2,\n",
        "            data_loader = test_dataloader,\n",
        "            loss_fn = loss_fn,\n",
        "            accuracy_fn = accuracy_fn,\n",
        "            device = device)\n",
        "\n",
        "end_time = timer()\n",
        "model_2_time = end_time -start_time\n",
        "print(f\"\\nTrain time for model_2: {end_time-start_time}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406,
          "referenced_widgets": [
            "2252a335d9c04d3fba153dae5f5e92ed",
            "44bad5b7b49240499eb9fefbe9d5ff95",
            "5d4081a379634d2b9761a107248c589e",
            "d5fcb426d04743dba69f32f4c36e7cef",
            "e367f470b08a41e8ad2793d0c6b15dfe",
            "204ef052cd1949fc89105314ed1cdd4e",
            "f9bd152f4ced4367b507352d6a6870ab",
            "b5d155917a574aac9f6c7505561d3357",
            "175ed85485034a13afbff18de8321e6f",
            "220eab841d3243bd938e9bf65e56fc49",
            "c893b5f9c730405cb635b012d8c0e269"
          ]
        },
        "id": "ZILxOdVPlrGd",
        "outputId": "7941ead0-95a6-415a-eb57-a4e71889600b"
      },
      "execution_count": 82,
      "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": "2252a335d9c04d3fba153dae5f5e92ed"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 0\n",
            " ------\n",
            "Train Loss: 0.42109 | Train accuracy: 84.76\n",
            "\n",
            " Test loss: 0.3803| Test acc: 85.90 \n",
            "\n",
            "Epoch: 1\n",
            " ------\n",
            "Train Loss: 0.33707 | Train accuracy: 87.99\n",
            "\n",
            " Test loss: 0.3258| Test acc: 88.51 \n",
            "\n",
            "Epoch: 2\n",
            " ------\n",
            "Train Loss: 0.30674 | Train accuracy: 88.93\n",
            "\n",
            " Test loss: 0.3165| Test acc: 88.65 \n",
            "\n",
            "\n",
            "Train time for model_2: 37.18970727200002\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!nvidia-smi"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Wiv2VlWHs9f6",
        "outputId": "cf53115b-1729-4881-8a45-6b064987f7da"
      },
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Thu Dec 21 17:21:41 2023       \n",
            "+---------------------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 535.104.05             Driver Version: 535.104.05   CUDA Version: 12.2     |\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  Tesla T4                       Off | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   59C    P0              28W /  70W |    203MiB / 15360MiB |     10%      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",
            "+---------------------------------------------------------------------------------------+\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Getting model_2 results\n",
        "model_2_results = eval_model(model_2,\n",
        "                             test_dataloader,\n",
        "                             loss_fn,\n",
        "                             accuracy_fn,\n",
        "                             device)\n",
        "model_2_results"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RB2CF_bCtAsN",
        "outputId": "6ce914af-ab50-4946-fc1a-0a05652a509d"
      },
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'model_name': 'FashionMNISTModelV2',\n",
              " 'model_loss': 0.316477507352829,\n",
              " 'model_acc': 88.64816293929712}"
            ]
          },
          "metadata": {},
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "compare_results = pd.DataFrame([model_0_results,\n",
        "                                model_1_results,\n",
        "                                model_2_results])\n",
        "compare_results"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "k8IqWfCRtXbF",
        "outputId": "6611cd09-e0c3-492e-d93b-9671a44373be"
      },
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            model_name  model_loss  model_acc\n",
              "0  FashionMNISTmodelVo    0.478218  83.336661\n",
              "1  FashionMNISTModelV1    0.685001  75.019968\n",
              "2  FashionMNISTModelV2    0.316478  88.648163"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-12c83101-a170-45eb-9cfc-af8dbef1051e\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>model_name</th>\n",
              "      <th>model_loss</th>\n",
              "      <th>model_acc</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>FashionMNISTmodelVo</td>\n",
              "      <td>0.478218</td>\n",
              "      <td>83.336661</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>FashionMNISTModelV1</td>\n",
              "      <td>0.685001</td>\n",
              "      <td>75.019968</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>FashionMNISTModelV2</td>\n",
              "      <td>0.316478</td>\n",
              "      <td>88.648163</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-12c83101-a170-45eb-9cfc-af8dbef1051e')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-12c83101-a170-45eb-9cfc-af8dbef1051e button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-12c83101-a170-45eb-9cfc-af8dbef1051e');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\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",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-1a6e3450-a0b8-489c-aeb2-a1c33bdbacf9\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-1a6e3450-a0b8-489c-aeb2-a1c33bdbacf9')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-1a6e3450-a0b8-489c-aeb2-a1c33bdbacf9 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_cf88b0e9-8323-484f-88e2-ea1ddf73ff75\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('compare_results')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_cf88b0e9-8323-484f-88e2-ea1ddf73ff75 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('compare_results');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 85
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "compare_results[\"Training_time\"] = [model_0_time,\n",
        "                                    model_1_time,\n",
        "                                    model_2_time]\n",
        "\n",
        "compare_results"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "KzVDpcN9ttQ_",
        "outputId": "99134dcd-93ee-4c0a-889e-f8aabc117156"
      },
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            model_name  model_loss  model_acc  Training_time\n",
              "0  FashionMNISTmodelVo    0.478218  83.336661      26.990753\n",
              "1  FashionMNISTModelV1    0.685001  75.019968      30.023442\n",
              "2  FashionMNISTModelV2    0.316478  88.648163      37.189707"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-40605932-a873-4878-b07d-5b4b07af0efe\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "    .dataframe thead th {\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>model_name</th>\n",
              "      <th>model_loss</th>\n",
              "      <th>model_acc</th>\n",
              "      <th>Training_time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>FashionMNISTmodelVo</td>\n",
              "      <td>0.478218</td>\n",
              "      <td>83.336661</td>\n",
              "      <td>26.990753</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>FashionMNISTModelV1</td>\n",
              "      <td>0.685001</td>\n",
              "      <td>75.019968</td>\n",
              "      <td>30.023442</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>FashionMNISTModelV2</td>\n",
              "      <td>0.316478</td>\n",
              "      <td>88.648163</td>\n",
              "      <td>37.189707</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-40605932-a873-4878-b07d-5b4b07af0efe')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-40605932-a873-4878-b07d-5b4b07af0efe button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-40605932-a873-4878-b07d-5b4b07af0efe');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\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",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-9fff77a8-e045-45f7-af08-f6357dfba0f9\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-9fff77a8-e045-45f7-af08-f6357dfba0f9')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-9fff77a8-e045-45f7-af08-f6357dfba0f9 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_206d1fd9-2f17-4582-92c2-10b5db32976a\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('compare_results')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_206d1fd9-2f17-4582-92c2-10b5db32976a button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('compare_results');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 86
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Make and evaluate predictions using our best model"
      ],
      "metadata": {
        "id": "SAvIwE-ROg2i"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def make_predictions(model : torch.nn.Module,\n",
        "                     data: list,\n",
        "                     device: torch.device = device):\n",
        "  pred_labels = []\n",
        "  model.eval()\n",
        "  with torch.inference_mode():\n",
        "    for sample in data :\n",
        "      # add a batch dimension and pass to device\n",
        "      sample = sample.unsqueeze(dim = 0 ).to(device)\n",
        "\n",
        "      y_logit = model(sample)\n",
        "\n",
        "      y_pred = y_logit.squeeze().softmax(dim = 0)\n",
        "\n",
        "      pred_labels.append(y_pred.argmax(dim = 0).item())\n",
        "\n",
        "    return pred_labels\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "5LpyzBTpOu9K"
      },
      "execution_count": 87,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "A = [torch.tensor([1,2,3]),torch.tensor([4,5,6]),torch.tensor([7,8,9])]\n",
        "B = torch.stack(A) # Stacking list as tensor\n",
        "B"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7sfAZ3pmQetO",
        "outputId": "df278c81-1740-45fa-e47e-1b0498b95a49"
      },
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[1, 2, 3],\n",
              "        [4, 5, 6],\n",
              "        [7, 8, 9]])"
            ]
          },
          "metadata": {},
          "execution_count": 88
        }
      ]
    },
    {
      "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",
        "test_samples[0].shape\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "diwNsYqAQo0S",
        "outputId": "8ac20d83-3f32-4d32-e5ad-6bcde54d04ad"
      },
      "execution_count": 89,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([1, 28, 28])"
            ]
          },
          "metadata": {},
          "execution_count": 89
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.imshow(test_samples[0].squeeze(dim=0), cmap=\"gray\")\n",
        "plt.title(class_names[test_labels[0]])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 470
        },
        "id": "3yrn4CIoSTMn",
        "outputId": "1cb88551-b8c1-402f-f643-fb0340d19097"
      },
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Dress')"
            ]
          },
          "metadata": {},
          "execution_count": 90
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_samples[0].shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tuNZUk85U8H5",
        "outputId": "ba299741-88f8-4736-9616-930a0fd8aeb7"
      },
      "execution_count": 91,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([1, 28, 28])"
            ]
          },
          "metadata": {},
          "execution_count": 91
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Make predictions\n",
        "pred_probs = make_predictions(model = model_2,\n",
        "                              data = test_samples)\n",
        "pred_probs"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1kyR3hNKSnMJ",
        "outputId": "232f9f30-e7c4-4409-aaeb-c75928c8d3b5"
      },
      "execution_count": 92,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[3, 7, 1, 1, 4, 4, 7, 2, 2]"
            ]
          },
          "metadata": {},
          "execution_count": 92
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_labels"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ywD4pcpGUCER",
        "outputId": "029716b5-ff0e-47c2-9e7b-da04d1bcb116"
      },
      "execution_count": 93,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[3, 9, 1, 1, 4, 4, 7, 6, 2]"
            ]
          },
          "metadata": {},
          "execution_count": 93
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Making Confusion matrix for further model evaluation\n",
        "1. Making predictions on all of the test data using our model\n",
        "2. Finding out the confusion matrix using 'torchmetrics.ConfusionMatrix'\n",
        "3. Plot the confusion matrix using mlxtend.plotting.plot_confusion_matrix()"
      ],
      "metadata": {
        "id": "OjUDwz3hziwp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "len(test_data.targets.tolist())\n"
      ],
      "metadata": {
        "id": "Ea_oSWzJWGMS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "79ebcffb-ae22-4cc7-d9ed-1b4aa157cbc5"
      },
      "execution_count": 94,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "10000"
            ]
          },
          "metadata": {},
          "execution_count": 94
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_samples = []\n",
        "for sample, label in test_data:\n",
        "  test_samples.append(sample)\n"
      ],
      "metadata": {
        "id": "afCiKtrz10ak"
      },
      "execution_count": 95,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_samples[0].shape, len(test_samples)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BMpUrxHM3MIm",
        "outputId": "1e4efbde-e4f3-452c-fc94-8716a4afc35a"
      },
      "execution_count": 96,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(torch.Size([1, 28, 28]), 10000)"
            ]
          },
          "metadata": {},
          "execution_count": 96
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = make_predictions(model=model_2,\n",
        "                          data = test_samples)\n",
        "y_pred = torch.tensor(y_pred)\n",
        "y_pred"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tk9jiOle0lqZ",
        "outputId": "1913fca1-a431-408e-d068-b38525cc2971"
      },
      "execution_count": 97,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([9, 2, 1,  ..., 8, 1, 8])"
            ]
          },
          "metadata": {},
          "execution_count": 97
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0rDNxS2x1abo",
        "outputId": "fd3f8af6-b6c5-4c2c-d8e7-6d7435395f90"
      },
      "execution_count": 98,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([10000])"
            ]
          },
          "metadata": {},
          "execution_count": 98
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_data.targets.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pds2l2I03i7E",
        "outputId": "048c4bbe-9e75-44ac-93ab-910cdd353bfe"
      },
      "execution_count": 99,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([10000])"
            ]
          },
          "metadata": {},
          "execution_count": 99
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "! pip install torchmetrics"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u5c8gWdX3vDP",
        "outputId": "27798599-a8c3-48a3-99d4-81a7702afdfc"
      },
      "execution_count": 100,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting torchmetrics\n",
            "  Downloading torchmetrics-1.2.1-py3-none-any.whl (806 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m806.1/806.1 kB\u001b[0m \u001b[31m5.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: numpy>1.20.0 in /usr/local/lib/python3.10/dist-packages (from torchmetrics) (1.23.5)\n",
            "Requirement already satisfied: packaging>17.1 in /usr/local/lib/python3.10/dist-packages (from torchmetrics) (23.2)\n",
            "Requirement already satisfied: torch>=1.8.1 in /usr/local/lib/python3.10/dist-packages (from torchmetrics) (2.1.0+cu121)\n",
            "Collecting lightning-utilities>=0.8.0 (from torchmetrics)\n",
            "  Downloading lightning_utilities-0.10.0-py3-none-any.whl (24 kB)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.10/dist-packages (from lightning-utilities>=0.8.0->torchmetrics) (67.7.2)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.10/dist-packages (from lightning-utilities>=0.8.0->torchmetrics) (4.5.0)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch>=1.8.1->torchmetrics) (3.13.1)\n",
            "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch>=1.8.1->torchmetrics) (1.12)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch>=1.8.1->torchmetrics) (3.2.1)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch>=1.8.1->torchmetrics) (3.1.2)\n",
            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from torch>=1.8.1->torchmetrics) (2023.6.0)\n",
            "Requirement already satisfied: triton==2.1.0 in /usr/local/lib/python3.10/dist-packages (from torch>=1.8.1->torchmetrics) (2.1.0)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch>=1.8.1->torchmetrics) (2.1.3)\n",
            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.8.1->torchmetrics) (1.3.0)\n",
            "Installing collected packages: lightning-utilities, torchmetrics\n",
            "Successfully installed lightning-utilities-0.10.0 torchmetrics-1.2.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import mlxtend\n",
        "mlxtend.__version__"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "OcZcJt3J3yW3",
        "outputId": "9611277a-7e48-4195-924d-a889301e4929"
      },
      "execution_count": 101,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'0.22.0'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 101
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from torchmetrics import ConfusionMatrix\n",
        "from mlxtend.plotting import plot_confusion_matrix\n",
        "\n",
        "confmat = ConfusionMatrix(task = \"multiclass\",num_classes = len(class_names))\n",
        "confmat_tensor = confmat(preds = y_pred,\n",
        "                         target = test_data.targets)\n",
        "\n",
        "# 3. Plot the confusion matrix\n",
        "fig, ax = plot_confusion_matrix(\n",
        "    conf_mat=confmat_tensor.numpy(), # matplotlib likes working with numpy\n",
        "    class_names=class_names,\n",
        "    figsize=(10, 7)\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 667
        },
        "id": "CGSB4j564AfN",
        "outputId": "77647e14-b924-4579-dcec-dd6e7fcfe7aa"
      },
      "execution_count": 102,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x700 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Saving and loading our best model (model_2)"
      ],
      "metadata": {
        "id": "nPymKLJwauU7"
      }
    },
    {
      "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 = \"CNN_MODEL_2.pth\"\n",
        "MODEL_SAVE_PATH = MODEL_PATH / MODEL_NAME\n",
        "\n",
        "print(f\"Saving model to: {MODEL_SAVE_PATH}\")\n",
        "torch.save(obj = model_2.state_dict(),\n",
        "           f = MODEL_SAVE_PATH)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Et7TA6aBa-dZ",
        "outputId": "d857230f-1055-4689-8a81-26814665ead6"
      },
      "execution_count": 103,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving model to: models/CNN_MODEL_2.pth\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Loading the model"
      ],
      "metadata": {
        "id": "e4pCxq7dcrCX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "loaded_model = FashionMNISTModelV2(input_shape = 1,\n",
        "                                   hidden_units = 10,\n",
        "                                   output_shape = 10)\n",
        "loaded_model.load_state_dict(torch.load(f=MODEL_SAVE_PATH))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_SowmVYActw4",
        "outputId": "676a34b9-bfb0-4bdd-db68-cb96943b48fd"
      },
      "execution_count": 104,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<All keys matched successfully>"
            ]
          },
          "metadata": {},
          "execution_count": 104
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "loaded_model.to(device)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XNk7iq4udUuU",
        "outputId": "f9a1ad57-fd3e-489e-c935-130759152cc2"
      },
      "execution_count": 106,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "FashionMNISTModelV2(\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",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 106
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_2_results"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SQPHhzpPdZgL",
        "outputId": "c01f7997-fa6f-42b4-cecd-37a805cb4b00"
      },
      "execution_count": 107,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'model_name': 'FashionMNISTModelV2',\n",
              " 'model_loss': 0.316477507352829,\n",
              " 'model_acc': 88.64816293929712}"
            ]
          },
          "metadata": {},
          "execution_count": 107
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluating the model\n",
        "loaded_model_results = eval_model(model = loaded_model,\n",
        "                                  data_loader = test_dataloader,\n",
        "                                  loss_fn = loss_fn,\n",
        "                                  accuracy_fn = accuracy_fn,\n",
        "                                  device = device)\n",
        "loaded_model_results"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AhCPViUYd-1g",
        "outputId": "ffc436cb-4a90-4c70-e82f-a26083625454"
      },
      "execution_count": 108,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'model_name': 'FashionMNISTModelV2',\n",
              " 'model_loss': 0.316477507352829,\n",
              " 'model_acc': 88.64816293929712}"
            ]
          },
          "metadata": {},
          "execution_count": 108
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "LLSoGlhfefvW"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}